Multi-EP: What it is, What it does, Why it Matters

One copy instead of 16. Utilize multiple endpoints. Saturate the fabric. Reduce memory usage per MPI rank. Multi-thread MPI performance comparable to single threaded options. How can you achieve this parallelism with your code and more? This short webinar delivers the goods.

Learn how the Intel® MPI Library implementation using multiple endpoints (Multi-EP) enables a single multi-threaded rank per node, and more than one MPI thread per rank, requiring fewer MPI ranks. Multi-EP is especially effective for hybrid MPI + OpenMP* threaded programs, enabling multiple threads to be active in the MPI library at the same time without requiring extra synchronization. This allows a single MPI rank using multiple threads to saturate the network bandwidth, namely Intel® Omni-Path Architecture, eliminating the need for multiple ranks per node. Watch now to learn:

  • How a Hybrid MPI + OpenMP program with 16 cores can be laid out in 5 different ways.
  • Three ways to do MPI_reduce
  • MPI shared memory and subcommunicators strategies
  • How Multi-EP allows multiple threads per rank to do MPI communications, allowing the user to choose the optimal number of MPI ranks/node (as few as one).

Download Intel MPI Library from this page, and click here for more information about Multi-EP.

Larry Meadows, Sr. Principal Engineer, Intel Corporation

Larry is a key contributor to the High Performance Ecosystems and Applications Team (HEAT)—part of Intel’s Data Center organization. Specializing in high performance computing sine 1982, Larry got his start at FPS Computing in Portland, Oregon, writing assembly language for array processors. In 1989 he was a co-founder of PGI, now NVIDIA’s compiler group, and he was a compiler engineer at Sun Microsystems before joining Intel in 2004. Current professional interests center around performance analysis and associated tools, IA micro-architecture, and future Intel processors and accelerators. Larry has a B.S. in Mathematics from Reed College, Portland Oregon. He lives in Oregon and enjoys downhill skiing and home renovation in his copious free time.

For more complete information about compiler optimizations, see our Optimization Notice.