Get the Latest on Intel® MPI to Boost Performance, Container & Cloud Support

The Intel® MPI Library is one of the most widely used MPI implementations for distributed computing, with continual optimizations that help HPC developers and data scientists deliver performance and flexibility.

In this session, Technical Consulting Engineer James Tullos gives a tour of the tool’s latest incarnation, including how it:

  • Increases performance on OpenFabrics Interfaces (OFI), a framework specifically designed to meet the performance and scalability requirements of HPC applications running in a tightly coupled network environment.
  • Provides tight integration for users of Singularity* containers which are used to package entire scientific workflows, software and libraries, and data.
  • Optimizes performance on AWS* Elastic Fabric Adapter, a network interface that enables you to run HPC apps at scale, including computational fluid dynamics, weather modeling, and reservoir simulation.

Get the software

  • Intel® MPI Library—This library is one of five free Intel® Performance Libraries. Download one or all of them today, free.
James Tullos, Technical Consulting Engineer, Intel Corporation

James Tullos is a Technical Consulting Engineer responsible for all things “customer technical support and training” on application performance using Intel Intel® Software Development Tools. He focuses on parallel performance in HPC and cluster environments, with specific knowledge around distributed memory computing using the Message Passing Interface (MPI).

James enjoys sharing his knowledge and is a regular presence at technical trade shows, workshops, and conferences. Prior to joining Intel, he worked in the aerospace engineering field focused on propulsion system analysis programs.

James holds an MS in Aeronautical and Astronautical Engineering from Purdue University, and a BS in Aerospace Engineering from Mississippi State University.

Performance varies by use, configuration, and other factors. Learn more at www.Intel.com/PerformanceIndex.