October 2026 - slurm-users - lists.schedmd.com

Making easy and simple security updates of Slurm compute nodes
by Ole Holm Nielsen 15 Oct '26

15 Oct '26

Dear Slurm users, Keeping Slurm compute nodes up-to-date with security updates is an important task, and since security updates are now released with a high frequency, it is desirable to make the updating process as simple and automatic as possible. The tools documented here in [1] offer a possible solution. It's enlightening to browse OS errata pages such as [2] or [3]. The design used in the present approach [1] is: * Configure all compute nodes to execute a crontab job script at every reboot. This script will usually do nothing, except in case the node has a Slurm "State=down" where it will perform the configured updates. * Making updates simply consists of rebooting compute node lists with the command "scontrol reboot asap nextstate=down <node-list>". Here we have employed (abused?) the Slurm "State=down" as a *trigger* telling the update script to perform some actions. Nodes with a "Reason=Node unexpectedly rebooted" are assumed to be unrelated to the present update system and will therefore be skipped. The page [1] gives details of configuration and the optional handling of mixed-state nodes. We have tested these tools on our 700-node cluster running RockyLinux 8 and 9 for some time now, and they work reliably in our environment when updating several times per week. I would welcome any comments or bug reports regarding these tools. Best regards, Ole [1] https://github.com/OleHolmNielsen/Slurm_tools/tree/master/nodes#compute-nod… [2] RockyLinux errata: https://errata.build.resf.org/ [3] AlmaLinux errata: https://errata.almalinux.org/ -- Ole Holm Nielsen PhD, Senior HPC Officer Department of Physics, Technical University of Denmark

1 0

Global AI in Drug Discovery Market Trends and Future Forecast Report
by abhaybhardwaj282＠gmail.com 15 Oct '26

15 Oct '26

The latest report published by Roots Analysis, titled Global AI in Drug Discovery Market offers a detailed study of the industry size, growth scenarios, and fundamental parameters of the market analysis. In this research report, the author provided an in-depth analysis of market dynamics, including key drivers, restraints, opportunities, and major threats that may impact growth during the upcoming years. Our market analysis report provides a brief discussion on leading market players, competition landscape, regional dominance, supply-demand ratio, business strategies, and future investment opportunities in the overall industry. This report provides a complete prototype of the estimated growth rate, market value, product types, applications, and type of end users active in this industry. What are the Report Highlights? The AI in Drug Discovery industry analysis report offers a comprehensive overview of the Market and its major growth prospects such as drivers, restraints, challenges, and upcoming opportunities. It also throws light on the current industry trends, major developments, and other interesting insights useful for this market. It further discusses the nature of the market competition, list of players and their key strategies to stay put in the competition. It also describes the table of segmentation and the names of the leading segment with market figures. The market report is provided on the company website. Major Players in AI in Drug Discovery market: Aiforia Technologies, Atomwise, BioSyntagma, Chemalive, Collaborations Pharmaceuticals, Cyclica, DeepMatter, Exscientia, InveniAI, MAbSilico, Optibrium, Recursion Pharmaceuticals, Sensyne Health and Valo Health. Leading market players and manufacturers are studied to help give a brief idea about them in the AI in Drug Discovery market report. The challenges faced by them and the reasons they are on that position is explained to help make a well informed decision. The competitive landscape of AI in Drug Discovery market is given presenting detailed insights into the company profiles, developments, mergers, acquisitions, economic status and best SWOT analysis. AI in Drug Discovery Market Segmentation: AI in Drug Discovery Market (2nd Edition): Industry Trends and Global Forecasts, till 2035 - Distribution by Drug Discovery Steps, Therapeutic Area and Key Geographies (North America, Europe, Asia-Pacific, Latin America, MENA, and RoW) Browse Complete Report @ https://www.rootsanalysis.com/reports/ai-based-drug-discovery-market.html

1 0

Fermenters and Bioreactors Market
by nidhithankur987＠gmail.com 15 Oct '26

15 Oct '26

Market Size & Growth Projections Market Valuation: The global industrial robotics market is projected to grow from USD 22 billion in 2026 to USD 35 billion by 2030, and reach USD 101 billion by 2040. Compound Annual Growth Rate (CAGR): The market is anticipated to expand at a robust 11.45% CAGR from 2026 through 2040, driven by the acceleration of Industry 4.0, smart factory automation, and rising global demand for consumer goods and automobiles. Core Market Segments & Share Insights By Type of Robot: Traditional Industrial Robots: Dominate the market with approximately 85.77% revenue share in 2026, valued for their high payload capacity, unmatched precision, and speed in heavy-duty sectors like automotive and metal fabrication. Collaborative Robots (Cobots): Represent the fastest-growing strategic segment, projected to expand at a 14.85% CAGR through 2040, fueled by labor shortages and adoption in SMEs, healthcare, logistics, and electronics. By Payload Capacity: Low Payloads (<20 Kgs): Lead the market with 54.20% revenue share in 2026, heavily deployed in electronics, pharmaceuticals, cosmetics, and semiconductor manufacturing. Medium Payloads (20 to 150 Kgs): Projected to register a fast 11.2% CAGR, ideal for mid-range manufacturing, welding, painting, and flexible assembly. By Mobility: Stationary Robots: Capture around 65% of the market share in 2026, essential for fixed, high-volume tasks such as welding and assembly. Mobile Robots: Represent the highest-growth mobility segment, expected to grow at a 16.5% CAGR, driven by warehouse automation, e-commerce fulfillment, and intra-factory logistics. By Application Area: Handling Operations: Currently lead the application sector, accounting for 42.0% of the market in 2026, supported by widespread compatibility and e-commerce logistics demands. Dispensing: Expected to see accelerated growth at a 12% CAGR, driven by precision material application needs across food, beverage, and agricultural industries. Key End-User Industries: Automotive Industry, Electronics Industry, Metal and Machinery Industry, Plastics Industry, and Food & Beverages Industry. Key Growth Drivers & Industry Trends Productivity & Safety: Automated labor environments report over a 5% increase in productivity, while industrial robot end-effectors and grippers safely execute hazardous or strenuous tasks (such as high-pressure vacuum or radiation chambers). Robot-as-a-Service (RaaS): Growing preference for flexible subscription and service-based deployment models is lowering upfront capital barriers for small and medium-sized enterprises (SMEs). AI & IoT Convergence: Next-generation industrial robots are increasingly integrated with artificial intelligence and IoT capabilities, enabling adaptive handling of complex production tasks and autonomous mobile navigation.

1 0

Slurm 26.05.4: idle~ nodes go to unknown after reconfigure
by Stefan Staeglich 14 Oct '26

14 Oct '26

Hi, since we updated our site from Slurm 24.11 to Slurm 26.05.4 the idle~ (powersaved) nodes goes to state unk (unknown) whenever running scontrol reconfigure. This state is not cleaned by Slurm itself. Is this a known issue of Slurm 26.05? Best, Stefan

4 8

Slurm version 26.05.5 is now available
by Marshall Garey 13 Oct '26

13 Oct '26

We are pleased to announce the availability of Slurm version 26.05.5. These releases fix some mild to moderate severity issues and potential crashes. The full list of changes are available in the CHANGELOG files for each version: https://github.com/SchedMD/slurm/blob/slurm-26.05/CHANGELOG/slurm-26.05.md https://github.com/SchedMD/slurm/blob/slurm-25.11/CHANGELOG/slurm-25.11.md Slurm can be downloaded from: https://www.schedmd.com/download-slurm/ Marshall Garey Senior System Software Engineer NVIDIA

1 0

Need configuration help
by Steffen Grunewald 13 Oct '26

13 Oct '26

Good afternoon all, after having run a Slurm cluster for a long time with exclusive node usage (the DEFAULT partition set to Shared=EXCLUSIVE, for historical reasons), I have now received a request to provide individual CPU cores as well, so "shared mode" would be it. I set up a new partition with OverSubscribe=NO, changed the DEFAULT stanza to OverSubscribe=EXCLUSIVE, and things seemed to work (after I changed SelectTypeParameters to CR_Core_Memory from CR_Core - apparently nobody ever hit a memory limit before...). But - now there are jobs that don't run anymore. After staring at them for hours, I found that they would request CPUs=32 (they always did, I suspect). Our compute nodes are 2s/16c/2t, "slurmd -C" on them reports CPUs=64 Boards=1 SocketsPerBoard=2 CoresPerSocket=16 ThreadsPerCore=2 Moving those jobs (that claim "never to be runnable") to the new partition now appears to work, and they claim enough memory not to share their node with other jobs, but I'd like to keep them in their original partition. Where's my mistake? Am I trying to mix things that can't be mixed? Thanks, S -- Steffen Grunewald, Cluster Administrator Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 * D-14476 Potsdam-Golm * Germany ~~~ Fon: +49-331-567 7274 Mail: steffen.grunewald(at)aei.mpg.de ~~~

3 2