timestamp1724721093939Storage quota units change: TB to TiBby Kilian Cavalotti, Technical Lead & Architect, HPCFollowing in Oak footsteps, we’re excited to announce that Sherlock is adopting a new unit of measure for file system quotas. Starting today, we're transitioning from Terabytes (TB) to Tebibytes (TiB) for all storage allocations on
timestamp1724450400000Sherlock 4.0 is coming!by Kilian Cavalotti, Technical Lead & Architect, HPCNewHardwareWe are thrilled to announce that the next generation of Stanford's High-Performance Computing cluster is just around the corner. Mark your calendars for August 29, as we prepare to unveil Sherlock 4.0! Building on the success of previous
timestamp1707349764699Sherlock goes full flashby Stéphane Thiell & Kilian Cavalotti, Research Computing TeamDataHardwareImprovementWhat could be more frustrating than anxiously waiting for your computing job to finish? Slow I/O that makes it take even longer is certainly high on the list. But not anymore! Fir, Sherlock’s scratch file system, has just undergone a major
timestamp1682557518100Instant lightweight GPU instances are now availableby Kilian Cavalotti, Technical Lead & Architect, HPCNewHardwareWe know that getting access to GPUs on Sherlock can be difficult and feel a little frustrating at times. Which is why we are excited to announce the immediate availability of our new instant lightweight GPU instances!
timestamp1679706261451A new tool to help optimize job resource requirementsby Kilian Cavalotti, Technical Lead & Architect, HPCIt’s not always easy to determine the right amount of resources to request for a computing job. Making sure that the application will have enough resources to run properly, but avoiding over-requests that would make the jobs spend too much
timestamp1670036242756ClusterShell on Sherlockby Kilian Cavalotti, Technical Lead & Architect, HPCSoftwareNewEver wondered how your jobs were doing while they were running? Keeping a eye on a log file is nice, but what if you could quickly gather process lists, usage metrics and other data points from all the nodes your multi-node jobs are running