Performance Tools for Software Developers - SSE generation and processor-specific optimizations continuedCan I combine the processor values and target more than one processor? How to generate optimized code for both Intel and AMD* architecture? Where can I find more information on processor-specific optimizations?
Which applications are most likely to benefit from recompilation for Intel® Advanced Vector Extensions (Intel® AVX)?Applications containing vectorizable, floating-point loops or calls to performance libraries are the most likely to see significant performance gains from rebuilding for the Intel® Advanced Vector Extensions (Intel® AVX)
Which applications are most likely to benefit from recompilation for Intel® Advanced Vector Extensions (Intel® AVX)?Applications that spend considerable time in floating-point loops that can be vectorized are likely to benefit the most from the increased vector width of Intel® Advanced Vector Instructions (Intel® AVX).
This blog contains additional content for the article "Advanced Vectorization" from Parallel Universe #12:
Intel® Math Kernel Library Improved Small Matrix Performance Using Just-in-Time (JIT) Code Generation for Matrix Multiplication (GEMM)
The most commonly used and performance-critical Intel® Math Kernel Library (Intel® MKL) functions are the general matrix multiply (GEMM) functions.