In the High Performance Computing (HPC) area, parallel computing techniques such as MPI, OpenMP*, one-sided communications, shmem, and Fortran coarray are widely utilized. This blog is part of a series that will introduce the use of these techniques, especially how to use them on the Intel® Xeon Phi™ coprocessor. This first blog discusses the main usage of the hybrid MPI/OpenMP model.
Tips and tricks on how to get the optimal performance settings for your mixed Intel MPI/OpenMP applications.
This morning, I took a rare break, and attended a tutorial at Supercomputing. I'm glad I did.
By Erik Niemeyer (Intel Corporation) and Ken Strandberg (Catlow Communications*)
Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
There are two principal methods of parallel computing: distributed memory computing and shared memory computing. As more processor cores are dedicated to large clusters solving scientific and engineering problems, hybrid programming techniques combining the best of distributed and shared memory programs are becoming more popular.