Argon Phase 3 Feedback June 4, 2019

Survey Response 34 Total Responses

Takeaways GPUs continue to increase in popularity Over 60% of respondents say they need GPUs. Nearly all users prefer nVidia. Surprisingly over 80% of users indicated they needed 16GB+ of gpu memory and 50% indicated 24GB+ are needed. This is surprising and does not generally align with what Research Services has seen from the community to date in terms of purchases/conversations. Most respondents want as many GPUs per node as they can get. Local disk continues to be requested by most respondents The most requested ram amounts were 1TB+, 192GB, and 384GB (in order of interest)

Takeaways HPC workload and high speed fabric remains important (~50% of respondents) Cost is important both in absolute terms of keeping compute node prices reasonable and in providing good compute bang for the buck. High clock rates were generally the least valued area by respondents. Large numbers of cores per node are viewed favorably.

Ranking of High Level Qualifiers 1. Keep the cost per compute node reasonably low (less than $7000) 7 People Rated This As Most Important 2. The best FLOPS/Dollar 4 People Rated This As Most Important 3. Multiple well performing nodes connected with a fast interconnect 4. As many processor cores in a single compute node as possible 5. The higher the clock rate the better 1 Person Rated This As Most Important 6. Ability to put as many GPUs into the system as possible 8 People Rated This As Most Important

Other Feedback Of Note/To Follow Up On Argon/Login Nodes/Interactive Operations are Slow Don’t like having maintenance on Thursday as it is disruptive to work during the week. Would prefer Monday or Friday. Request for Duo to save state based on ip address for some period of time, particularly now that VPN has duo enabled. Requested ability to use GPU nodes for remote visualization. Need access to “Training nodes” with many GPUs and high memory. For our molecular dynamics research, we have found uses for both 1 and 2 GPU nodes, but 1 GPU in a node many times performs better than 2 GPUs per node due to the increased number of threads available to the single GPU.  In cases where there are 4 CPUs in a node, 2GPUs are preferred. In cases where 2 CPUs are in a node, 1 GPU is preferred. (especially for cost reasons). This is GPU-dependent in the sense that newer GPUs are powerful enough to only need one (as long as it has enough RAM to hold the entire system we care to look at), but a critical component of our research is that CUDA is a must. AMD GPUs are of no use to our group yet as most modern MD codes are specifically designed around NVIDIA GPUs (even if OpenCL alternatives are offered).