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The latter are necessary to disambiguate dependences among loop iterations and communicate vectorization opportunities to the compiler. Just as OpenMP has historically provided a way for users to direct execution to be parallelized across threads, it now provides ways to parallelize across SIMD lanes by means of compiler directives. Thankfully, the community is converging on a vectorization standard, in OpenMP 4.x, that eases the programming burden.
#INTEL C COMPILER SVML MATH FUNCTIONS FULL#
In some cases, unleashing the full performance potential of computational loops can require expertise in language interfaces, compiler features, and microarchitecture. Exploiting SIMD for codes with complex control flow, leading to masking and execution overhead, can be difficult. On the Intel ® Xeon Phi ™ (co)processor, for instance, the 8-wide double-precision SIMD units can provide up to one order of magnitude higher performance per core. Data parallelism is achieved with a combination of multiple threads and increasingly-wide SIMD units. On modern CPUs, effective use of SIMD (Single Instruction, Multiple Data) is essential to approach peak performance. All microbenchmarks are available as open source as a reference for programmers and compiler experts to enhance SIMD code generation.
#INTEL C COMPILER SVML MATH FUNCTIONS PORTABLE#
Our experiments show that in many cases portable performance can be achieved. We compare OpenMP* 4.x SIMD vectorization with and without vector data types against SIMD intrinsics and C++ SIMD types. We explore coding strategies for improving SIMD performance across different compilers and platforms (Intel ® Xeon ® processor and Intel ® Xeon Phi ™ (co)processor). We investigate the ability of current compilers (GNU, Clang, and Intel) to generate SIMD code for microbenchmarks that cover common patterns in scientific codes and for two kernels from the VASP and the MOM5/ERGOM application. The OpenMP 4.x SIMD directives strive to provide portability. Compilers often require programmers to identify opportunities for vectorization, using directives to disprove data dependences. Effective vectorization is becoming increasingly important for high performance and energy efficiency on processors with wide SIMD units.