Automatic intra-register vectorization for the Intel architecture
International Journal of Parallel Programming
Vectorization for SIMD architectures with alignment constraints
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Optimizing Compiler for the CELL Processor
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
Multi-platform Auto-vectorization
Proceedings of the International Symposium on Code Generation and Optimization
Outer-loop vectorization: revisited for short SIMD architectures
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
International Journal of Parallel Programming
A case study on compiler optimizations for the Intel® Core™ 2 duo processor
International Journal of Parallel Programming
An Evaluation of Vectorizing Compilers
PACT '11 Proceedings of the 2011 International Conference on Parallel Architectures and Compilation Techniques
Extending a C-like language for portable SIMD programming
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Extending OpenMP* with vector constructs for modern multicore SIMD architectures
IWOMP'12 Proceedings of the 8th international conference on OpenMP in a Heterogeneous World
Boost.SIMD: generic programming for portable SIMDization
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
| Hi-index | 0.00 |
Using SIMD (Single Instruction Multiple Data) is a cost-effective way to explore data parallelism on modern processors. Most processor vendors today provide SIMD engines, such as Altivec/VSX for POWER, SSE/AVX for Intel processors, and NEON for ARM. While high-level SIMD programming models are rapidly evolving, for many SIMD developers, the most effective way to get the performance out of SIMD is still by programming directly via vendor-provided SIMD intrinsics. However, intrinsics programming is both tedious and error-prone, and worst of all, introduces non-portable codes. This paper presents the Generic SIMD Library (https://github.com/genericsimd/generic_simd/), an open-source, portable C++ interface that provides an abstraction of short vectors and overloads most C/C++ operators for short vectors. The library provides several mappings from platform-specific intrinsics to the generic SIMD intrinsic interface so that codes developed based on the library are portable across different SIMD platforms. We have evaluated the library with several applications from the multimedia, data analytics and math domains. Compared with platform-specific intrinsics codes, using Generic SIMD Library results in less line-of-code, a 22% reduction on average, and achieves similar performance as platform-specific intrinsics versions.