Version : Intel® MKL 10.* Product : Intel® Math Kernel Library Operating System : All supported OS’s – Microsoft Windows*, Linux* and Mac OS X Problem Description : When iparm[59] is set 1 and – memory required for the task solution is less then MKL_PARDISO_OOC_MAX_CORE_SIZE and – RAM available in the system > = MKL_PARDISO_OOC_MAX_CORE_SIZE PARDISO reports the following message: “=== PARDISO is running in Out-Of-Core mode, because iparam(60)=1 and there is not enough RAM for In-Core ===”.
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PARDISO: incorrect behavior in deciding whether to use in-Core or out-of-core (OOC)
Author: Feilong Huang – Intel Compiler Team ============ Check out source code and compile with gcc ========== 1. Set up build environment. Please refer to the section “Check out source code and compile with gcc” in this article . 2
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Compile dtdrive for Moblin with Intel Compiler
Problem: While running the application linked with IPP a command window is popped-up with the following text: OMP: Warning #2: Cannot open message catalog “1037libiomp5ui.dll”: OMP: System error #126: The specified module could not be found. OMP: Info #3: Default messages will be used.
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Warning message: Cannot open message catalog”1037libiomp5ui.dll”
In Intel® MKL PARDISO, iparm(60) controls – out-of-core (OOC) version or in-core version – is used. The OOC PARDISO can solve very large problems by holding the matrix factors in files on the disk resulting the amount of main memory required by OOC PARDISO is significantly reduced.
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How to use OOC PARDISO?
Introduction The low-level primitives within the Intel IPP library generally represent basic atomic operations.
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OpenMP and the Intel® IPP Library
Overview This article contains links to the redistributable installation packages for the Intel Parallel Composer. The redistributable packages are for the end users who use applications that are built with Intel Parallel Composer. Please note that there is one redistributable package for every update
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Redistributable libraries for the Intel(R) Parallel Composer
Introduction : The new 11.1 compiler options allow thread affinity to be compiled into the executable. The compiled-in affinity of the executable will override any setting of the affinity environment variables KMP_AFFINITY and GOMP_CPU_AFFINITY. Usage of the affinity compiler options and environment variables is demonstrated. Version : Intel® C++ and Fortran Compilers for Windows* (versions 11.1.048 or higher) Intel® C++ and Fortran Compilers for Linux* (versions 11.1.056 or higher) Application Notes : The thread affinity compiler options are /Qpar-affinity (Windows*) or -par-affinity (Linux*). You must compile the main program with these options for them to have any effect. Further, these options only have an effect if /Qopenmp and/or /Qparallel (Windows*) or -openmp and/or -parallel (Linux*) have also been specified. Obtaining Source Code : The compiler OpenMP samples may be used. Prerequisites : Thread affinity is supported on Windows* OS systems and versions of Linux* OS systems that have kernel support for thread affinity
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Thread affinity compiler options and environment variables
By Mike Yi and Orion Granatir Game developers want to deliver the best experience possible for each player, but they also want a game that is fair to all players. A higher-performing machine for one player can and should lead to a better game experience, but not a gameplay advantage in a multi-player situation. Many solutions to this dilemma exist; one approach is to use the extra power to render more frames
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Scaling Ambient Animations for Improved Game Experience
Overview This article contains links to the redistributable installation packages for the Intel Compiler Professional Editions for Windows. The redistributable packages are for the end users who use applications that are built with Intel Compilers
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Redistributable libraries for the Intel(R) C++ and Visual Fortran Compiler for Windows
Introduction This article describes how to use the Intel® Math Kernel Library (Intel® MKL) from a Python program.
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Using Intel® MKL in your Python program
