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Issues with MIG after update to 23.11
by Cumer Cristiano 10 May '24

10 May '24

Dear list, after an update form 22.05 to 23.11 a host where I'm using MIG started to discard the mig devices during the NVML setuop. I can see those lines in the log: slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) where the MIG devices are discarded during the start of the slurmd deamon. this is my gres.conf file cat /etc/slurm/gres.conf ################################################################## # Slurm's Generic Resource (GRES) configuration file ################################################################## AutoDetect=nvml and that still works for hosts on 22.05 Any idea of what is going on? I tried to disable MIG on the upgraded server, and then all the A100 are recognized without issues. Thanks Cristiano slurmd: debug: Log file re-opened slurmd: debug2: hwloc_topology_init slurmd: debug2: hwloc_topology_load slurmd: debug2: hwloc_topology_export_xml slurmd: debug: CPUs:64 Boards:1 Sockets:2 CoresPerSocket:32 ThreadsPerCore:1 slurmd: debug: cgroup/v1: init: Cgroup v1 plugin loaded slurmd: debug2: hwloc_topology_init slurmd: debug2: xcpuinfo_hwloc_topo_load: xml file (/var/lib/slurm/slurmd/hwloc_topo_whole.xml) found slurmd: debug: CPUs:64 Boards:1 Sockets:2 CoresPerSocket:32 ThreadsPerCore:1 slurmd: debug: gres/gpu: init: loaded slurmd: debug: gpu/nvml: init: init: GPU NVML plugin loaded slurmd: debug2: gpu/nvml: _nvml_init: Successfully initialized NVML slurmd: debug: gpu/nvml: _get_system_gpu_list_nvml: Systems Graphics Driver Version: 550.54.15 slurmd: debug: gpu/nvml: _get_system_gpu_list_nvml: NVML Library Version: 12.550.54.15 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVML API Version: 11 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Total CPU count: 64 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device count: 4 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: GPU index 0: slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Name: nvidia_a100-sxm4-80gb slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: UUID: GPU-808805ee-2ae5-ee6d-14fd-6e63028d4a2a slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Domain/Bus/Device: 0:1:0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Bus ID: 00000000:01:00.0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVLinks: -1,0,0,0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device File (minor number): /dev/nvidia0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: CPU Affinity Range - Machine: 24-31 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Core Affinity Range - Abstract: 24-31 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG mode: enabled slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG count: 2 slurmd: debug2: gpu/nvml: _handle_mig: GPU minor 0, MIG index 0: slurmd: debug2: gpu/nvml: _handle_mig: MIG Profile: nvidia_a100_3g.40gb slurmd: debug2: gpu/nvml: _handle_mig: MIG UUID: MIG-ab1b2cb1-d690-5564-885e-056f23c1c618 slurmd: debug2: gpu/nvml: _handle_mig: UniqueID: MIG-ab1b2cb1-d690-5564-885e-056f23c1c618 slurmd: debug2: gpu/nvml: _handle_mig: GPU Instance (GI) ID: 1 slurmd: debug2: gpu/nvml: _handle_mig: Compute Instance (CI) ID: 0 slurmd: debug2: gpu/nvml: _handle_mig: GI Minor Number: 12 slurmd: debug2: gpu/nvml: _handle_mig: CI Minor Number: 13 slurmd: debug2: gpu/nvml: _handle_mig: Device Files: /dev/nvidia0,/dev/nvidia-caps/nvidia-cap12,/dev/nvidia-caps/nvidia-cap13 slurmd: debug2: gpu/nvml: _handle_mig: GPU minor 0, MIG index 1: slurmd: debug2: gpu/nvml: _handle_mig: MIG Profile: nvidia_a100_3g.40gb slurmd: debug2: gpu/nvml: _handle_mig: MIG UUID: MIG-c43ecbcb-9f3f-5972-8eb9-6a2a09848bd5 slurmd: debug2: gpu/nvml: _handle_mig: UniqueID: MIG-c43ecbcb-9f3f-5972-8eb9-6a2a09848bd5 slurmd: debug2: gpu/nvml: _handle_mig: GPU Instance (GI) ID: 2 slurmd: debug2: gpu/nvml: _handle_mig: Compute Instance (CI) ID: 0 slurmd: debug2: gpu/nvml: _handle_mig: GI Minor Number: 21 slurmd: debug2: gpu/nvml: _handle_mig: CI Minor Number: 22 slurmd: debug2: gpu/nvml: _handle_mig: Device Files: /dev/nvidia0,/dev/nvidia-caps/nvidia-cap21,/dev/nvidia-caps/nvidia-cap22 slurmd: debug2: Possible GPU Memory Frequencies (1): slurmd: debug2: ------------------------------- slurmd: debug2: *1593 MHz [0] slurmd: debug2: Possible GPU Graphics Frequencies (81): slurmd: debug2: --------------------------------- slurmd: debug2: *1410 MHz [0] slurmd: debug2: *1395 MHz [1] slurmd: debug2: ... slurmd: debug2: *810 MHz [40] slurmd: debug2: ... slurmd: debug2: *225 MHz [79] slurmd: debug2: *210 MHz [80] slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: GPU index 1: slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Name: nvidia_a100-sxm4-80gb slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: UUID: GPU-05911075-e3e1-973b-708a-33a77ddd381c slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Domain/Bus/Device: 0:65:0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Bus ID: 00000000:41:00.0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVLinks: 0,-1,0,0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device File (minor number): /dev/nvidia1 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: CPU Affinity Range - Machine: 8-15 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Core Affinity Range - Abstract: 8-15 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG mode: enabled slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG count: 2 slurmd: debug2: gpu/nvml: _handle_mig: GPU minor 1, MIG index 0: slurmd: debug2: gpu/nvml: _handle_mig: MIG Profile: nvidia_a100_3g.40gb slurmd: debug2: gpu/nvml: _handle_mig: MIG UUID: MIG-36b02f58-efa6-5071-9956-510c8a8e705a slurmd: debug2: gpu/nvml: _handle_mig: UniqueID: MIG-36b02f58-efa6-5071-9956-510c8a8e705a slurmd: debug2: gpu/nvml: _handle_mig: GPU Instance (GI) ID: 1 slurmd: debug2: gpu/nvml: _handle_mig: Compute Instance (CI) ID: 0 slurmd: debug2: gpu/nvml: _handle_mig: GI Minor Number: 147 slurmd: debug2: gpu/nvml: _handle_mig: CI Minor Number: 148 slurmd: debug2: gpu/nvml: _handle_mig: Device Files: /dev/nvidia1,/dev/nvidia-caps/nvidia-cap147,/dev/nvidia-caps/nvidia-cap148 slurmd: debug2: gpu/nvml: _handle_mig: GPU minor 1, MIG index 1: slurmd: debug2: gpu/nvml: _handle_mig: MIG Profile: nvidia_a100_3g.40gb slurmd: debug2: gpu/nvml: _handle_mig: MIG UUID: MIG-c5ca2779-f34d-544b-af52-55ef14aa0af6 slurmd: debug2: gpu/nvml: _handle_mig: UniqueID: MIG-c5ca2779-f34d-544b-af52-55ef14aa0af6 slurmd: debug2: gpu/nvml: _handle_mig: GPU Instance (GI) ID: 2 slurmd: debug2: gpu/nvml: _handle_mig: Compute Instance (CI) ID: 0 slurmd: debug2: gpu/nvml: _handle_mig: GI Minor Number: 156 slurmd: debug2: gpu/nvml: _handle_mig: CI Minor Number: 157 slurmd: debug2: gpu/nvml: _handle_mig: Device Files: /dev/nvidia1,/dev/nvidia-caps/nvidia-cap156,/dev/nvidia-caps/nvidia-cap157 slurmd: debug2: Possible GPU Memory Frequencies (1): slurmd: debug2: ------------------------------- slurmd: debug2: *1593 MHz [0] slurmd: debug2: Possible GPU Graphics Frequencies (81): slurmd: debug2: --------------------------------- slurmd: debug2: *1410 MHz [0] slurmd: debug2: *1395 MHz [1] slurmd: debug2: ... slurmd: debug2: *810 MHz [40] slurmd: debug2: ... slurmd: debug2: *225 MHz [79] slurmd: debug2: *210 MHz [80] slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: GPU index 2: slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Name: nvidia_a100-sxm4-80gb slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: UUID: GPU-7b67964f-9217-908f-042f-f37f06cbb1a2 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Domain/Bus/Device: 0:129:0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Bus ID: 00000000:81:00.0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVLinks: 0,0,-1,4 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device File (minor number): /dev/nvidia2 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: CPU Affinity Range - Machine: 56-63 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Core Affinity Range - Abstract: 56-63 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG mode: disabled slurmd: debug2: Possible GPU Memory Frequencies (1): slurmd: debug2: ------------------------------- slurmd: debug2: *1593 MHz [0] slurmd: debug2: Possible GPU Graphics Frequencies (81): slurmd: debug2: --------------------------------- slurmd: debug2: *1410 MHz [0] slurmd: debug2: *1395 MHz [1] slurmd: debug2: ... slurmd: debug2: *810 MHz [40] slurmd: debug2: ... slurmd: debug2: *225 MHz [79] slurmd: debug2: *210 MHz [80] slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: GPU index 3: slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Name: nvidia_a100-sxm4-80gb slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: UUID: GPU-87d7526f-6a29-214e-1c24-722a20211263 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Domain/Bus/Device: 0:193:0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Bus ID: 00000000:C1:00.0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVLinks: 0,0,4,-1 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device File (minor number): /dev/nvidia3 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: CPU Affinity Range - Machine: 40-47 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Core Affinity Range - Abstract: 40-47 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG mode: disabled slurmd: debug2: Possible GPU Memory Frequencies (1): slurmd: debug2: ------------------------------- slurmd: debug2: *1593 MHz [0] slurmd: debug2: Possible GPU Graphics Frequencies (81): slurmd: debug2: --------------------------------- slurmd: debug2: *1410 MHz [0] slurmd: debug2: *1395 MHz [1] slurmd: debug2: ... slurmd: debug2: *810 MHz [40] slurmd: debug2: ... slurmd: debug2: *225 MHz [79] slurmd: debug2: *210 MHz [80] slurmd: gpu/nvml: _get_system_gpu_list_nvml: 4 GPU system device(s) detected slurmd: debug: Gres GPU plugin: Merging configured GRES with system GPUs slurmd: debug2: gres/gpu: _merge_system_gres_conf: gres_list_conf: slurmd: debug2: GRES[gpu] Type:a100_3g.39gb Count:4 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: debug2: GRES[gpu] Type:a100-sxm4-80gb Count:2 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: debug: gres/gpu: _merge_system_gres_conf: Including the following GPU matched between system and configuration: slurmd: debug: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):56-63 Links:0,0,-1,4 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia2 UniqueId:(null) slurmd: debug: gres/gpu: _merge_system_gres_conf: Including the following GPU matched between system and configuration: slurmd: debug: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):40-47 Links:0,0,4,-1 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia3 UniqueId:(null) slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: warning: The following autodetected GPUs are being ignored: slurmd: GRES[gpu] Type:nvidia_a100_3g.40gb Count:1 Cores(64):24-31 Links:(null) Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia0,/dev/nvidia-caps/nvidia-cap12,/dev/nvidia-caps/nvidia-cap13 UniqueId:MIG-ab1b2cb1-d690-5564-885e-056f23c1c618 slurmd: GRES[gpu] Type:nvidia_a100_3g.40gb Count:1 Cores(64):24-31 Links:(null) Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia0,/dev/nvidia-caps/nvidia-cap21,/dev/nvidia-caps/nvidia-cap22 UniqueId:MIG-c43ecbcb-9f3f-5972-8eb9-6a2a09848bd5 slurmd: GRES[gpu] Type:nvidia_a100_3g.40gb Count:1 Cores(64):8-15 Links:(null) Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia1,/dev/nvidia-caps/nvidia-cap147,/dev/nvidia-caps/nvidia-cap148 UniqueId:MIG-36b02f58-efa6-5071-9956-510c8a8e705a slurmd: GRES[gpu] Type:nvidia_a100_3g.40gb Count:1 Cores(64):8-15 Links:(null) Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia1,/dev/nvidia-caps/nvidia-cap156,/dev/nvidia-caps/nvidia-cap157 UniqueId:MIG-c5ca2779-f34d-544b-af52-55ef14aa0af6 slurmd: debug2: gres/gpu: _merge_system_gres_conf: gres_list_gpu slurmd: debug2: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):56-63 Links:0,0,-1,4 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia2 UniqueId:(null) slurmd: debug2: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):40-47 Links:0,0,4,-1 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia3 UniqueId:(null) slurmd: debug: Gres GPU plugin: Final merged GRES list: slurmd: debug: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):56-63 Links:0,0,-1,4 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia2 UniqueId:(null) slurmd: debug: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):40-47 Links:0,0,4,-1 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia3 UniqueId:(null) slurmd: Gres Name=gpu Type=a100-sxm4-80gb Count=1 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=a100-sxm4-80gb Count=1 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: topology/default: init: topology Default plugin loaded slurmd: CPU frequency setting not configured for this node slurmd: debug: Resource spec: No specialized cores configured by default on this node slurmd: debug: Resource spec: Reserved system memory limit not configured for this node slurmd: debug: task/cgroup: init: Tasks containment cgroup plugin loaded slurmd: task/affinity: init: task affinity plugin loaded with CPU mask 0xffffffffffffffff slurmd: debug: spank: opening plugin stack /var/lib/slurm/slurmd/conf-cache/plugstack.conf slurmd: debug: /var/lib/slurm/slurmd/conf-cache/plugstack.conf: 1: include "/etc/slurm/plugstack.conf.d/*.conf" slurmd: cred/munge: init: Munge credential signature plugin loaded slurmd: warning: Core limit is only 0 KB slurmd: slurmd version 23.11.4 started slurmd: debug2: No acct_gather.conf file (/var/lib/slurm/slurmd/conf-cache/acct_gather.conf) slurmd: debug: jobacct_gather/cgroup: init: Job accounting gather cgroup plugin loaded slurmd: debug: MPI: Loading all types slurmd: debug: mpi/pmix_v4: init: PMIx plugin loaded slurmd: debug: mpi/pmix_v4: init: PMIx plugin loaded slurmd: debug2: No mpi.conf file (/var/lib/slurm/slurmd/conf-cache/mpi.conf) slurmd: slurmd started on Fri, 10 May 2024 14:30:30 +0000 slurmd: CPUs=64 Boards=1 Sockets=2 Cores=32 Threads=1 Memory=1031664 TmpDisk=0 Uptime=2512944 CPUSpecList=(null) FeaturesAvail=(null) FeaturesActive=(null) ^Cslurmd: got shutdown request slurmd: all threads complete slurmd: debug: mpi/pmix_v4: fini: (null) [0]: mpi_pmix.c:203: (null): call fini() slurmd: debug: mpi/pmix_v4: fini: (null) [0]: mpi_pmix.c:203: (null): call fini() slurmd: debug: jobacct_gather/cgroup: fini: Job accounting gather cgroup plugin unloaded slurmd: debug2: acct_gather_profile_startpoll: poll already ended! slurmd: debug: task/cgroup: fini: Tasks containment cgroup plugin unloaded slurmd: debug: task/affinity: fini: task affinity plugin unloaded slurmd: debug: hash/k12: fini: fini: unloading KangarooTwelve hash plugin slurmd: debug: gres/gpu: fini: unloading ^Cslurmd: debug2: gpu/nvml: _nvml_shutdown: Successfully shut down NVML slurmd: debug: gpu/nvml: fini: fini: unloading GPU NVML plugin slurmd: debug2: acct_gather_profile_startpoll: poll already ended! slurmd: debug: cgroup/v1: fini: unloading Cgroup v1 plugin slurmd: cred/munge: fini: Munge credential signature plugin unloaded slurmd: Slurmd shutdown completing

1 0

Trouble Running Slurm C Extension Plugin
by Glen MacLachlan 10 May '24

10 May '24

Hi, We have a plugin in Lua that mostly does what we want but there are features available in the C extension that are not available to lua. For that reason, we are attempting to convert to C using the guidance found here: https://slurm.schedmd.com/job_submit_plugins.html#building. We arrived here because the lua plugins don't seem to stretch enough to cover the use case we were looking at, i.e., branching off of the value of alloc_id or, for that matter, get_sid(). The goal is to disallow interactive allocations (i.e., salloc) on specific partitions while allowing it on others. However, we've run into an issue with our C plugin right out of the gate and I've included a minimal reproducer as an example which is basically a "Hello World" type of test (job_submit_disallow_salloc.c, see attached). *Expectation* What we expect to happen is a sort of hello-world result with a message being written to a /tmp/min_repo.log but that does not occur. It seems that the plugin does not get run at all when jobs are submitted. Jobs still run as expected but the plugin seems to be ignored. *Steps* We compile gcc -fPIC -DHAVE_CONFIG_H -I /modules/source/slurm-23.02.4 -g -O2 -pthread -fno-gcse -Werror -Wall -g -O0 -fno-strict-aliasing -MT job_submit_disallow_salloc.lo -MD -MP -MF .deps/job_submit_disallow_salloc.Tpo -c job_submit_disallow_salloc.c -o .libs/job_submit_disallow_salloc.o mv .deps/job_submit_disallow_salloc.Tpo .deps/job_submit_disallow_salloc.Plo and link gcc -shared -fPIC -DPIC .libs/job_submit_disallow_salloc.o -O2 -pthread -O0 -pthread -Wl,-soname -Wl,job_submit_disallow_salloc.so -o job_submit_disallow_salloc.so Check links after copying to /usr/lib64/slurm: ldd /usr/lib64/slurm/job_submit_disallow_salloc.so linux-vdso.so.1 (0x00007ffe467aa000) libpthread.so.0 => /lib64/libpthread.so.0 (0x00007f1c02095000) libc.so.6 => /lib64/libc.so.6 (0x00007f1c01cd0000) /lib64/ld-linux-x86-64.so.2 (0x00007f1c024b7000) Can someone point out what we are doing incorrectly or how we might troubleshoot this issue? Kindest regards, Glen *Reproducer* The minimal reproducer is basically a "hello world" for C extensions which I've pasted below (I've also attached it for convenience): #include <slurm/slurm.h> #include <slurm/slurm_errno.h> #include <stdio.h> #include "src/slurmctld/slurmctld.h" const char plugin_name[] = "Min Reproducer"; const char plugin_type[] = "job_submit/disallow_salloc"; const uint32_t plugin_version = SLURM_VERSION_NUMBER; extern int job_submit(job_desc_msg_t *job_desc, uint32_t submit_uid, char **err_msg) { FILE *fp; fp = fopen("/tmp/min_repo.log", "w"); fprintf(fp,"Hello!"); fclose(fp); return SLURM_SUCCESS; } int job_modify(job_desc_msg_t *job_desc, job_record_t *job_ptr, uint32_t submit_uid, char **err_msg) { return SLURM_SUCCESS; }

3 2

RunTimeQuery never configured in oci.conf
by Arnuld 10 May '24

10 May '24

I am using Slurm integrated with Podman. It runs the container fine and the Controller daemon log always says "WEXITSTATUS 0" . Container also runs successfully (it runs the python test program with no errors). But there are two things that I noticed: - slurmd.log says: " error: _get_container_state: RunTimeQuery failed rc:256 output:RunTimeQuery never configured in oci.conf" - podman --log-level=debug reports: "[conmon:d]: failed to write to /proc/self/oom_score_adj: Permission denied" How can I diagnose further to know what's wrong

1 0

GPU GRES verification and some really broad questions.
by Shooktija S N 10 May '24

10 May '24

Hi, I am a complete slurm-admin and sys-admin noob trying to set up a 3 node Slurm cluster. I have managed to get a minimum working example running, in which I am able to use a GPU (NVIDIA GeForce RTX 4070 ti) as a GRES. This is *slurm.conf* without the comment lines: root@server1:/etc/slurm# grep -v "#" slurm.confClusterName=DlabClusterSlurmctldHost=server1GresTypes=gpuProctrackType=proctrack/linuxprocReturnToService=1SlurmctldPidFile=/var/run/slurmctld.pidSlurmctldPort=6817SlurmdPidFile=/var/run/slurmd.pidSlurmdPort=6818SlurmdSpoolDir=/var/spool/slurmdSlurmUser=rootStateSaveLocation=/var/spool/slurmctldTaskPlugin=task/affinity,task/cgroupInactiveLimit=0KillWait=30MinJobAge=300SlurmctldTimeout=120SlurmdTimeout=300Waittime=0SchedulerType=sched/backfillSelectType=select/cons_tresJobCompType=jobcomp/noneJobAcctGatherFrequency=30SlurmctldDebug=infoSlurmctldLogFile=/var/log/slurmctld.logSlurmdDebug=debug3SlurmdLogFile=/var/log/slurmd.logNodeName=server[1-3] RealMemory=128636 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 State=UNKNOWN Gres=gpu:1PartitionName=mainPartition Nodes=ALL Default=YES MaxTime=INFINITE State=UP This is *gres.conf* (only one line), each node has been assigned its corresponding NodeName: root@server1:/etc/slurm# cat gres.confNodeName=server1 Name=gpu File=/dev/nvidia0 Those are the only config files I have. I have a few general questions, loosely arranged in ascending order of generality: 1) I have enabled the allocation of GPU resources as a GRES and have tested this by running: shookti@server1:~$ srun --nodes=3 --gpus=3 --label hostname2: server30: server11: server2 Is this a good way to check if the configs have worked correctly? How else can I easily check if the GPU GRES has been properly configured? 2) I want to reserve a few CPU cores, and a few gigs of memory for use by non slurm related tasks. According to the documentation, I am to use CoreSpecCount <https://slurm.schedmd.com/slurm.conf.html#OPT_CoreSpecCount> and MemSpecLimit <https://slurm.schedmd.com/slurm.conf.html#OPT_MemSpecLimit> to achieve this. The documentation for CoreSpecCount says "the Slurm daemon slurmd may either be confined to these resources (the default) or prevented from using these resources", how do I change this default behaviour to have the config specify the cores reserved for non slurm stuff instead of specifying how many cores slurm can use? 3) While looking up examples online on how to run Python scripts inside a conda env, I have seen that the line 'module load conda' should be run before running 'conda activate myEnv' in the sbatch submission script. The command 'module' did not exist until I installed the apt package 'environment-modules', but now I see that conda is not listed as a module that can be loaded when I check using the command 'module avail'. How do I fix this? 4) A very broad question: while managing the resources being used by a program, slurm might happen to split the resources across multiple computers that might not necessarily have the files required by this program to run. For example, a python script that requires the package 'numpy' to function but that package was not installed on all of the computers. How are such things dealt with? Is the module approach meant to fix this problem? In my previous question, if I had a python script that users usually run just by running a command like 'python3 someScript.py' instead of running it within a conda environment, how should I enable slurm to manage the resources required by this script? Would I have to install all the packages required by this script on all the computers that are in the cluster? 5) Related to the previous question: I have set up my 3 nodes in such a way that all the users' home directories are stored on a ceph cluster <https://en.wikipedia.org/wiki/Ceph_(software)> created using the hard drives from all the 3 nodes, which essentially means that a user's home directory is mounted at the same location on all 3 computers - making a user's data visible to all 3 nodes. Does this make the process of managing the dependencies of a program as described in the previous question easier? I realise that programs having to read and write to files on the hard drives of a ceph cluster is not really the fastest so I am planning on having users use the /tmp/ directory for speed critical reading and writing, as the OSs have been installed on NVME drives.

2 1

Problems with gres.conf
by Gestió Servidors 10 May '24

10 May '24

Hello, I am trying to rewrite my gres.conf file. Before changes, this file was just like this: NodeName=node-gpu-1 AutoDetect=off Name=gpu Type=GeForceRTX2070 File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-1 AutoDetect=off Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia1 Cores=12-23 NodeName=node-gpu-2 AutoDetect=off Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-2 AutoDetect=off Name=gpu Type=GeForceGTX1080 File=/dev/nvidia1 Cores=12-23 NodeName=node-gpu-3 AutoDetect=off Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-4 AutoDetect=off Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 Cores=0-7 # you can seee that nodes node-gpu-1 and node-gpu-2 have two GPUs each one, whereas nodes node-gpu-3 and node-gpu-4 have only one GPU each one And my slurmd.conf was this: [...] AccountingStorageTRES=gres/gpu GresTypes=gpu NodeName=node-gpu-1 CPUs=24 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceRTX2070:1,gpu:GeForceGTX1080Ti:1 NodeName=node-gpu-2 CPUs=24 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceGTX1080Ti:1,gpu:GeForceGTX1080:1 NodeName=node-gpu-3 CPUs=12 SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=23000 Gres=gpu:GeForceRTX3080:1 NodeName=node-gpu-4 CPUs=8 SocketsPerBoard=1 CoresPerSocket=4 ThreadsPerCore=2 RealMemory=7800 Gres=gpu:GeForceRTX3080:1 NodeName=node-worker-[0-22] CPUs=12 SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=47000 [...] With this configuration, all seems works fine, except slurmctld.log reports: [...] error: _node_config_validate: gres/gpu: invalid GRES core specification (0-11) on node node-gpu-3 error: _node_config_validate: gres/gpu: invalid GRES core specification (12-23) on node node-gpu-1 error: _node_config_validate: gres/gpu: invalid GRES core specification (12-23) on node node-gpu-2 error: _node_config_validate: gres/gpu: invalid GRES core specification (0-7) on node node-gpu-4 [...] However, even these errors, users can submit jobs and request GPUs resources. Now, I have tried to reconfigure gres.conf and slurmd.conf in this way: gres.conf: Name=gpu Type=GeForceRTX2070 File=/dev/nvidia0 Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia1 Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia0 Name=gpu Type=GeForceGTX1080 File=/dev/nvidia1 Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 # there is no NodeName attribute slurmd.conf: [...] NodeName=node-gpu-1 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceRTX2070:1,gpu:GeForceGTX1080Ti:1 NodeName=node-gpu-2 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceGTX1080Ti:1,gpu:GeForceGTX1080:1 NodeName=node-gpu-3 SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=23000 Gres=gpu:GeForceRTX3080:1 NodeName=node-gpu-4 SocketsPerBoard=1 CoresPerSocket=4 ThreadsPerCore=2 RealMemory=7800 Gres=gpu:GeForceRTX3080:1 NodeName=node-worker-[0-22] SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=47000 # there is no CPUs attribute [...] With this new configuration, nodes with GPU start correctly slurmd.service daemon, but nodes without GPU (node-worker-[0-22]) can't start slurmd.service daemon and returns this error: [...] error: Waiting for gres.conf file /dev/nvidia0 fatal: can't stat gres.conf file /dev/nvidia0: No such file or directory [...] It seems SLURM is waiting that "node-workers" have also an nvidia GPU but not, theses nodes haven't GPU... So, where is my configuration error? I have read in https://slurm.schedmd.com/gres.conf.html about syntax and examples but it seems I'm doing some wrong. Thanks!!

1 0

Re: srun launched mpi job occasionally core dumps
by Henderson, Brent 08 May '24

08 May '24

Thanks for the suggestion Ole - I'll see if I can get that in the mix to try over the next few days. I can report that 23.02.7 tree had the same issues, so going backwards on the slurm bits did not have any impact. Brent

1 0

Partition Preemption Configuration Question
by Jason Simms 08 May '24

08 May '24

Hello all, The Slurm docs have me a bit confused... I'm wanting to enable job preemption on certain partitions but not others. I *presume* I would set PreemptType=preempt/partition_prio globally, but then on the partitions where I don't want jobs to be able to be preempted, I would set PreemptMode=off within the configuration for that specific partition. The documentation, however, says that setting PreemptMode=off at a partition level "is only compatible with PreemptType=preempt/none at a global level" yet then immediately says that doing so is a "common use case for this parameter is to set it on a partition to disable preemption for that partition," which indicates preemption would still be allowable for other partitions. If PreemptType is set to preempt/none globally, and I *cannot* set that as an option for a given partition (at least, the documentation doesn't indicate that is a valid parameter for a partition), wouldn't preemption be disabled globally anyway? The wording seems odd to me and almost contradictory. Is it possible to have PreemptType=preempt/partition_prio set globally, yet also disable it on specific partitions with PreemptMode=off? Is PreemptType actually a valid configuration option for specific partitions? Thanks for any guidance. Warmest regards, Jason -- *Jason L. Simms, Ph.D., M.P.H.* Manager of Research Computing Swarthmore College Information Technology Services (610) 328-8102 Schedule a meeting: https://calendly.com/jlsimms

3 3

Container Jobs "hanging"
by sean+slurm＠superorbital.io 08 May '24

08 May '24

Hello. I am new to this list and Slurm overall. I have a lot of experience in computer operations, including Kubernetes, but I am currently exploring Slurm in some depth. I have set up a small cluster and, in general, have gotten things working, but when I try to run a container job, it runs the command but then appears to hang as if the job container is still running. So, running the following works, but it never returns to the prompt unless I use [Control-C]. $ srun --container /shared_fs/shared/oci_images/alpine uptime 19:21:47 up 20:43, 0 users, load average: 0.03, 0.25, 0.15 I'm unsure if something is misconfigured or if I'm misunderstanding how this should work, but any help and/or pointers would be greatly appreciated. Thanks! Sean

1 0

scrontab question
by Sandor 08 May '24

08 May '24

I am working out the details of scrontab. My initial testing is giving me an unsolvable question Within scrontab editor I have the following example from the slurm documentation: 0,5,10,15,20,25,30,35,40,45,50,55 * * * * /directory/subdirectory/crontest.sh When I save it, scrontab marks the line with #BAD: I do not understand why. The only difference I have is the directory structure. Is there an underlying assumption that traditional Linux crontab is available to the general user?

4 3

Slurm With Podman - No child processes error
by ARNULD 08 May '24

08 May '24

I have integrated Podman with Slurm as per the docs ( https://slurm.schedmd.com/containers.html#podman-scrun) and when I do a test run: "podman run hello-world" (this runs fine) ---- $ podman run alpine hostname executable file `/usr/bin/hostname` not found in $PATH: No such file or directory srun: error: slurm1: task 0: Exited with exit code 1 ----- $ podman run alpine printenv SLURM_JOB_ID executable file `/usr/bin/printenv` not found in $PATH: No such file or directory srun: error: slurm1: task 0: Exited with exit code 1 scrun: error: run_command_waitpid_timeout: waitpid(67537): No child processes --- podman run alpine uptime 11:31:28 up 5:32, 0 users, load average: 0.00, 0.00, 0.00 scrun: error: run_command_waitpid_timeout: waitpid(68160): No child processes ------ I built a small image from python:alpine3.19 which just prints "hello world" and numbers from 1 to 10. Here is a run: $ podman run -it --rm hello-python $ podman run -it --rm hello-python Hello, world! Numbers from 1 to 10: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] ---- No error with my image. Also I tested podman on another machine without Slurm. Podman with its default runtime prints the hostname fine with "podman run alpine hostname". So something to do with its integration with Slurm. What can I do to diagnose the problem?

1 0

StateSaveLocation and Slurm HA
by Pierre Abele 07 May '24

07 May '24

Hi all, I am looking for a clean way to set up Slurms native high availability feature. I am managing a Slurm cluster with one control node (hosting both slurmctld and slurmdbd), one login node and a few dozen compute nodes. I have a virtual machine that I want to set up as a backup control node. The Slurm documentation says the following about the StateSaveLocation directory: > The directory used should be on a low-latency local disk to prevent file system delays from affecting Slurm performance. If using a backup host, the StateSaveLocation should reside on a file system shared by the two hosts. We do not recommend using NFS to make the directory accessible to both hosts, but do recommend a shared mount that is accessible to the two controllers and allows low-latency reads and writes to the disk. If a controller comes up without access to the state information, queued and running jobs will be cancelled. [1] My question: How do I implement the shared file system for the StateSaveLocation? I do not want to introduce a single point of failure by having a single node that hosts the StateSaveLocation, neither do I want to put that directory on the clusters NFS storage since outages/downtime of the storage system will happen at some point and I do not want that to cause an outage of the Slurm controller. Any help or ideas would be appreciated. Best, Pierre [1] https://slurm.schedmd.com/quickstart_admin.html#Config -- Pierre Abele, M.Sc. HPC Administrator Max-Planck-Institute for Evolutionary Anthropology Department of Primate Behavior and Evolution Deutscher Platz 6 04103 Leipzig Room: U2.80 E-Mail: pierre_abele(a)eva.mpg.de Phone: +49 (0) 341 3550 245

3 2

"token expired" errors with auth/slurm
by Fabio Ranalli 07 May '24

07 May '24

Hi there, We've updated to 23.11.6 and replaced MUNGE with SACK. Performance and stability have both been pretty good, but we're occasionally seeing this in the slurmctld.log /[2024-05-07T03:50:16.638] error: decode_jwt: token expired at 1715053769 [2024-05-07T03:50:16.638] error: cred_p_unpack: decode_jwt() failed [2024-05-07T03:50:16.638] error: Malformed RPC of type REQUEST_BATCH_JOB_LAUNCH(4005) received [2024-05-07T03:50:16.641] error: slurm_receive_msg_and_forward: [[headnode.internal]:58286] failed: Header lengths are longer than data received [2024-05-07T03:50:16.648] error: service_connection: slurm_receive_msg: Header lengths are longer than data received/ it seems to impact a subset of nodes: jobs get killed and no new ones are allocated. Full functionality can be restored by simply restarting slurmctld first, and then slurmd. Is the token expected to actually expire? I didn't see this possibility mentioned in the docs. The problem occurs on an R&D cloud cluster based on EL9, with a pretty "flat" setup. _headnode_: configless slurmctld, slurmdbd, mariadb, nfsd _elastic compute nodes_: autofs, slurmd *//etc/slurm/slurm.conf/* AuthType=auth/slurm AuthInfo=use_client_ids CredType=cred/slurm *//etc/slurm/slurmdbd.conf/* AuthType=auth/slurm AuthInfo=use_client_ids Has anyone else encountered the same error? Thanks, Fabio -- *Fabio Ranalli* | Principal Systems Administrator Schrödinger, Inc. <https://schrodinger.com>

1 0

FreeBSD/aarch64: ld: error: unknown emulation: elf_aarch64
by Nuno Teixeira 06 May '24

06 May '24

Hello, I'm new maintainer of slurm FreeBSD port <https://www.freshports.org/sysutils/slurm-wlm/> and for a long time it failing on aarch64 platform. Any clues? > ld: error: unknown emulation: elf_aarch64 `clang -print-targets`: Registered Targets: aarch64 - AArch64 (little endian) aarch64_32 - AArch64 (little endian ILP32) aarch64_be - AArch64 (big endian) arm - ARM arm64 - ARM64 (little endian) arm64_32 - ARM64 (little endian ILP32) armeb - ARM (big endian) ppc32 - PowerPC 32 ppc32le - PowerPC 32 LE ppc64 - PowerPC 64 ppc64le - PowerPC 64 LE riscv32 - 32-bit RISC-V riscv64 - 64-bit RISC-V thumb - Thumb thumbeb - Thumb (big endian) x86 - 32-bit X86: Pentium-Pro and above x86-64 - 64-bit X86: EM64T and AMD64 Full log: https://pkg-status.freebsd.org/ampere2/data/main-arm64-default/pb78025a96ed… Thanks, -- Nuno Teixeira FreeBSD UNIX: <eduardo(a)FreeBSD.org> Web: https://FreeBSD.org

3 7

Rootless Docker Errors with Slurm
by ARNULD 06 May '24

06 May '24

I am trying to integrate Rootless Docker with Slurm. have set-up Rootless Docker as per the docs "https://slurm.schedmd.com/containers.html" . I have scrum.lua, oci.conf (for crun) and slurm.conf in place. Then "~/.config/docker/daemon.json" and "~/.config/systemd/user/docker.service.d/override.conf" are in place too. But I can't seem to get it to work: $ docker run $DOCKER_SECURITY alpine /bin/printenv SLURM_JOB_ID Unable to find image 'alpine:latest' locally latest: Pulling from library/alpine 4abcf2066143: Pull complete Digest: sha256:c5b1261d6d3e43071626931fc004f70149baeba2c8ec672bd4f27761f8e1ad6b Status: Downloaded newer image for alpine:latest docker: Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: unable to retrieve OCI runtime error (open /run/user/1000/docker-exec/containerd/daemon/io.containerd.runtime.v2.task/moby/97e8dd767977ac03ab7af54c015c0fd5dfd26e737771b977acb7e41f799023aa/log.json: no such file or directory): /usr/bin/scrun did not terminate successfully: exit status 1: unknown. One thing is if I don't use Slurm as the runtime for Docker (if I remove the " ~/.config/docker/daemon.json") then docker runs fine

1 0

Invalid/incorrect gres.conf syntax
by Gestió Servidors 06 May '24

06 May '24

Hello, I have configured my "gres.conf" in this way: NodeName=node-gpu-1 AutoDetect=off Name=gpu Type=GeForceRTX2070 File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-1 AutoDetect=off Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia1 Cores=12-23 NodeName=node-gpu-2 AutoDetect=off Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-2 AutoDetect=off Name=gpu Type=GeForceGTX1080 File=/dev/nvidia1 Cores=12-23 NodeName=node-gpu-3 AutoDetect=off Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-4 AutoDetect=off Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 Cores=0-7 node-gpu-1 and node-gpu-2 are two systems with two sockets; node-gpu-3 and node-gpu-4 have only one socket. In my "slurm.conf" I have these lines: AccountingStorageTRES=gres/gpu SelectType=select/cons_tres GresTypes=gpu NodeName=node-gpu-1 CPUs=24 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceRTX2070:1,gpu:GeForceGTX1080Ti:1 NodeName=node-gpu-2 CPUs=24 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceGTX1080Ti:1,gpu:GeForceGTX1080:1 NodeName=node-gpu-3 CPUs=12 SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=23000 Gres=gpu:GeForceRTX3080:1 NodeName=node-gpu-4 CPUs=8 SocketsPerBoard=1 CoresPerSocket=4 ThreadsPerCore=2 RealMemory=7800 Gres=gpu:GeForceRTX3080:1 Thanks a lot!

1 0

slurm reservation: how to use more nodes (bigger than NodeCnt) when submitting jobs
by WANG, Hongying 03 May '24

03 May '24

Hi all, I have a question about the reservation. If I create a reservation with 3 nodes (NodeCnt=3), how can I use more than 3 nodes when submitting a job with --reservation=$RES_NAME ? Thanks a lot. Regards, -- WANG, Hongying ClusterTech Limited Advanced I.T. Solutions and Consultancy SINCE 2000 Cloud + High Performance Computing + AI & Analytics + Big Data www.clustertech.com Units 211-213, Lakeside 1, No. 8 Science Park West Avenue, Hong Kong Science Park, N.T. Hong Kong Hong Kong • Beijing • Shanghai • Guangzhou • Shenzhen • Xi'an • Sydney

1 0

scheduling according time requirements
by Dietmar Rieder 30 Apr '24

30 Apr '24

Hi, is it possible to have slurm scheduling jobs automatical according to the "-t" time requirements to a fitting partition? e.g. 3 partitions PartitionName=standard Nodes=c-[01-10] Default=YES MaxTime=04:00:00 DefaultTime=00:10:00 State=UP OverSubscribe=NO PartitionName=medium Nodes=c-[04-08] Default=NO MaxTime=24:00:00 DefaultTime=04:00:00 State=UP OverSubscribe=NO PartitionName=long Nodes=c-[09-10] Default=NO MaxTime=336:00:00 DefaultTime=24:00:00 State=UP OverSubscribe=NO So in the standard partition which is the default we have all nodes and a max time of 4h, in the medium partition we have 4 nodes with a max time of 24h and in the long partition we have 2 nodes with a max time of 336h. I was hoping that if I submit a job with -t 01:00:00 it can be run on any node (standard partition), whereas when specifying -t 05:00:00 or -t 48:00:00 the job will run on the nodes of the medium or long partition respectively. However, my job will not get scheduled at all when -t is greater than 01:00:00 i.e. ]$ srun --cpus-per-task 1 -t 01:00:01 --pty bash srun: Requested partition configuration not available now srun: job 42095 queued and waiting for resources it will wait forever because the standard partition is selected, I was thinking that slurm would automatically switch to the medium partition. Do I misunderstand something there? Or can this be somehow configured. Thanks so much and sorry for the naive question Dietmar

4 10

Trying to Track Down root Usage
by Jason Simms 29 Apr '24

29 Apr '24

Hello all, Each week, I generate an automated report of the top users by CPU hours. This week, for whatever reason the user root accounted for a massive number of hours: -------------------------------------------------------- Login Proper Name Used Account --------------- -------------------- --------- --------- root root 28224 root I've tried to generate a report on what might account for this, but so far I'm stumped. For example, when I run a typical report: sacct -o jobid,jobname,start,end,NNodes,NCPUS,ReqMem,CPUTime,AveRSS,MaxRSS -S 2024-04-22 -E 2024-04-29 --user=root --units=G Nothing comes up (same if I change --user=root to --account=root). I'd appreciate any thoughts as to how to investigate or understand this. It may well be that I'm missing something fundamental. Warmest regards, Jason -- *Jason L. Simms, Ph.D., M.P.H.* Manager of Research Computing Swarthmore College Information Technology Services (610) 328-8102 Schedule a meeting: https://calendly.com/jlsimms

2 2

HPC Principal System Engineer at the Broad
by Paul Edmon 25 Apr '24

25 Apr '24

A friend ask me to pass this along. Figured some folks on this list might be interested. https://broadinstitute.avature.net/en_US/careers/JobDetail/HPC-Principal-Sy… -Paul Edmon-

1 0

Apply an specific QoS to all users that belongs to an specific account
by Gestió Servidors 23 Apr '24

23 Apr '24

Hi, I would like to know if it is possible to apply an specific QoS to all users that belongs to an specific account. For example, I have created some new users "user_XX" and, also, I have created their new accounts in SLURM with "sacctmgr create account name=Test" and "sacctmgr create user name=user_XX DefaultAccount=Test". After it, I have changed default QoS "normal" to a new QoS "minimal" (with some limits) to account "Test" (sacctmgr modify account where name=Test set qos=minimal), but what I have seen is that users "user_XX" that belongs to "Test" account continue in QoS "normal" (QoS by default), so it seems that users have not inherited QoS applied to their account. Is there any way to do this and am I doing something wrong? Thanks. -- [cid:image003.jpg@01DA9569.AE233AA0]<http://www.uab.cat/> Daniel Ruiz Molina Tècnic Mitjà Informàtic Arquitectura de Computadors i Sistemes Operatius Escola d'Enginyeria Edifici Q - Despatx QC/3052 - Carrer de les Sitges Campus de la UAB · 08193 Bellaterra (Cerdanyola del Vallès) · Barcelona · Spain +34 93 581 35 44 www.uab.cat<http://www.uab.cat/> Daniel Ruiz at UAB<https://tinyurl.com/yd95zb8j> [cid:image004.jpg@01DA9569.AE233AA0]<www.linkedin.com/in/daniel-ruiz-molina-50a83b27> Aquest missatge s'adreça exclusivament a la persona destinatària i pot contenir informació privada o confidencial. Si l'heu rebut per error, comuniqueu-nos-ho i destruïu-lo, i tingueu present que no teniu autorització per fer-ne cap ús. Abans d'imprimir aquest missatge penseu en el medi ambient.

1 0

Integrating Slurm with WekaIO
by Jeffrey Layton 19 Apr '24

19 Apr '24

Good afternoon, I'm working on a cluster of NVIDIA DGX A100's that is using BCM 10 (Base Command Manager which is based on Bright Cluster Manager). I ran into an error and only just learned that Slurm and Weka don't get along (presumably because Weka pins their client threads to cores). I read through their documentation here: https://docs.weka.io/best-practice-guides/weka-and-slurm-integration#headin… I through I set everything correctly but when I try to restart the slurm server I get the following: Apr 19 05:29:39 bcm10-headnode slurmd[3992058]: slurmd: error: resolve_ctls_from_dns_srv: res_nsearch error: Unknown host Apr 19 05:29:39 bcm10-headnode slurmd[3992058]: slurmd: error: fetch_config: DNS SRV lookup failed Apr 19 05:29:39 bcm10-headnode slurmd[3992058]: slurmd: error: _establish_configuration: failed to load configs Apr 19 05:29:39 bcm10-headnode slurmd[3992058]: slurmd: error: slurmd initialization failed Apr 19 05:29:39 bcm10-headnode slurmd[3992058]: error: resolve_ctls_from_dns_srv: res_nsearch error: Unknown host Apr 19 05:29:39 bcm10-headnode slurmd[3992058]: error: fetch_config: DNS SRV lookup failed Apr 19 05:29:39 bcm10-headnode slurmd[3992058]: error: _establish_configuration: failed to load configs Apr 19 05:29:39 bcm10-headnode slurmd[3992058]: error: slurmd initialization failed Apr 19 05:29:39 bcm10-headnode systemd[1]: slurmd.service: Main process exited, code=exited, status=1/FAILURE Apr 19 05:29:39 bcm10-headnode systemd[1]: slurmd.service: Failed with result 'exit-code'. Has anyone encountered this? I read this is usually associated with configless Slurm, but I don't know how Slurm is built in BCM. slurm.conf is located in /cm/shared/apps/slurm/var/etc/slurm and this is what I edited. The environment variables for Slurm are set correctly so it points to this slurm.conf file. One thing that I did not do was tell Slurm which cores Weka was using. I can seem to figure out the syntax for this. Can someone share the changes they made to slurm.conf? Thanks! Jeff

3 5

Munge log-file fills up the file system to 100%
by Ole Holm Nielsen 19 Apr '24

19 Apr '24

We have some new AMD EPYC compute nodes with 96 cores/node running RockyLinux 8.9. We've had a number of incidents where the Munge log-file /var/log/munge/munged.log suddenly fills up the root file system, after a while to 100% (tens of GBs), and the node eventually comes to a grinding halt! Wiping munged.log and restarting the node works around the issue. I've tried to track down the symptoms and this is what I found: 1. In munged.log there are infinitely many lines filling up the disk: 2024-04-11 09:59:29 +0200 Info: Suspended new connections while processing backlog 2. The slurmd is not getting any responses from munged, even though we run "munged --num-threads 10". The slurmd.log displays errors like: [2024-04-12T02:05:45.001] error: If munged is up, restart with --num-threads=10 [2024-04-12T02:05:45.001] error: Munge encode failed: Failed to connect to "/var/run/munge/munge.socket.2": Resource temporarily unavailable [2024-04-12T02:05:45.001] error: slurm_buffers_pack_msg: auth_g_create: RESPONSE_ACCT_GATHER_UPDATE has authentication error 3. The /var/log/messages displays the errors from slurmd as well as NetworkManager saying "Too many open files in system". The telltale syslog entry seems to be: Apr 12 02:05:48 e009 kernel: VFS: file-max limit 65536 reached where the limit is confirmed in /proc/sys/fs/file-max. We have never before seen any such errors from Munge. The error may perhaps be triggered by certain user codes (possibly star-ccm+) that might be opening a lot more files on the 96-core nodes than on nodes with a lower core count. My workaround has been to edit the line in /etc/sysctl.conf: fs.file-max = 131072 and update settings by "sysctl -p". We haven't seen any of the Munge errors since! The version of Munge in RockyLinux 8.9 is 0.5.13, but there is a newer version in https://github.com/dun/munge/releases/tag/munge-0.5.16 I can't figure out if 0.5.16 has a fix for the issue seen here? Questions: Have other sites seen the present Munge issue as well? Are there any good recommendations for setting the fs.file-max parameter on Slurm compute nodes? Thanks for sharing your insights, Ole -- Ole Holm Nielsen PhD, Senior HPC Officer Department of Physics, Technical University of Denmark

4 10

Reserving resources for use by non-slurm stuff
by Shooktija S N 17 Apr '24

17 Apr '24

Hi, I am running Slurm (v22.05.8) on 3 nodes each with the following specs: OS: Proxmox VE 8.1.4 x86_64 (based on Debian 12) CPU: AMD EPYC 7662 (128) GPU: NVIDIA GeForce RTX 4070 Ti Memory: 128 Gb This is /etc/slurm/slurm.conf on all 3 computers without the comment lines: ClusterName=DlabCluster SlurmctldHost=server1 GresTypes=gpu ProctrackType=proctrack/linuxproc ReturnToService=1 SlurmctldPidFile=/var/run/slurmctld.pid SlurmctldPort=6817 SlurmdPidFile=/var/run/slurmd.pid SlurmdPort=6818 SlurmdSpoolDir=/var/spool/slurmd SlurmUser=root StateSaveLocation=/var/spool/slurmctld TaskPlugin=task/affinity,task/cgroup InactiveLimit=0 KillWait=30 MinJobAge=300 SlurmctldTimeout=120 SlurmdTimeout=300 Waittime=0 SchedulerType=sched/backfill SelectType=select/cons_tres JobCompType=jobcomp/none JobAcctGatherFrequency=30 SlurmctldDebug=debug3 SlurmctldLogFile=/var/log/slurmctld.log SlurmdDebug=debug3 SlurmdLogFile=/var/log/slurmd.log NodeName=server[1-3] RealMemory=128636 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 State=UNKNOWN Gres=gpu:1 PartitionName=mainPartition Nodes=ALL Default=YES MaxTime=INFINITE State=UP I want to reserve a few cores and a few gigs of RAM for use only by the OS which cannot be accessed by jobs being managed by Slurm. What configuration do I need to do to achieve this? Is it possible to reserve in a similar fashion a 'percent' of the GPU which Slurm cannot exceed so that the OS has some GPU resources? Is it possible to have these configs be different for each of the 3 nodes? Thanks!

3 2

Inconsistencies in CPU time Reporting by sreport and sacct Tools
by KK 17 Apr '24

17 Apr '24

I wish to ascertain the CPU core time utilized by user dj1 and dj. I have tested with sreport cluster UserUtilizationByAccount, sreport job SizesByAccount, and sacct. It appears that sreport cluster UserUtilizationByAccount displays the total core hours used by the entire account, rather than the individual user's cpu time. Here are the specifics: Users dj and dj1 are both under the account mehpc. In 2024-04-12 ~ 2024-04-15, dj1 used approximately 10 minutes of core time, while dj used about 4 minutes. However, "sreport Cluster UserUtilizationByAccount user=dj1 start=2024-04-12 end=2024-04-15" shows 14 minutes of usage. Similarly, "sreport job SizesByAccount Users=dj start=2024-04-12 end=2024-04-15" hows about 14 minutes. Using "sreport job SizesByAccount Users=dj1 start=2024-04-12 end=2024-04-15" or "sacct -u dj1 -S 2024-04-12 -E 2024-04-15 -o "jobid,partition,account,user,alloccpus,cputimeraw,state,workdir%60" -X |awk 'BEGIN{total=0}{total+=$6}END{print total}'" yields the accurate values, which are around 10 minutes for dj1. Attachment are the details.

1 0

Association limit problem
by Gestió Servidors 17 Apr '24

17 Apr '24

Hello, I'm doing some test with "associations" with "sacctmgr". I have created three users (user_1, user_2 and user_3). For each of these users, I have created an association: [root@myserver log]# sacctmgr show user user_1 --associations User Def Acct Admin Cluster Account Partition Share Priority MaxJobs MaxNodes MaxCPUs MaxSubmit MaxWall MaxCPUMins QOS Def QOS ---------- ---------- --------- ---------- ---------- ---------- --------- ---------- ------- -------- -------- --------- ----------- ----------- -------------------- --------- user_1 test None q50004 test aolin.q 1 4 2 10 normal user_1 test None q50004 test cuda-staf+ 1 4 2 10 normal [root@myserver log]# sacctmgr show user user_2 --associations User Def Acct Admin Cluster Account Partition Share Priority MaxJobs MaxNodes MaxCPUs MaxSubmit MaxWall MaxCPUMins QOS Def QOS ---------- ---------- --------- ---------- ---------- ---------- --------- ---------- ------- -------- -------- --------- ----------- ----------- -------------------- --------- user_2 test None q50004 test cuda-int.q 1 4 normal [root@myserver log]# sacctmgr show user user_3 --associations User Def Acct Admin Cluster Account Partition Share Priority MaxJobs MaxNodes MaxCPUs MaxSubmit MaxWall MaxCPUMins QOS Def QOS ---------- ---------- --------- ---------- ---------- ---------- --------- ---------- ------- -------- -------- --------- ----------- ----------- -------------------- --------- user_3 test None q50004 test research.q 1 2 1 normal user_3 test None q50004 test xeon.q 1 2 1 normal All users belong to "Test" account: [root@myserver log]# sacctmgr show account test --association Account Descr Org Cluster ParentName User Share Priority GrpJobs GrpNodes GrpCPUs GrpMem GrpSubmit GrpWall GrpCPUMins MaxJobs MaxNodes MaxCPUs MaxSubmit MaxWall MaxCPUMins QOS Def QOS ---------- -------------------- -------------------- ---------- ---------- ---------- --------- ---------- ------- -------- -------- ------- --------- ----------- ----------- ------- -------- -------- --------- ----------- ----------- -------------------- --------- test test test q50004 root 1 normal test test test q50004 user_1 1 4 2 10 normal test test test q50004 user_1 1 4 2 10 normal test test test q50004 user_2 1 4 normal test test test q50004 user_3 1 2 1 normal test test test q50004 user_3 1 2 1 normal When I submit with "user_1", all tests are running fine: some jobs are queued and executed and some jobs are rejected because of the limits. However, with users "user_2" and "user_3" I can't submit any job. All jobs are rejected with these messages: 11168 research. test user_3 PENDING 0:00 2024-04-17T12:53:21 N/A 1 1 OK N/A (AssocMaxCpuPerJo (null) 11173 research. test user_3 PENDING 0:00 2024-04-17T13:06:02 N/A 1 1 OK N/A (AssocMaxCpuPerJo (null) 11174 research. test user_3 PENDING 0:00 2024-04-17T13:06:16 N/A 1 1 OK N/A (AssocMaxCpuPerJo (null) 11176 research. test user_3 PENDING 0:00 2024-04-17T13:07:23 N/A 1 1 OK N/A (AssocMaxCpuPerJo (null) 11180 research. test user_3 PENDING 0:00 2024-04-17T13:08:45 N/A 1 1 OK N/A (AssocMaxCpuPerJo (null) For example, user "user_3" are trying to submit in this way (test.sh script only is a simple "sleep 50": sbatch -p aolin.q -N 2 ./test.sh --> sbatch: error: Batch job submission failed: Invalid account or account/partition combination specified sbatch -p aolin.q -N 1 ./test.sh --> sbatch: error: Batch job submission failed: Invalid account or account/partition combination specified sbatch -p research.q -N 1 ./test.sh --> submitted but not running --> nodelist(reason)= (AssocMaxCpuPerJobLimit) -> WHY??? sbatch -p research.q -N 1 -n 1 ./test.sh --> submitted but not running --> nodelist(reason)= (AssocMaxCpuPerJobLimit) --> WHY??? sbatch -p xeon.q -N 1 -n 1 ./test.sh --> submitted and running!! [root@myserver log]# squeue JOBID PARTITION NAME USER STATE TIME SUBMIT_TIME START_TIME NODE CPUS OVER_S TRES_PER_NODE NODELIST(REASON) DEPENDENCY REQ_NODES NODELIST 11202 research. test user_3 PENDING 0:00 2024-04-17T13:33:31 N/A 1 1 OK N/A (AssocMaxCpuPerJo (null) 11200 research. test user_3 PENDING 0:00 2024-04-17T13:33:17 N/A 1 1 OK N/A (AssocMaxCpuPerJo (null) 11212 xeon.q test user_3 RUNNING 0:18 2024-04-17T13:36:10 2024-04-17T13:36:10 1 1 OK N/A aolin-cpu-1 (null) aolin-cpu-1 Why? What am I doing wrong? Where is the limit that I am not seeing? Thanks a lot!

1 0

Redirect jobs submitted to old partition to new
by wdennis＠nec-labs.com 17 Apr '24

17 Apr '24

Hi all, I have a single-partition Slurm cluster (the single partition name being "default_queue") that I now want to implement multiple different queues on to subdivide the resources. Say the new default queue is "queue1"; Should I set the "default_queue" to `State=INACTIVE` and then use `Alternate=queue1` on it to redirect jobs sent to the old "default_queue" to end up on "queue1"? Thinking it would be nice to have an AltPartitionName= construct to handle this... (must be a reason this doesn't exist)

2 1

Slurm version 23.11.6 is now available
by Marshall Garey 16 Apr '24

16 Apr '24

We are pleased to announce the availability of Slurm version 23.11.6. The 23.11.6 release includes two different problems with the priority/multifactor plugin: a crash and a miscalculation of AssocGrpCPURunMinutes after a slurmctld reconfiguration/restart. The wsrep_on errors that sites running MySQL or older MariaDB should happen much less frequently and has a clarifying statement when it is an innocuous error. Slurm can be downloaded from https://www.schedmd.com/downloads.php . -Marshall > * Changes in Slurm 23.11.6 > ========================== > -- Avoid limiting sockets per node to one when using gres enforce-binding. > -- slurmrestd - Avoid permission denied errors when attempting to listen on > the same port multiple times. > -- Fix GRES reservations where the GRES has no topology > (no cores= in gres.conf). > -- Ensure that thread_id_rpc is gone before priority_g_fini(). > -- Fix scontrol reboot timeout removing drain state from nodes. > -- squeue - Print header on empty reponse to `--only-job-state`. > -- Fix slurmrestd not ending job properly when xauth is not present and a x11 > job is sent. > -- Add experimental job state caching with > SchedulerParameters=enable_job_state_cache to speed up querying job states > with squeue --only-job-state. > -- slurmrestd - Correct dumping of invalid ArrayJobIds returned from > 'GET /slurm/v0.0.40/jobs/state'. > -- squeue - Correct dumping of invalid ArrayJobIds returned from > `squeue --only-job-state --{json|yaml}`. > -- If scancel --ctld is not used with --interactive, --sibling, or specific > step ids, then this option issues a single request to the slurmctld to > signal all jobs matching the specified filters. This greatly improves > the performance of slurmctld and scancel. The updated --ctld option also > fixes issues with the --partition or --reservation scancel options for jobs > that requested multiple partitions or reservations. > -- slurmrestd - Give EINVAL error when failing to parse signal name to numeric > signal. > -- slurmrestd - Allow ContentBody for all methods per RFC7230 even if ignored. > -- slurmrestd - Add 'DELETE /slurm/v0.0.40/jobs' endpoint to allow bulk job > signaling via slurmctld. > -- Fix combination of --nodelist and --exclude not always respecting the > excluded node list. > -- Fix jobs incorrectly allocating nodes exclusively when started on a > partition that doesn't enforce it. This could happen if a multi-partition > job doesn't specify --exclusive and is evaluated first on a partition > configured with OverSubscribe=EXCLUSIVE but ends up starting in a partition > configured with OverSubscribe!=EXCLUSIVE evaluated afterwards. > -- Setting GLOB_SILENCE flag no longer exposes old bugged behavior. > -- Fix associations AssocGrpCPURunMinutes being incorrectly computed for > running jobs after a controller reconfiguration/restart. > -- Fix scheduling jobs that request --gpus and nodes have different node > weights and different numbers of gpus. > -- slurmrestd - Add "NO_CRON_JOBS" as possible flag value to the following: > 'DELETE /slurm/v0.0.40/jobs' flags field. > 'DELETE /slurm/v0.0.40/job/{job_id}?flags=' flags query parameter. > -- Fix scontrol segfault/assert failure if the TRESPerNode parameter is used > when creating reservations. > -- Avoid checking for wsrep_on when restoring streaming replication settings. > -- Clarify in the logs that error "1193 Unknown system variable 'wsrep_on'" is > innocuous. > -- accounting_storage/mysql - Fix problem when loading reservations from an > archive dump. > -- slurmdbd - Fix minor race condition when sending updates to a shutdown > slurmctld. > -- slurmctld - Fix invalid refusal of a reservation update. > -- openapi - Fix memory leak of /meta/slurm/cluster response field. > -- Fix memory leak when using auth/slurm and AuthInfo=use_client_ids. -- Marshall Garey Release Management, Support, and Development SchedMD LLC - Commercial Slurm Development and Support

1 0

Fwd: sreport cluster UserUtilizationByaccount Used result versus sreport job SizesByAccount or sacct: inconsistencies
by KK 16 Apr '24

16 Apr '24

---------- Forwarded message --------- 发件人： KK <daijiangkuicgo(a)gmail.com> Date: 2024年4月15日周一 13:25 Subject: sreport cluster UserUtilizationByaccount Used result versus sreport job SizesByAccount or sacct: inconsistencies To: <slurm-users(a)schedmd.com> I wish to ascertain the CPU core hours utilized by user dj1 and dj. I have tested with sreport cluster UserUtilizationByAccount, sreport job SizesByAccount, and sacct. It appears that sreport cluster UserUtilizationByAccount displays the total core hours used by the entire account, rather than the individual user's cpu time. Here are the specifics: Users dj and dj1 are both under the account mehpc. In 2024-04-12 ~ 2024-04-15, dj1 used approximately 10 minutes of core time, while dj used about 4 minutes. However, "*sreport Cluster UserUtilizationByAccount user=dj1 start=2024-04-12 end=2024-04-15*" shows 14 minutes of usage. Similarly, "*sreport job SizesByAccount Users=dj start=2024-04-12 end=2024-04-15*" hows about 14 minutes. Using "*sreport job SizesByAccount Users=dj1 start=2024-04-12 end=2024-04-15*" or "*sacct -u dj1 -S 2024-04-12 -E 2024-04-15 -o "jobid,partition,account,user,alloccpus,cputimeraw,state,workdir%60" -X |awk 'BEGIN{total=0}{total+=$6}END{print total}'*" yields the accurate values, which are around 10 minutes for dj1. Here are the details: [root@ood-master ~]# sacctmgr list assoc format=cluster,user,account,qos Cluster User Account QOS ---------- ---------- ---------- -------------------- mehpc root normal mehpc root root normal mehpc mehpc normal mehpc dj mehpc normal mehpc dj1 mehpc normal [root@ood-master ~]# sacct -X -u dj1 -S 2024-04-12 -E 2024-04-15 -o jobid,ncpus,elapsedraw,cputimeraw JobID NCPUS ElapsedRaw CPUTimeRAW ------------ ---------- ---------- ---------- 4 1 60 60 5 2 120 240 6 1 61 61 8 2 120 240 9 0 0 0 [root@ood-master ~]# sacct -X -u dj -S 2024-04-12 -E 2024-04-15 -o jobid,ncpus,elapsedraw,cputimeraw JobID NCPUS ElapsedRaw CPUTimeRAW ------------ ---------- ---------- ---------- 7 2 120 240 [root@ood-master ~]# sreport job SizesByAccount Users=dj1 start=2024-04-12 end=2024-04-15 -------------------------------------------------------------------------------- Job Sizes 2024-04-12T00:00:00 - 2024-04-14T23:59:59 (259200 secs) Time reported in Minutes -------------------------------------------------------------------------------- Cluster Account 0-49 CPUs 50-249 CPUs 250-499 CPUs 500-999 CPUs >= 1000 CPUs % of cluster --------- --------- ------------- ------------- ------------- ------------- ------------- ------------ mehpc root 10 0 0 0 0 100.00% [root@ood-master ~]# sreport job SizesByAccount Users=dj start=2024-04-12 end=2024-04-15 -------------------------------------------------------------------------------- Job Sizes 2024-04-12T00:00:00 - 2024-04-14T23:59:59 (259200 secs) Time reported in Minutes -------------------------------------------------------------------------------- Cluster Account 0-49 CPUs 50-249 CPUs 250-499 CPUs 500-999 CPUs >= 1000 CPUs % of cluster --------- --------- ------------- ------------- ------------- ------------- ------------- ------------ mehpc root 4 0 0 0 0 100.00% [root@ood-master ~]# sreport Cluster UserUtilizationByAccount user=dj1 start=2024-04-12 end=2024-04-15 -------------------------------------------------------------------------------- Cluster/User/Account Utilization 2024-04-12T00:00:00 - 2024-04-14T23:59:59 (259200 secs) Usage reported in CPU Minutes -------------------------------------------------------------------------------- Cluster Login Proper Name Account Used Energy --------- --------- --------------- --------------- -------- -------- mehpc dj1 dj1 dj1 mehpc 14 0 [root@ood-master ~]# sreport Cluster UserUtilizationByAccount user=dj start=2024-04-12 end=2024-04-15 -------------------------------------------------------------------------------- Cluster/User/Account Utilization 2024-04-12T00:00:00 - 2024-04-14T23:59:59 (259200 secs) Usage reported in CPU Minutes -------------------------------------------------------------------------------- Cluster Login Proper Name Account Used Energy --------- --------- --------------- --------------- -------- -------- mehpc dj dj dj mehpc 14 0 [root@ood-master ~]# sacct -u dj1 -S 2024-04-12 -E 2024-04-15 -o "jobid,partition,account,user,alloccpus,cputimeraw,state,workdir%60" -X |awk 'BEGIN{total=0}{total+=$6}END{print total}' 601 [root@ood-master ~]# sacct -u dj -S 2024-04-12 -E 2024-04-15 -o "jobid,partition,account,user,alloccpus,cputimeraw,state,workdir%60" -X |awk 'BEGIN{total=0}{total+=$6}END{print total}' 240 [root@ood-master ~]# sreport cluster AccountUtilizationByUser accounts=mehpc start=2024-01-01 end=2024-04-15 -------------------------------------------------------------------------------- Cluster/Account/User Utilization 2024-01-01T00:00:00 - 2024-04-14T23:59:59 (9072000 secs) Usage reported in CPU Minutes -------------------------------------------------------------------------------- Cluster Account Login Proper Name Used Energy --------- --------------- --------- --------------- -------- -------- mehpc mehpc 14 0 mehpc mehpc dj dj dj 14 0 mehpc mehpc dj1 dj1 dj1 14 0

1 0

Slurm.conf and workers
by Xaver Stiensmeier 15 Apr '24

15 Apr '24

Dear slurm-user list, as far as I understood it, the slurm.conf needs to be present on the master and on the workers at slurm.conf (if no other path is set via SLURM_CONF). However, I noticed that when adding a partition only in the master's slurm.conf, all workers were able to "correctly" show the added partition when calling sinfo on them. Is the stored slurm.conf on every instance just a fallback for when connection is down or what is the purpose? The documentation only says: "This file should be consistent across all nodes in the cluster." Best regards, Xaver

2 1

Interfaces of topology/tree and Topology Awareness
by nico.derl＠tutanota.com 15 Apr '24

15 Apr '24

Hello everyone, I'm trying to improve topology awareness in a local Slurm-managed HPC system. It's using the default hierarchical 3-level topology with the tree-plugin. It however does not always confine jobs to the most tightly packed group of nodes, seems to over-provision switches for smaller jobs, and gets slow or overwhelmed with jobs that have a high node count. I'd like to implement something more literally aligned with best-fit, but I'm having trouble understanding the relevant interfaces to hook into the topology model of Slurm. I would like a high-level explanation of how the tree- and common topology components work, how they integrate into the higher scheduling logic and what the internal topology model looks like. Or some pointers to relevant docs discussing this. I have read the topology guide and its dev-doc, which does note some of the caveats I mentioned. It however only talks about providing a set of weights to the upper logic levels in the form of a node ranking. I can't see how this ranking resembles the topology and how it's being used. From looking at the signatures and C-code I can tell this much: topology-tree consumes the topology.conf and generates a ranking of some kind that is passed to topology-common. topology-common consumes a ranking and uses its own gres-sched to figure out what nodes can fit a job (possibly pulling info from the gres-select-plugin to determine node capabilities). It's then supposed to apply a best-fit algorithm to efficiently fill up vacant cluster-capacity, but I can't manage to follow this part in the code as everything crumbles into separate files that I can't link correctly in my head. Thanks in advance. referenced docs: <https://slurm.schedmd.com/topology.html> <https://hpc.rz.rptu.de/documentation/topology_plugin.html> <https://github.com/SchedMD/slurm/tree/master/src/plugins/topology/common>

2 1

Re: slurmrestd connect to 192.168.87.113:6819 Connection refused
by shaobo liu 15 Apr '24

15 Apr '24

Thanks, The reason was found. It was caused by the expiration of the rest api token. <nico.derl(a)tutanota.com> 于2024年4月12日周五 22:56写道： > If you say DBd isn't using 6819, in the sense that you selected a > different port, make sure the dbdport directive reflects that in both > slurmdbd.conf and AccountingStoragePort in slurm.conf. > It must be getting the 6819 from somewhere. > > > 12. Apr. 2024, 16:05 von dspam.liu(a)gmail.com: > > slurmctrld and rest are on the same machine, No firewall. secondary > slurmdbd is background mode, slurmdbd does not listen on port 6819. > > OS: ubuntu 20.04 > SLURM: 23.11.0 > > <nico.derl(a)tutanota.com> 于2024年4月12日周五 20:18写道： > > Hey, > Are slurmctrld and restd on separate machines? Can you manually reach > them? Could there be a firewall/closed port in the way? > > > 12. Apr. 2024, 11:36 von slurm-users(a)lists.schedmd.com: > > hi，slurm configured primary and secondary，The error when requesting > slurmrest api is as follows, may I ask what is the reason? > > # scontrol ping > Slurmctld(primary) at node003 is UP > Slurmctld(backup) at node113 is UP > > > # systemctl status slurmrestd.service > ● slurmrestd.service - Slurm REST daemon > Loaded: loaded (/lib/systemd/system/slurmrestd.service; enabled; > vendor preset: enabled) > Active: active (running) since Fri 2024-04-12 17:07:08 CST; 21min ago > Main PID: 705425 (slurmrestd) > Tasks: 21 (limit: 629145) > Memory: 20.3M > CGroup: /system.slice/slurmrestd.service > └─705425 /usr/sbin/slurmrestd -f /etc/slurm/slurm.conf > unix:/var/spool/slurm/slurmrestd.socket 0.0.0.0:6820 -vvv > > Apr 12 17:08:46 node003 slurmrestd[705425]: debug2: _slurm_connect: failed > to connect to 192.168.87.113:6819: Connection refused > Apr 12 17:08:46 node003 slurmrestd[705425]: debug2: Error connecting slurm > stream socket at 192.168.87.113:6819: Connection refused > Apr 12 17:08:46 node003 slurmrestd[705425]: slurmrestd: error: > slurm_persist_conn_open_without_init: failed to open persistent connection > to host:node113:6819: Connection refused > Apr 12 17:08:46 node003 slurmrestd[705425]: slurmrestd: error: Sending > PersistInit msg: Connection refused > Apr 12 17:08:46 node003 slurmrestd[705425]: slurmrestd: error: > slurm_rest_auth_p_get_db_conn: unable to connect to slurmdbd: Connection > refused > Apr 12 17:08:46 node003 slurmrestd[705425]: slurmrestd: error: > init_connection[v0.0.39]:[[2.0.1.191]:50652] rc[7000]=Unable to connect to > database -> openapi_get_db_conn() failed to open slurmdb connecti> > Apr 12 17:08:46 node003 slurmrestd[705425]: error: > slurm_persist_conn_open_without_init: failed to open persistent connection > to host:node113:6819: Connection refused > Apr 12 17:08:46 node003 slurmrestd[705425]: error: Sending PersistInit > msg: Connection refused > Apr 12 17:08:46 node003 slurmrestd[705425]: error: > slurm_rest_auth_p_get_db_conn: unable to connect to slurmdbd: Connection > refused > Apr 12 17:08:46 node003 slurmrestd[705425]: error: > init_connection[v0.0.39]:[[2.0.1.191]:50652] rc[7000]=Unable to connect to > database -> openapi_get_db_conn() failed to open slurmdb connection > > > >

1 0

slurmrestd connect to 192.168.87.113:6819 Connection refused
by shaobo liu 12 Apr '24

12 Apr '24

hi，slurm configured primary and secondary，The error when requesting slurmrest api is as follows, may I ask what is the reason? # scontrol ping Slurmctld(primary) at node003 is UP Slurmctld(backup) at node113 is UP # systemctl status slurmrestd.service ● slurmrestd.service - Slurm REST daemon Loaded: loaded (/lib/systemd/system/slurmrestd.service; enabled; vendor preset: enabled) Active: active (running) since Fri 2024-04-12 17:07:08 CST; 21min ago Main PID: 705425 (slurmrestd) Tasks: 21 (limit: 629145) Memory: 20.3M CGroup: /system.slice/slurmrestd.service └─705425 /usr/sbin/slurmrestd -f /etc/slurm/slurm.conf unix:/var/spool/slurm/slurmrestd.socket 0.0.0.0:6820 -vvv Apr 12 17:08:46 node003 slurmrestd[705425]: debug2: _slurm_connect: failed to connect to 192.168.87.113:6819: Connection refused Apr 12 17:08:46 node003 slurmrestd[705425]: debug2: Error connecting slurm stream socket at 192.168.87.113:6819: Connection refused Apr 12 17:08:46 node003 slurmrestd[705425]: slurmrestd: error: slurm_persist_conn_open_without_init: failed to open persistent connection to host:node113:6819: Connection refused Apr 12 17:08:46 node003 slurmrestd[705425]: slurmrestd: error: Sending PersistInit msg: Connection refused Apr 12 17:08:46 node003 slurmrestd[705425]: slurmrestd: error: slurm_rest_auth_p_get_db_conn: unable to connect to slurmdbd: Connection refused Apr 12 17:08:46 node003 slurmrestd[705425]: slurmrestd: error: init_connection[v0.0.39]:[[2.0.1.191]:50652] rc[7000]=Unable to connect to database -> openapi_get_db_conn() failed to open slurmdb connecti> Apr 12 17:08:46 node003 slurmrestd[705425]: error: slurm_persist_conn_open_without_init: failed to open persistent connection to host:node113:6819: Connection refused Apr 12 17:08:46 node003 slurmrestd[705425]: error: Sending PersistInit msg: Connection refused Apr 12 17:08:46 node003 slurmrestd[705425]: error: slurm_rest_auth_p_get_db_conn: unable to connect to slurmdbd: Connection refused Apr 12 17:08:46 node003 slurmrestd[705425]: error: init_connection[v0.0.39]:[[2.0.1.191]:50652] rc[7000]=Unable to connect to database -> openapi_get_db_conn() failed to open slurmdb connection

2 1

visualisation of JobComp and JobacctGather data with Grafana - screenshots, ideas?
by Josef Dvoracek 12 Apr '24

12 Apr '24

Is here anybody having nice visualization of JobComp and JobacctGather data in Grafana? I save JobComp data in Elasticsearch, JobacctGather data in influxDB, and thinking about how to provide meaningful insights to $users. Things I'd like to show..: especially memory & cpu utilization, job result, possible malicious effects like OOMs... Any screenshots, ideas, experience welcomed! cheers Josef

2 1

Re: Slurmd enabled crash with CgroupV2
by Josef Dvoracek 11 Apr '24

11 Apr '24

I observe same behavior on slurm 23.11.5 Rocky Linux8.9.. > [root@compute ~]# cat /sys/fs/cgroup/cgroup.subtree_control > memory pids > [root@compute ~]# systemctl disable slurmd > Removed /etc/systemd/system/multi-user.target.wants/slurmd.service. > [root@compute ~]# cat /sys/fs/cgroup/cgroup.subtree_control > cpuset cpu io memory pids > [root@compute ~]# systemctl enable slurmd > Created symlink /etc/systemd/system/multi-user.target.wants/slurmd.service → /usr/lib/systemd/system/slurmd.service. > [root@compute ~]# cat /sys/fs/cgroup/cgroup.subtree_control > cpuset cpu io memory pids over time (i see this thread is ~1 year old, is here better / new understanding of this? cheers josef On 23. 05. 23 12:46, Alan Orth wrote: > I notice the exact same behavior as Tristan. My CentOS Stream 8 system > is in full unified cgroupv2 mode, the slurmd.service has a > "Delegate=Yes" override added to it, and all cgroup stuff is added to > slurm.conf and cgroup.conf, yet slurmd does not start after reboot. I > don't understand what is happening, but I see the exact same behavior > regarding the cgroup subtree_control with disabling / re-enabling slurmd. >

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Jobs of a user are stuck in Completing stage for a long time and cannot cancel them
by archisman.pathak＠nksecurities.com 11 Apr '24

11 Apr '24

We are running a slurm cluster with version `slurm 22.05.8`. One of our users has reported that their jobs have been stuck at the completion stage for a long time. Referring to Slurm Workload Manager - Slurm Troubleshooting Guide we found that indeed the batchhost for the job was removed from the cluster, perhaps without draining it first. How do we cancel/delete the jobs ? * We tried scancel on the batch and individual job ids from both the user and from SlurmUser

4 5

Upgrading nodes
by Steve Berg 11 Apr '24

11 Apr '24

I just finished migrating a few dozen blade servers from torque to slurm. They're all running Alma 8 currently with the slurm that is available from epel. I do want to get it all upgraded to running Alma 9 and the current version of slurm. Got one system set up as the slurmctld system running Alma 9. I grabbed the tar ball and built RPMs for 9.x. Got a few questions about the best path to proceed. Can I use the Alma 9 system to build rpms for Alma 8? I'm sure I can rig up an 8 system to build rpms on but thought I'd see if there was a way to do it on the one 9 system. My plan will be to get the rpms built for 8 and 9, update the slurmctld system to the latest version of slurm, then update all the nodes to the current slurmd version. Once that's done I should be able to reinstall individual nodes to Alma 9 and the same version of slurmd. Am I missing anything in that sequence? I'm fairly confident that the users aren't running any code that will notice the difference between a node running 8 or 9, that should be transparent to them. -- //- Fixer of that which is broke -// //- Home = sberg(a)mississippi.com -// //- Sinners can repent, but stupid is forever. -//

2 1

single node configuration
by Alison Peterson 10 Apr '24

10 Apr '24

Hi everyone, I'm conducting some tests. I've just set up SLURM on the head node and haven't added any compute nodes yet. I'm trying to test it to ensure it's working, but I'm encountering an error: 'Nodes required for the job are DOWN, DRAINED, or reserved for jobs in higher priority partitions. *[stsadmin@head ~]$ squeue* JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 6 lab test_slu stsadmin PD 0:00 1 (Nodes required for job are DOWN, DRAINED or reserved for jobs in higher priority partitions) *[stsadmin@head ~]$ scontrol show job 6* JobId=6 JobName=test_slurm UserId=stsadmin(1000) GroupId=stsadmin(1000) MCS_label=N/A Priority=1 Nice=0 Account=(null) QOS=normal JobState=PENDING Reason=Nodes_required_for_job_are_DOWN,_DRAINED_or_reserved_for_jobs_in_higher_priority_partitions Dependency=(null) Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0 RunTime=00:00:00 TimeLimit=01:00:00 TimeMin=N/A SubmitTime=2024-04-09T10:43:14 EligibleTime=2024-04-09T10:43:14 AccrueTime=2024-04-09T10:43:14 StartTime=Unknown EndTime=Unknown Deadline=N/A SuspendTime=None SecsPreSuspend=0 LastSchedEval=2024-04-09T10:43:23 Scheduler=Backfill:* Partition=lab AllocNode:Sid=head:5147 ReqNodeList=(null) ExcNodeList=(null) NodeList= NumNodes=1-1 NumCPUs=1 NumTasks=1 CPUs/Task=1 ReqB:S:C:T=0:0:*:* ReqTRES=cpu=1,mem=1G,node=1,billing=1 AllocTRES=(null) Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=* MinCPUsNode=1 MinMemoryCPU=1G MinTmpDiskNode=0 Features=(null) DelayBoot=00:00:00 OverSubscribe=YES Contiguous=0 Licenses=(null) Network=(null) Command=/home/stsadmin/Downloads/test.sh WorkDir=/home/stsadmin StdErr=/home/stsadmin/test_slurm_output.txt StdIn=/dev/null StdOut=/home/stsadmin/test_slurm_output.txt Power= *[stsadmin@head ~]$ scontrol show node head* NodeName=head CoresPerSocket=6 CPUAlloc=0 CPUEfctv=24 CPUTot=24 CPULoad=0.00 AvailableFeatures=(null) ActiveFeatures=(null) Gres=(null) NodeAddr=head NodeHostName=head RealMemory=184000 AllocMem=0 FreeMem=N/A Sockets=2 Boards=1 State=DOWN+NOT_RESPONDING ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A Partitions=lab BootTime=None SlurmdStartTime=None LastBusyTime=2024-04-09T10:42:53 ResumeAfterTime=None CfgTRES=cpu=24,mem=184000M,billing=24 AllocTRES= CapWatts=n/a CurrentWatts=0 AveWatts=0 ExtSensorsJoules=n/a ExtSensorsWatts=0 ExtSensorsTemp=n/a Reason=Not responding [slurm@2024-04-09T10:14:10] I will take any advice to guide me in the proper direction, thank you! -- *Alison Peterson* IT Research Support Analyst *Information Technology* apeterson5(a)sdsu.edu <mfarley(a)sdsu.edu> O: 619-594-3364 *San Diego State University | SDSU.edu <http://sdsu.edu/>* 5500 Campanile Drive | San Diego, CA 92182-8080

2 1

Avoiding fragmentation
by Gerhard Strangar 10 Apr '24

10 Apr '24

Hi, I'm trying to figure out how to deal with a mix of few- and many-cpu jobs. By that I mean most jobs use 128 cpus, but sometimes there are jobs with only 16. As soon as that job with only 16 is running, the scheduler splits the next 128 cpu jobs into 96+16 each, instead of assigning a full 128 cpu node to them. Is there a way for the administrator to achieve preferring full nodes? The existence of pack_serial_at_end makes me believe there is not, because that basically is what I needed, apart from my serial jobs using 16 cpus instead of 1. Gerhard

6 5

Nodes required for job are down, drained or reserved
by Alison Peterson 09 Apr '24

09 Apr '24

Hi everyone, I'm conducting some tests. I've just set up SLURM on the head node and haven't added any compute nodes yet. I'm trying to test it to ensure it's working, but I'm encountering an error: 'Nodes required for the job are DOWN, DRAINED, or reserved for jobs in higher priority partitions. Any guidance will be appreciated thank you! -- *Alison Peterson* IT Research Support Analyst *Information Technology* apeterson5(a)sdsu.edu <mfarley(a)sdsu.edu> O: 619-594-3364 *San Diego State University | SDSU.edu <http://sdsu.edu/>* 5500 Campanile Drive | San Diego, CA 92182-8080

2 9

DefCpuPerGPU and multiple partitions
by Ansgar Esztermann-Kirchner 09 Apr '24

09 Apr '24

Hello List, does anyone have experience with DefCpuPerGPU and jobs requesting multiple partitions? I would expect Slurm to select a partition from those requested by the job, then assign CPUs based on that partition's DefCpuPerGPU. But according to my observations, it appears that (at least sometimes), DefCpuPerGPU from another partition (also requested by the job, but not selected to execute it) is used to assign CPUs. I haven't done extensive testing to determine whether there's a pattern to this (e.g. always the first partition is used to calculate the number of CPUs), but I've found a bug report that seems to describe what I'm seeing: https://bugs.schedmd.com/show_bug.cgi?id=15238 Unfortunately, it appears to have been closed as invalid really quickly. I'm on 23.02.5. Best, A. -- Ansgar Esztermann Sysadmin Dep. Theoretical and Computational Biophysics https://www.mpinat.mpg.de/person/11315/3883774

1 0

Elastic Computing: Is it possible to incentivize grouping power_up calls?
by Xaver Stiensmeier 09 Apr '24

09 Apr '24

Dear slurm user list, we make use of elastic cloud computing i.e. node instances are created on demand and are destroyed when they are not used for a certain amount of time. Created instances are set up via Ansible. If more than one instance is requested at the exact same time, Slurm will pass those into the resume script together and one Ansible call will handle all those instances. However, more often than not workflows will request multiple instances within the same second, but not at the exact same time. This leads to multiple resume script calls and therefore to multiple Ansible calls. This will lead to less clear log files, greater CPU consumption by the multiple running Ansible calls and so on. What I am looking for is an option to force Slurm to wait a certain amount and then perform a single resume call for all instances within that time frame (let's say 1 second). Is this somehow possible? Best regards, Xaver

2 2

Slurm User Group 2024 Call for Papers
by Victoria Hobson 08 Apr '24

08 Apr '24

Slurm User Group (SLUG) 2024 is set for September 12-13 at the University of Oslo in Oslo, Norway. Registration information and a high-level schedule can be found here: https://slug24.splashthat.com/ We invite all interested attendees to submit a presentation abstract to be given at SLUG. Presentation content can be in the form of a tutorial, technical presentation or site report. SLUG 2024 is sponsored and organized by the University of Oslo and SchedMD. This international event is open to those who want to: Learn more about Slurm, a highly scalable resource manager and job scheduler - Share their knowledge and experience with other users and administrators - Get detailed information about the latest features and developments - Share requirements and discuss future developments Everyone who wants to present their own usage, developments, site report, or tutorial about Slurm is invited to submit abstract details here: https://forms.gle/N7bFo5EzwuTuKkBN7 Abstracts are due Friday, May 31st and notifications of acceptance will go out by Friday, June 14th. -- Victoria Hobson SchedMD LLC Vice President of Marketing

1 0

How to reinstall / reconfigure Slurm?
by Shooktija S N 08 Apr '24

08 Apr '24

Hi, I am setting up Slurm on our lab's 3 node cluster and I have run into a problem while adding GPUs (each node has an NVIDIA 4070 ti) as a GRES. There is an error at the 'debug' log level in slurmd.log that says that the GPU is file-less and is being removed from the final GRES list. This error according to some older posts on this forum might be fixed by reinstalling / reconfiguring Slurm with the right flag (the '--with-nvml' flag according to this <https://groups.google.com/g/slurm-users/c/cvGb4JnK8BY> post). Line in /var/log/slurmd.log: [2024-04-03T15:42:02.695] debug: Removing file-less GPU gpu:rtx4070 from final GRES list Does this error require me to either reinstall / reconfigure Slurm? What does 'reconfigure Slurm' mean? I'm about as clueless as a caveman with a smartphone when it comes to Slurm administration and Linux system administration in general. So, if you could, please explain it to me as simply as possible. slurm.conf without comment lines: ClusterName=DlabCluster SlurmctldHost=server1 GresTypes=gpu ProctrackType=proctrack/linuxproc ReturnToService=1 SlurmctldPidFile=/var/run/slurmctld.pid SlurmctldPort=6817 SlurmdPidFile=/var/run/slurmd.pid SlurmdPort=6818 SlurmdSpoolDir=/var/spool/slurmd SlurmUser=root StateSaveLocation=/var/spool/slurmctld TaskPlugin=task/affinity,task/cgroup InactiveLimit=0 KillWait=30 MinJobAge=300 SlurmctldTimeout=120 SlurmdTimeout=300 Waittime=0 SchedulerType=sched/backfill SelectType=select/cons_tres JobCompType=jobcomp/none JobAcctGatherFrequency=30 SlurmctldDebug=debug2 SlurmctldLogFile=/var/log/slurmctld.log SlurmdDebug=debug2 SlurmdLogFile=/var/log/slurmd.log NodeName=server[1-3] RealMemory=128636 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 State=UNKNOWN Gres=gpu:rtx4070:1 PartitionName=mainPartition Nodes=ALL Default=YES MaxTime=INFINITE State=UP gres.conf (only one line): AutoDetect=nvml While installing cuda, I know that nvml has been installed because of this line in /var/log/cuda-installer.log: [INFO]: Installing: cuda-nvml-dev Thanks! PS: I could've added this as a continuation to this post <https://groups.google.com/g/slurm-users/c/p68dkeUoMmA>, but for some reason I do not have permission to post to that group, so here I am starting a new thread.

2 3

No SLURM_SHARDS_ON_NODE in prolog/epilog
by Thomas Green - Staff in University IT, Research Technologies / Staff Technoleg Gwybodaeth, Technolegau Ymchwil 05 Apr '24

05 Apr '24

Hi, I have found that SLURM_SHARDS_ON_NODE is not an environment variable in prolog or epilog. Is ther ea reason for this? I thought it would be handy to know if shards have been requested in a prolog/epilog. Tom -- Thomas Green Senior Programmer ARCCA, Redwood Building, King Edward VII Avenue, Cardiff, CF10 3NB Tel: +44 (0)29 208 79269 Fax: +44 (0)29 208 70734 Email: greent10(a)cardiff.ac.uk<mailto:greent10@cardiff.ac.uk> Web: http://www.cardiff.ac.uk/arcca Thomas Green Uwch Raglennydd ARCCA, Adeilad Redwood, King Edward VII Avenue, Caerdydd, CF10 3NB Ffôn: +44 (0)29 208 79269 Ffacs: +44 (0)29 208 70734 E-bost: greent10(a)caerdydd.ac.uk<mailto:greent10@caerdydd.ac.uk> Gwefan: http://www.caerdydd.ac.uk/arcca

1 0

Suggestions for Partition/QoS configuration
by thomas.hartmann＠th-ht.de 04 Apr '24

04 Apr '24

Hi, we're testing possible slurm configurations on a test system right now. Eventually, it is going to serve ~1000 users. We're going to have some users who are going to run lots of short jobs (a couple of minutes to ~4h) and some users that run jobs that are going to run for days or weeks. I want to avoid a situation in which a group of users basically saturates the whole cluster with jobs that run for a week or two and nobody could run any short jobs anymore. I also would like to favor short jobs, because they make the whole cluster feel more dynamic and agile for everybody. On the other hand, I would like to make the most of the ressources, i.e. when nobody is sending short jobs, long jobs could run on all the nodes. My idea was to basically have three partitions: 1. PartitionName=short MaxTime=04:00:00 State=UP Nodes=node[01-99] PriorityTier=100 2. PartitionName=long_safe MaxTime=14-00:00:00 State=UP Nodes=node[01-50] PriorityTier=100 3. PartitionName=long_preempt MaxTime=14-00:00:00 State=UP Nodes=nodes[01-99] PriorityTier=40 PreemptMode=requeue and then use the JobSubmitPlugin "all_partitions" so that all jobs get submitted to all partitions by default. This way, a short job ends up in the `short` partition and is able to use all nodes. A long job ends up using the `long_safe` partition until for the first 50 nodes. These jobs are not going to be preempted. Remaining long jobs use the `long_preempt` queue. So they run on the remaining nodes as long as there are no higher prio short (or long) jobs in the queue. So, the cluster could be saturated with long running jobs but if short jobs are submitted and the user has a high enough fair share, some of the long jobs would get preempted and the short ones would run. This scenario works fine.... BUT the long jobs seem to be playing pingpong on the `long_preempt` partition because as soon as they run, they stop accruing AGE priority unlike still queued jobs. As soon as a queued job, albeit by the same user, "overtakes" a running one, it preempts the running one, stops accruing age and so on.... So, is there maybe a cleverer way to do this? Thanks a lot! Thomas

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Re: [EXT] Re: SLURM configuration help
by Renfro, Michael 04 Apr '24

04 Apr '24

Yep, from your scontrol show node output: CfgTRES=cpu=64,mem=2052077M,billing=64 AllocTRES=cpu=1,mem=2052077M The running job (77) has allocated 1 CPU and all the memory on the node. That’s probably due to the partition using the default DefMemPerCPU value [1], which is unlimited. Since all our nodes are shared, and our workloads vary widely, we set our DefMemPerCPU value to something considerably lower than mem_in_node/cores_in_node . That way, most jobs will leave some memory available by default, and other jobs can use that extra memory as long as CPUs are available. [1] https://slurm.schedmd.com/slurm.conf.html#OPT_DefMemPerCPU From: Alison Peterson <apeterson5(a)sdsu.edu> Date: Thursday, April 4, 2024 at 11:58 AM To: Renfro, Michael <Renfro(a)tntech.edu> Subject: Re: [EXT] Re: [slurm-users] SLURM configuration help External Email Warning This email originated from outside the university. Please use caution when opening attachments, clicking links, or responding to requests. ________________________________ Here is the info: sma@cusco:/data/work/sma-scratch/tohoku_wOcean$ scontrol show node cusco NodeName=cusco Arch=x86_64 CoresPerSocket=32 CPUAlloc=1 CPUTot=64 CPULoad=0.02 AvailableFeatures=(null) ActiveFeatures=(null) Gres=gpu:4 NodeAddr=cusco NodeHostName=cusco Version=19.05.5 OS=Linux 5.4.0-172-generic #190-Ubuntu SMP Fri Feb 2 23:24:22 UTC 2024 RealMemory=2052077 AllocMem=2052077 FreeMem=1995947 Sockets=2 Boards=1 State=MIXED ThreadsPerCore=1 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A Partitions=mainpart BootTime=2024-03-01T17:06:26 SlurmdStartTime=2024-03-01T17:06:53 CfgTRES=cpu=64,mem=2052077M,billing=64 AllocTRES=cpu=1,mem=2052077M CapWatts=n/a CurrentWatts=0 AveWatts=0 ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s sma@cusco:/data/work/sma-scratch/tohoku_wOcean$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 78 mainpart CF1090_w sma PD 0:00 1 (Resources) 77 mainpart CF0000_w sma R 0:26 1 cusco sma@cusco:/data/work/sma-scratch/tohoku_wOcean$ scontrol show job 78 JobId=78 JobName=CF1090_wOcean500m.shell UserId=sma(1008) GroupId=myfault(1001) MCS_label=N/A Priority=4294901720 Nice=0 Account=(null) QOS=(null) JobState=PENDING Reason=Resources Dependency=(null) Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0 RunTime=00:00:00 TimeLimit=01:00:00 TimeMin=N/A SubmitTime=2024-04-04T09:55:34 EligibleTime=2024-04-04T09:55:34 AccrueTime=2024-04-04T09:55:34 StartTime=2024-04-04T10:55:28 EndTime=2024-04-04T11:55:28 Deadline=N/A SuspendTime=None SecsPreSuspend=0 LastSchedEval=2024-04-04T09:55:58 Partition=mainpart AllocNode:Sid=newcusco:2450574 ReqNodeList=(null) ExcNodeList=(null) NodeList=(null) SchedNodeList=cusco NumNodes=1 NumCPUs=1 NumTasks=1 CPUs/Task=1 ReqB:S:C:T=0:0:*:* TRES=cpu=1,node=1,billing=1 Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=* MinCPUsNode=1 MinMemoryNode=0 MinTmpDiskNode=0 Features=(null) DelayBoot=00:00:00 OverSubscribe=YES Contiguous=0 Licenses=(null) Network=(null) Command=/data/work/sma-scratch/tohoku_wOcean/CF1090_wOcean500m.shell WorkDir=/data/work/sma-scratch/tohoku_wOcean StdErr=/data/work/sma-scratch/tohoku_wOcean/slurm-78.out StdIn=/dev/null StdOut=/data/work/sma-scratch/tohoku_wOcean/slurm-78.out Power= On Thu, Apr 4, 2024 at 8:57 AM Renfro, Michael <Renfro(a)tntech.edu<mailto:Renfro@tntech.edu>> wrote: What does “scontrol show node cusco” and “scontrol show job PENDING_JOB_ID” show? On one job we currently have that’s pending due to Resources, that job has requested 90 CPUs and 180 GB of memory as seen in its ReqTRES= value, but the node it wants to run on only has 37 CPUs available (seen by comparing its CfgTRES= and AllocTRES= values). From: Alison Peterson via slurm-users <slurm-users(a)lists.schedmd.com<mailto:slurm-users@lists.schedmd.com>> Date: Thursday, April 4, 2024 at 10:43 AM To: slurm-users(a)lists.schedmd.com<mailto:slurm-users@lists.schedmd.com> <slurm-users(a)lists.schedmd.com<mailto:slurm-users@lists.schedmd.com>> Subject: [slurm-users] SLURM configuration help External Email Warning This email originated from outside the university. Please use caution when opening attachments, clicking links, or responding to requests. ________________________________ I am writing to seek assistance with a critical issue on our single-node system managed by Slurm. Our jobs are queued and marked as awaiting resources, but they are not starting despite seeming availability. I'm new with SLURM and my only experience was a class on installing it so I have no experience, running it or using it. Issue Summary: Main Problem: Jobs submitted only one run and the second says NODELIST(REASON) (Resources). I've checked that our single node has enough RAM (2TB) and CPU's (64) available. # COMPUTE NODES NodeName=cusco CPUs=64 Sockets=2 CoresPerSocket=32 ThreadsPerCore=1 RealMemory=2052077 Gres=gpu:1,gpu:1,gpu:1,gpu:1 PartitionName=mainpart Default=YES MinNodes=1 DefaultTime=00:60:00 MaxTime=UNLIMITED AllowAccounts=ALL Nodes=ALL State=UP OverSubscribe=Force System Details: We have a single-node setup with Slurm as the workload manager. The node appears to have sufficient resources for the queued jobs. Troubleshooting Performed: Configuration Checks: I have verified all Slurm configurations and the system's resource availability, which should not be limiting job execution. Service Status: The Slurm daemon slurmdbd is active and running without any reported issues. System resource monitoring shows no shortages that would prevent job initiation. Any guidance and help will be deeply appreciated! -- Alison Peterson IT Research Support Analyst Information Technology apeterson5(a)sdsu.edu<mailto:mfarley@sdsu.edu> O: 619-594-3364 San Diego State University | SDSU.edu<http://sdsu.edu/> 5500 Campanile Drive | San Diego, CA 92182-8080 Error! Filename not specified. -- Alison Peterson IT Research Support Analyst Information Technology apeterson5(a)sdsu.edu<mailto:mfarley@sdsu.edu> O: 619-594-3364 San Diego State University | SDSU.edu<http://sdsu.edu/> 5500 Campanile Drive | San Diego, CA 92182-8080 [Image removed by sender.]

2 1

SLURM configuration help
by Alison Peterson 04 Apr '24

04 Apr '24

I am writing to seek assistance with a critical issue on our single-node system managed by Slurm. Our jobs are queued and marked as awaiting resources, but they are not starting despite seeming availability. I'm new with SLURM and my only experience was a class on installing it so I have no experience, running it or using it. Issue Summary: Main Problem: Jobs submitted only one run and the second says *NODELIST(REASON) (Resources*). I've checked that our single node has enough RAM (2TB) and CPU's (64) available. # COMPUTE NODES NodeName=cusco CPUs=64 Sockets=2 CoresPerSocket=32 ThreadsPerCore=1 RealMemory=2052077 Gres=gpu:1,gpu:1,gpu:1,gpu:1 PartitionName=mainpart Default=YES MinNodes=1 DefaultTime=00:60:00 MaxTime=UNLIMITED AllowAccounts=ALL Nodes=ALL State=UP OverSubscribe=Force System Details: We have a single-node setup with Slurm as the workload manager. The node appears to have sufficient resources for the queued jobs. Troubleshooting Performed: Configuration Checks: I have verified all Slurm configurations and the system's resource availability, which should not be limiting job execution. Service Status: The Slurm daemon slurmdbd is active and running without any reported issues. System resource monitoring shows no shortages that would prevent job initiation. Any guidance and help will be deeply appreciated! -- *Alison Peterson* IT Research Support Analyst *Information Technology* apeterson5(a)sdsu.edu <mfarley(a)sdsu.edu> O: 619-594-3364 *San Diego State University | SDSU.edu <http://sdsu.edu/>* 5500 Campanile Drive | San Diego, CA 92182-8080

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Slurm 23.11 - Unknown system variable 'wsrep_on'
by Russell Jones 04 Apr '24

04 Apr '24

Hi all, I am working on upgrading a Slurm cluster from 20 -> 23. I was successfully able to upgrade to 22, however now that I am trying to go from 22 to 23, starting slurmdbd results in the following error being logged: error: mysql_query failed: 1193 Unknown system variable 'wsrep_on' When trying to start slurmctld, I get: [2024-04-02T15:09:52.439] Couldn't find tres gres/gpumem in the database, creating. [2024-04-02T15:09:52.439] Couldn't find tres gres/gpuutil in the database, creating. [2024-04-02T15:09:52.440] fatal: Problem adding tres to the database, can't continue until database is able to make new tres Any ideas what could be causing these errors? Is MariaDB 5.5 still officially supported?

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scrun: Failed to run the container due to GID mapping configuration
by Toshiki Sonoda (Fujitsu) 04 Apr '24

04 Apr '24

Dear All, We set up scrun (slurm 23.11.5) integrated with rootless podman, referring to the official documentation. https://slurm.schedmd.com/containers.html#podman-scrun https://slurm.schedmd.com/scrun.html#SECTION_Example-%3CB%3Escrun.lua%3C/B%… However, runc/crun prints the error message and fails to run the container. Looking at the error message from runc, it seems that there is a problem with the GID mapping configuration. - runc [hoge@slm-master hoge]$ podman run centos echo "Hello World" time="2024-04-03T02:39:36Z" level=error msg="runc run failed: cannot specify gid=5 mount option for rootless container: User namespaces enabled, but no group mapping found." srun: error: slm-worker: task 0: Exited with exit code 1 - crun [hoge@slm-master hoge]$ podman run centos echo "Hello World" srun: error: slm-worker: task 0: Exited with exit code 1 mount `devpts` to `dev/pts`: Invalid argument When we removed the process of linux.gidMappings configuration from scrun.lua, runc failed to run the container due to the different error, but crun succeeded. - scrun.lua (The process of linux.gidMappings configuration) ... snip ... -- Provide default group map as root if one not provided -- mappings fail with build??? if (true or config["linux"]["gidMappings"] == nil) then config["linux"]["gidMappings"] = {{containerID=process_group_id, hostID=math.floor(group_id), size=1}} end ... snip ... - runc [hoge@slm-master hoge]$ podman run centos echo "Hello World" time="2024-04-03T02:37:18Z" level=error msg="runc run failed: User namespaces enabled, but no gid mappings found." srun: error: slm-worker: task 0: Exited with exit code 1 - crun [hoge@slm-master hoge]$ podman run centos echo "Hello World" Hello World Therefore, we would like to know the following two questions. - The reason for the failure to run the container - Required or recommended configuration to run the container Best Regards, Toshiki Sonoda

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File-less NVIDIA GeForce 4070 Ti being removed from GRES list
by Shooktija S N 02 Apr '24

02 Apr '24

Hi, I am trying to set up Slurm (version 22.05) on a 3 node cluster each having an NVIDIA GeForce RTX 4070 Ti GPU. I tried to follow along with the GRES setup tutorial on the Schedmd website and added the following (Gres=gpu:RTX4070TI:1) to the Node configuration in /etc/slurm/slurm.conf: NodeName=server[1-3] RealMemory=128636 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 State=UNKNOWN Gres=gpu:RTX4070TI:1 I do not have a gres.conf. However, I see this line at the debug log level in /var/log/slurmd.log: [2024-04-02T15:57:19.022] debug: Removing file-less GPU gpu:RTX4070TI from final GRES list What other configs are necessary for Slurm to work with my GPU? More information: OS: Proxmox VE 8.1.4 Kernel: 6.5.13 CPU: AMD EPYC 7662 Memory: 128636MiB /etc/slurm/slurm.conf that's shared by all the 3 nodes without the comment lines: ClusterName=DlabCluster SlurmctldHost=server1 GresTypes=gpu ProctrackType=proctrack/linuxproc ReturnToService=1 SlurmctldPidFile=/var/run/slurmctld.pid SlurmctldPort=6817 SlurmdPidFile=/var/run/slurmd.pid SlurmdPort=6818 SlurmdSpoolDir=/var/spool/slurmd SlurmUser=root StateSaveLocation=/var/spool/slurmctld TaskPlugin=task/affinity,task/cgroup InactiveLimit=0 KillWait=30 MinJobAge=300 SlurmctldTimeout=120 SlurmdTimeout=300 Waittime=0 SchedulerType=sched/backfill SelectType=select/cons_tres JobCompType=jobcomp/none JobAcctGatherFrequency=30 SlurmctldDebug=debug SlurmctldLogFile=/var/log/slurmctld.log SlurmdDebug=debug SlurmdLogFile=/var/log/slurmd.log NodeName=server[1-3] RealMemory=128636 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 State=UNKNOWN Gres=gpu:RTX4070TI:1 PartitionName=mainPartition Nodes=ALL Default=YES MaxTime=INFINITE State=UP

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