Loop-Oriented pointer analysis for automatic SIMD vectorization

Publication Type:
Journal Article
ACM Transactions on Embedded Computing Systems, 2018, 17 (2)
Issue Date:
Filename Description Size
tecs18.pdfPublished Version3.02 MB
Adobe PDF
Full metadata record
© 2018 ACM. Compiler-based vectorization represents a promising solution to automatically generate code that makes efficient use of modern CPUs with SIMD extensions. Two main auto-vectorization techniques, superword-level parallelism vectorization (SLP) and loop-level vectorization (LLV), require precise dependence analysis on arrays and structs to vectorize isomorphic scalar instructions (in the case of SLP) and reduce dynamic dependence checks at runtime (in the case of LLV). The alias analyses used in modern vectorizing compilers are either intra-procedural (without tracking inter-procedural data-flows) or inter-procedural (by using field-sensitive models, which are too imprecise in handling arrays and structs). This article proposes an inter-procedural Loop-oriented Pointer Analysis for C, called Lpa, for analyzing arrays and structs to support aggressive SLP and LLV optimizations effectively. Unlike field-insensitive solutions that pre-allocate objects for each memory allocation site, our approach uses a lazy memory model to generate access-based location sets based on how structs and arrays are accessed. Lpa can precisely analyze arrays 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 so that existing points-to resolution algorithms (e.g., flow-insensitive and flow-sensitive pointer analysis) can be reused easily. We have implemented Lpa fully in the LLVM compiler infrastructure (version 3.8.0). We evaluate Lpa by considering SLP and LLV, the two classic vectorization techniques, on a set of 20 C and Fortran CPU2000/2006 benchmarks. For SLP, Lpa outperforms LLVM’s BasicAA and ScevAA by discovering 139 and 273 more vectorizable basic blocks, respectively, resulting in the best speedup of 2.95% for 173.applu. For LLV, LLVM introduces totally 551 and 652 static bound checks under BasicAA and ScevAA, respectively. In contrast, Lpa has reduced these static checks to 220, with an average of 15.7 checks per benchmark, resulting in the best speedup of 7.23% for 177.mesa.
Please use this identifier to cite or link to this item: