Video SIMDBench: Benchmarking the Compiler Vectorization for Multimedia Applications

Single Instruction Multiple Data (SIMD) Extensions become popular in computer architectures as a simple and efficient way to exploit the data parallelism hidden in applications. The compiler research community has proposed automatic vectorization as the answer to the complexity of low-level programming of vector units. Despite recent advances in compilation techniques, modern compilers miss opportunities to automatically vectorize code. One of the biggest challenges is to evaluate the changes against the best hand-written code. This paper presents a benchmark suite based on video encoding and decoding kernels. The suite contains hand-written versions of the kernels provided by the open source community that support the latest SIMD extensions. The paper also compares the performance of three available compilers (GCC, LLVM, and ICC) against the hand-written kernels. A performance evaluation, using an i7-4790 processor, shows that the auto-vectorized version produced by the best compiler achieves on average only 28% of the hand-tuned kernels.