English

Revec: Program Rejuvenation through Revectorization

Programming Languages 2019-02-11 v1 Performance

Abstract

Modern microprocessors are equipped with Single Instruction Multiple Data (SIMD) or vector instructions which expose data level parallelism at a fine granularity. Programmers exploit this parallelism by using low-level vector intrinsics in their code. However, once programs are written using vector intrinsics of a specific instruction set, the code becomes non-portable. Modern compilers are unable to analyze and retarget the code to newer vector instruction sets. Hence, programmers have to manually rewrite the same code using vector intrinsics of a newer generation to exploit higher data widths and capabilities of new instruction sets. This process is tedious, error-prone and requires maintaining multiple code bases. We propose Revec, a compiler optimization pass which revectorizes already vectorized code, by retargeting it to use vector instructions of newer generations. The transformation is transparent, happening at the compiler intermediate representation level, and enables performance portability of hand-vectorized code. Revec can achieve performance improvements in real-world performance critical kernels. In particular, Revec achieves geometric mean speedups of 1.160×\times and 1.430×\times on fast integer unpacking kernels, and speedups of 1.145×\times and 1.195×\times on hand-vectorized x265 media codec kernels when retargeting their SSE-series implementations to use AVX2 and AVX-512 vector instructions respectively. We also extensively test Revec's impact on 216 intrinsic-rich implementations of image processing and stencil kernels relative to hand-retargeting.

Keywords

Cite

@article{arxiv.1902.02816,
  title  = {Revec: Program Rejuvenation through Revectorization},
  author = {Charith Mendis and Ajay Jain and Paras Jain and Saman Amarasinghe},
  journal= {arXiv preprint arXiv:1902.02816},
  year   = {2019}
}

Comments

The first two authors contributed equally to this work. Published at CC 2019