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Enhance Data Center Efficiency and Throughput by Co-Scheduling on NVIDIA CPU+GPU Systems#

GTC 2026 digest Program Core pick San Jose · 16–19 March 2026

Abstract Talk page Recording

Brian Austin (NERSC), Mathias Wagner (NVIDIA) — Talk, Thursday, March 19

  • Why: Utilization economics for coherent CPU-GPU systems, with concrete isolation mechanisms the PM can use directly — cgroups, ARM MPAM cache/memory-bandwidth partitioning, and InfiniBand QoS for network traffic separation.
  • What: Co-scheduling CPU-only, partially accelerated, and fully GPU-accelerated jobs on the same coherent CPU-GPU nodes to boost throughput and energy efficiency, removing the need for dedicated per-workload clusters, with workload studies for NERSC's upcoming Doudna system and projected utilization/efficiency gains. (from abstract only)
  • How: Isolation via CPU affinity (cgroups), cache and memory-bandwidth partitioning through ARM's MPAM extensions, and InfiniBand QoS for network separation; plus methodology for identifying co-scheduling candidates and designing scheduling policies users will accept.
  • Where applicable: NERSC HPC context (Doudna, Grace-based coherent nodes), but cgroups/MPAM/IB-QoS are general techniques for any shared GPU fabric; the policy/ incentive discussion is broadly applicable to mixed-workload clusters.