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Performance Tuning#

Overview#

  • Goals: throughput, latency, and utilization improvements

  • Workload categories and tuning priorities

CPU and Memory#

  • CPU governor and affinity

  • NUMA and memory policies (if applicable)

  • Swap, huge pages, and cache tuning

GPU Optimization#

  • Power limits and application clocks

  • Mixed precision and memory optimization

  • Kernel and data pipeline bottlenecks

Storage and I/O#

  • NVMe scratch and filesystem options

  • I/O schedulers and queue depths

  • Dataset layout and caching strategies

Spark/RAPIDS Tuning#

  • Executor sizing and GPU allocation

  • RAPIDS Accelerator configuration (placeholder)

  • Shuffle and spill tuning

Validation and Profiling#

  • Nsight Systems/Compute usage

  • Micro-benchmarks and A/B comparisons

  • Regression detection and guardrails