Resolve NUMA Performance Impact with Intel® VTune™ Profiler | Intel Software
5Intel VTune Profiler helps you identify and resolve memory latency issues. One possible cause for increased memory latency are non-uniform memory accesses (NUMA), especially in memory bound workloads. In this video we demonstrate how to track this kind of issues down and how to address it.
Intel VTune Profiler Platform Analysis collects coarse-grained, system-level metrics to identify hardware bottlenecks and inefficient use of hardware. The example workload in this video has steady memory utilization, but it is memory bound and has a high percentage of remote NUMA accesses where data being accessed is in DRAM off the other socket on a dual-socket system. This also generates large cross-socket UPI traffic with many spikes to satisfy the remote NUMA accesses.
By assigning affinity of all threads in the workload to cores on a single socket, we can optimize memory access performance for this workload.
VTune Profiler is able to visualize memory access latency and types of memory accesses across the platform for you. It thus makes it easy to identify the root cause of a performance issue in a memory bound workload and point you towards a solution.
Test your multi-socket server workload with VTune Profiler today.
Intel® VTune™ Profiler: https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html
Intel Distribution for Python*: https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html
Intel® oneAPI: https://www.intel.com/content/www/us/en/developer/tools/oneapi/overview.html
oneAPI Code Samples Library: https://www.intel.com/content/www/us/en/developer/tools/oneapi/code-samples.html
oneAPI Code Samples Catalog: https://oneapi-src.github.io/oneAPI-samples/
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Resolve NUMA Performance Impact with Intel® VTune™ Profiler | Intel Software