Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
Part one of this three-part series focuses on thread parallelism and race conditions, and discusses using mutexes in OpenMP* to resolve race conditions.
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.
A look into the contents of the two "Pearls" books, edited by James Reinders and Jim Jeffers. These books contain a collection of examples of code modernization.
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.
How to install and enable Offload Over Fabric, configure the hardware, and test the configuration.
This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
LAMMPS is an open-source software package that simulates classical molecular dynamics. As it supports many energy models and simulation options, its versatility has made it a popular choice. It was first developed at Sandia National Laboratories to use large-scale parallel computation.