Trace-Driven Memory Access Pattern Recognition in Computational Kernels

Abstract

Classifying memory access patterns is paramount to the selection of the right set of optimizations and determination of the parallelization strategy. Static analyses suffer from ambiguities present in source code, which modern compilation techniques, such as profile-guided optimization, alleviate by observing runtime behavior and feeding back into the compilation flow. This paper discusses a dynamic analysis technique for recognizing memory access patterns, with application to the stencils domain, and presents our design and C++ implementation using the memory-tracing tool Gleipnir. Finally, we evaluate and discuss the performance and matching capability of our classifiers in the context of the Polybench scientific benchmark suite, which includes both stencil and matrix computations.

Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling

“…Prior work also has examined nested loops with affine array subscript patterns, using different structures (vector [29], matrix [60] or reference [32]) to model memory access patterns or polyhedral model [45] to perform localization analysis. Since static analysis deals poorly with ambiguities in source code [6], recent work proposes profile-guided methods [17] and memorytracing [54] to capture memory access patterns. Simpler approaches focus on examining stencil code [23-25, 36, 54].…”