Libraries

Intel® Cluster Studio XE works on Xeon® Phi coprocessor? OpenMP*? TBB? MPI?

Intel(R) Cluster Studio XE 2013 is a powerful tool suite - which helps you to develop applications, with low latency Intel MPI library, high performance C++/FORTRAN compiler, native profiling component named VTune Amplifier XE 2013, node level analysis component named Intel® Trace Collector/Analyzer, Threading and memory correctness components named Inspector XE 2013.     

Purposes of this article are: 

  • Developers
  • Linux*
  • C/C++
  • Intermediate
  • Intel® Cluster Toolkit
  • Intel® Cluster Ready
  • Intel Cluster Studio MPI OpenMP TBB VTune Amplifier Inspector ITAC
  • Development Tools
  • Enabling Connectionless DAPL UD in the Intel® MPI Library

    What is DAPL UD?

    Traditional InfiniBand* support involves MPI message transfer over the Reliable Connection (RC) protocol. While RC is long-standing and rich in functionality, it does have certain drawbacks: since it requires that each pair of processes setup a one-to-one connection at the start of the execution, memory consumption could (at the worst case) grow linearly as more MPI ranks are added and the number of pair connections grows.

  • Developers
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Server
  • C/C++
  • Fortran
  • Intermediate
  • Intel® MPI Library
  • user datagrams
  • ud
  • dapl ud
  • IB
  • InfiniBand
  • scalability
  • Message Passing Interface
  • Cluster Computing
  • Using Intel® MPI Library and Intel® Xeon Phi™ coprocessor tips

    1. Check prerequisites

    • Each host and each Intel® Xeon Phi™ coprocessor should have a unique IP address across a cluster;
    • ssh access between host(s) and Intel® Xeon Phi™ coprocessor(s) should be password-less;
    • Update the Intel® Manycore Platform Software Stack (Intel® MPSS) to current version;
  • Developers
  • Students
  • Linux*
  • Advanced
  • Intermediate
  • Cluster Tools
  • Intel® MPI Library
  • Intel® Cluster Studio
  • Intel® Cluster Studio XE
  • Cluster Computing
  • Parallel Computing
  • How to Resolve ARPACK issues with Intel MKL 11.0 Update 3

    Some ARPACK users have reported stability issues after upgrading to the Intel® Math Kernel Library (Intel® MKL) 11.0 Update 3 release. See examples here and here.

  • Developers
  • Partners
  • Professors
  • Students
  • Linux*
  • Unix*
  • C/C++
  • Fortran
  • Advanced
  • Beginner
  • Intermediate
  • Intel® Math Kernel Library
  • ARPACK
  • Development Tools
  • Open Source
  • Controlling Process Placement with the Intel® MPI Library

    When running an MPI program, process placement is critical to maximum performance.  Many applications can be sufficiently controlled with a simple process placement scheme, while some will require a more complex approach.  The Intel® MPI Library offers multiple options for controlling process placement within the Hydra process manager.

  • Developers
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Beginner
  • Intel® MPI Library
  • Rank Placement
  • Message Passing Interface
  • Cluster Computing
  • Recommendations to choose the right MKL usage model for Xeon Phi

    Intel(R) Math Kernel Library (Intel(R) MKL) has full support for the Intel(R) Xeon Phi(TM) Co-processor and supports the following compute models, one of which has the capability to use both multicore host and many-core co-processors at the same time. Here is a short summary of the execution models. In this article, we will describe data sizes and parallel programming / offload techniques best benefit from a particular MKL execution model:

  • Developers
  • Linux*
  • C/C++
  • Fortran
  • Intermediate
  • Intel® Math Kernel Library
  • Xeon Phi
  • MKL Offload
  • MKL Automatic Offload
  • MKL Native
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