NDLI: Loop-oriented array- and field-sensitive pointer analysis for automatic SIMD vectorization

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Loop-oriented array- and field-sensitive pointer analysis for automatic SIMD vectorization

Content Provider	ACM Digital Library
Author	Xue, Jingling Fan, XIaokang Zhou, Hao Sui, Yulei
Abstract	Compiler-based auto-vectorization is a promising solution to automatically generate code that makes efficient use of SIMD processors in high performance platforms and embedded systems. Two main auto-vectorization techniques, superword-level parallelism vectorization (SLP) and loop-level vectorization (LLV), re- quire precise dependence analysis on arrays and structs in order to vectorize isomorphic scalar instructions and/or reduce dynamic dependence checks incurred at runtime. The alias analyses used in modern vectorizing compilers are either intra-procedural (without tracking inter-procedural data-flows) or inter-procedural (by using field-insensitive models, which are too imprecise in handling arrays and structs). This paper pro- poses an inter-procedural Loop-oriented Pointer Analysis, called LPA, for analyzing arrays and structs to support aggressive SLP and LLV optimizations. Unlike field-insensitive solutions that pre- allocate objects for each memory allocation site, our approach uses a fine-grained memory model to generate location sets based on how structs and arrays are accessed. LPA can precisely analyze ar- rays and nested aggregate structures to enable SIMD optimizations for large programs. By separating the location set generation as an independent concern from the rest of the pointer analysis, LPA is designed to reuse easily existing points-to resolution algorithms. We evaluate LPA using SLP and LLV, the two classic vectorization techniques on a set of 20 CPU2000/2006 benchmarks. For SLP, LPA enables it to vectorize a total of 133 more basic blocks, with an average of 12.09 per benchmark, resulting in the best speedup of 2.95% for 173.applu. For LLV, LPA has reduced a total of 319 static bound checks, with an average of 22.79 per benchmark, resulting in the best speedup of 7.18% for 177.mesa. .
Starting Page	41
Ending Page	51
Page Count	11
File Format	PDF
ISBN	9781450343169
DOI	10.1145/2907950.2907957
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Array Loop Field Pointer analysis Simd
Content Type	Text
Resource Type	Article

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