Matching memory access patterns and data placement for NUMA systems

• Authors: Zoltan Majo;Thomas R. Gross
• Venue: Proceedings of the Tenth International Symposium on Code Generation and Optimization
• Year: 2012

Many recent multicore multiprocessors are based on a nonuniform memory architecture (NUMA). A mismatch between the data access patterns of programs and the mapping of data to memory incurs a high overhead, as remote accesses have higher latency and lower throughput than local accesses. This paper reports on a limit study that shows that many scientific loop-parallel programs include multiple, mutually incompatible data access patterns, therefore these programs encounter a high fraction of costly remote memory accesses. Matching the data distribution of a program to the individual data access patterns is possible, however it is difficult to find a data distribution that matches all access patterns. Directives as included in, e.g., OpenMP provide a way to distribute the computation, but the induced data partitioning does not take into account the placement of data into the processors' memory. To alleviate this problem we describe a small set of language-level primitives for memory allocation and loop scheduling. Using the primitives together with simple program-level transformations eliminates mutually incompatible access patterns from OpenMP-style parallel programs. This result represents an improvement of up to 3.3X over the default setup, and the programs obtain a speedup of up to 33.6X over single-core execution (19X on average) on a 4-processor 32-core machine.