Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
The Intel® MPI Library and OpenMP* runtime libraries can create affinities between processes or threads, and hardware resources. This affinity keeps an MPI process or OpenMP thread from migrating to a different hardware resource, which can have a dramatic effect on the execution speed of a program.
Intel is bringing to market, in anticipation of general availability of the Intel® Xeon Phi™ Processor (codenamed Knights Landing), the Developer Access Program (DAP). DAP is an early access program for developers worldwide to purchase an Intel Xeon Phi 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.
This is the second article in a series of articles about High Performance Computing with the Intel Xeon Phi.
This case study examines the situation where the problem decomposition is the same for threading as it is for Message Passing Interface* (MPI); that is, the threading parallelism is elevated to the same level as MPI parallelism.