Intel Xeon-Phi an Overview – MKL
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Using Intel® Math Kernel Library and Intel® Integrated Performance Primitives in the Microsoft* .NET* Framework
Using Intel® MKL and Intel® IPP in Microsoft* .NET* Framework Using Intel® Math Kernel Library and Intel® Integrated Performance Primitives in the Microsoft* .NET* Framework Document Number: 323195-001US.pdf 1 Using Intel® MKL and Intel® IPP in Microsoft* .NET* Framework INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL® PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS PROVIDED IN INTEL'S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER, AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. UNLESS OTHERWISE AGREED IN WRITING BY INTEL, THE INTEL PRODUCTS ARE NOT DESIGNED NOR INTENDED FOR ANY APPLICATION IN WHICH THE FAILURE OF THE INTEL PRODUCT COULD CREATE A SITUATION WHERE PERSONAL INJURY OR DEATH MAY OCCUR. Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the absence or characteristics of any features or instructions marked "reserved" or "undefined." Intel reserves these for future definition and shall have no responsibility whatsoever for conflicts or incompatibilities arising from future changes to them. The information here is subject to change without notice. Do not finalize a design with this information. The products described in this document may contain design defects or errors known as errata which may cause the product to deviate from published specifications. -
Intel(R) Math Kernel Library for Linux* OS User's Guide
Intel® Math Kernel Library for Linux* OS User's Guide MKL 10.3 - Linux* OS Document Number: 315930-012US Legal Information Legal Information INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL(R) PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS PROVIDED IN INTEL'S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER, AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. UNLESS OTHERWISE AGREED IN WRITING BY INTEL, THE INTEL PRODUCTS ARE NOT DESIGNED NOR INTENDED FOR ANY APPLICATION IN WHICH THE FAILURE OF THE INTEL PRODUCT COULD CREATE A SITUATION WHERE PERSONAL INJURY OR DEATH MAY OCCUR. Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the absence or characteristics of any features or instructions marked "reserved" or "undefined." Intel reserves these for future definition and shall have no responsibility whatsoever for conflicts or incompatibilities arising from future changes to them. The information here is subject to change without notice. Do not finalize a design with this information. The products described in this document may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. Contact your local Intel sales office or your distributor to obtain the latest specifications and before placing your product order. -
Intel® Math Kernel Library for Windows* OS User's Guide
Intel® Math Kernel Library for Windows* OS User's Guide Intel® MKL - Windows* OS Document Number: 315930-027US Legal Information Contents Contents Legal Information................................................................................7 Introducing the Intel® Math Kernel Library...........................................9 Getting Help and Support...................................................................11 Notational Conventions......................................................................13 Chapter 1: Overview Document Overview.................................................................................15 What's New.............................................................................................15 Related Information.................................................................................15 Chapter 2: Getting Started Checking Your Installation.........................................................................17 Setting Environment Variables ..................................................................17 Compiler Support.....................................................................................19 Using Code Examples...............................................................................19 What You Need to Know Before You Begin Using the Intel® Math Kernel Library...............................................................................................19 Chapter 3: Structure of the Intel® Math Kernel Library Architecture Support................................................................................23 -
0 BLIS: a Modern Alternative to the BLAS
0 BLIS: A Modern Alternative to the BLAS FIELD G. VAN ZEE and ROBERT A. VAN DE GEIJN, The University of Texas at Austin We propose the portable BLAS-like Interface Software (BLIS) framework which addresses a number of short- comings in both the original BLAS interface and present-day BLAS implementations. The framework allows developers to rapidly instantiate high-performance BLAS-like libraries on existing and new architectures with relatively little effort. The key to this achievement is the observation that virtually all computation within level-2 and level-3 BLAS operations may be expressed in terms of very simple kernels. Higher-level framework code is generalized so that it can be reused and/or re-parameterized for different operations (as well as different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any and all BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are supported through a straightforward compatibility layer, though calling sequences must be updated for those who wish to access new functionality. Experimental performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL). Categories and Subject Descriptors: G.4 [Mathematical Software]: Efficiency General Terms: Algorithms, Performance Additional Key Words and Phrases: linear algebra, libraries, high-performance, matrix, BLAS ACM Reference Format: ACM Trans. Math. Softw. 0, 0, Article 0 ( 0000), 31 pages. -
Intel® Parallel Studio Xe 2017 Runtime
Intel® Parallel StudIo Xe 2017 runtIme Release Notes 26 July 2016 Contents 1 Introduction ................................................................................................................................................... 1 1.1 What Every User Should Know About This Release ..................................................................... 1 2 Product Contents ......................................................................................................................................... 2 3 System Requirements ................................................................................................................................ 3 3.1 Processor Requirements........................................................................................................................... 3 3.2 Disk Space Requirements ......................................................................................................................... 3 3.3 Operating System Requirements .......................................................................................................... 3 3.4 Memory Requirements .............................................................................................................................. 3 3.5 Additional Software Requirements ...................................................................................................... 3 4 Issues and Limitations .............................................................................................................................. -
0 BLIS: a Framework for Rapidly Instantiating BLAS Functionality
0 BLIS: A Framework for Rapidly Instantiating BLAS Functionality FIELD G. VAN ZEE and ROBERT A. VAN DE GEIJN, The University of Texas at Austin The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly in- stantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels. While others have had similar insights, BLIS reduces the necessary kernels to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and imple- mented in ISO C99 so that it can be reused and/or re-parameterized for different operations (and different architectures) with little to no modification. Inserting high-performance kernels into the framework facili- tates the immediate optimization of any BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the the traditional Fortran-77 BLAS interface, a generalized C interface, or any other higher level interface that builds upon this latter API. Preliminary performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL). Categories and Subject Descriptors: G.4 [Mathematical Software]: Efficiency General Terms: Algorithms, Performance Additional Key Words and Phrases: linear algebra, libraries, high-performance, matrix, BLAS ACM Reference Format: ACM Trans. -
Accelerating Spark ML Applications Date Published: 2020-01-16 Date Modified
Best Practices Accelerating Spark ML Applications Date published: 2020-01-16 Date modified: https://docs.cloudera.com/ Legal Notice © Cloudera Inc. 2021. All rights reserved. The documentation is and contains Cloudera proprietary information protected by copyright and other intellectual property rights. No license under copyright or any other intellectual property right is granted herein. Copyright information for Cloudera software may be found within the documentation accompanying each component in a particular release. Cloudera software includes software from various open source or other third party projects, and may be released under the Apache Software License 2.0 (“ASLv2”), the Affero General Public License version 3 (AGPLv3), or other license terms. Other software included may be released under the terms of alternative open source licenses. Please review the license and notice files accompanying the software for additional licensing information. Please visit the Cloudera software product page for more information on Cloudera software. For more information on Cloudera support services, please visit either the Support or Sales page. Feel free to contact us directly to discuss your specific needs. Cloudera reserves the right to change any products at any time, and without notice. Cloudera assumes no responsibility nor liability arising from the use of products, except as expressly agreed to in writing by Cloudera. Cloudera, Cloudera Altus, HUE, Impala, Cloudera Impala, and other Cloudera marks are registered or unregistered trademarks in the United States and other countries. All other trademarks are the property of their respective owners. Disclaimer: EXCEPT AS EXPRESSLY PROVIDED IN A WRITTEN AGREEMENT WITH CLOUDERA, CLOUDERA DOES NOT MAKE NOR GIVE ANY REPRESENTATION, WARRANTY, NOR COVENANT OF ANY KIND, WHETHER EXPRESS OR IMPLIED, IN CONNECTION WITH CLOUDERA TECHNOLOGY OR RELATED SUPPORT PROVIDED IN CONNECTION THEREWITH. -
Intel Threading Building Blocks
Praise for Intel Threading Building Blocks “The Age of Serial Computing is over. With the advent of multi-core processors, parallel- computing technology that was once relegated to universities and research labs is now emerging as mainstream. Intel Threading Building Blocks updates and greatly expands the ‘work-stealing’ technology pioneered by the MIT Cilk system of 15 years ago, providing a modern industrial-strength C++ library for concurrent programming. “Not only does this book offer an excellent introduction to the library, it furnishes novices and experts alike with a clear and accessible discussion of the complexities of concurrency.” — Charles E. Leiserson, MIT Computer Science and Artificial Intelligence Laboratory “We used to say make it right, then make it fast. We can’t do that anymore. TBB lets us design for correctness and speed up front for Maya. This book shows you how to extract the most benefit from using TBB in your code.” — Martin Watt, Senior Software Engineer, Autodesk “TBB promises to change how parallel programming is done in C++. This book will be extremely useful to any C++ programmer. With this book, James achieves two important goals: • Presents an excellent introduction to parallel programming, illustrating the most com- mon parallel programming patterns and the forces governing their use. • Documents the Threading Building Blocks C++ library—a library that provides generic algorithms for these patterns. “TBB incorporates many of the best ideas that researchers in object-oriented parallel computing developed in the last two decades.” — Marc Snir, Head of the Computer Science Department, University of Illinois at Urbana-Champaign “This book was my first introduction to Intel Threading Building Blocks. -
Intel® Math Kernel Library 10.1 for Windows*, Linux*, and Mac OS* X
Intel® Math Kernel Library 10.1 for Windows*, Linux*, and Mac OS* X Product Brief The Flagship for High-Performance Computing Intel® Math Kernel Library 10.1 Math Software for Windows*, Linux*, and Mac OS* X Intel® Math Kernel Library (Intel® MKL) is a library of highly optimized, extensively threaded math routines for science, engineering, and financial applications that require maximum performance. Availability • Intel® C++ Compiler Professional Editions (Windows, Linux, Mac OS X) • Intel® Fortran Compiler Professional Editions (Windows, Linux, Mac OS X) • Intel® Cluster Toolkit Compiler Edition (Windows, Linux) • Intel® Math Kernel Library 10.1 (Windows, Linux, Mac OS X) Functionality “By adopting the • Linear Algebra—BLAS and LAPACK • Fast Fourier Transforms Intel MKL DGEMM • Linear Algebra—ScaLAPACK • Vector Math Library libraries, our standard • Linear Algebra—Sparse Solvers • Vector Random Number Generators benchmarks timing DGEMM Threaded Performance Intel® Xeon® Quad-Core Processor E5472 3.0GHZ, 8MB L2 Cache,16GB Memory Intel® Xeon® Quad-Core Processor improved between Redhat 5 Server Intel® MKL 10.1; ATLAS 3.8.0 DGEMM Function 43 percent and 71 Intel MKL - 8 Threads Intel MKL - 1 Thread ATLAS - 8 Threads 100 percent…” ATLAS - 1 Thread 90 Matt Dunbar 80 Software Developer, 70 ABAQUS, Inc. s 60 p GFlo 50 40 30 20 10 0 4 6 8 2 4 0 8 2 8 4 6 0 4 2 4 8 6 6 8 9 10 11 12 13 14 16 18 19 20 22 25 32 38 51 Matrix Size (M=20000, N=4000, K=64, ..., 512) Features and Benefits Vector Random Number Generators • Outstanding performance Intel MKL Vector Statistical Library (VSL) is a collection of 9 random number generators and 22 probability distributions • Multicore and multiprocessor ready that deliver significant performance improvements in physics, • Extensive parallelism and scaling chemistry, and financial analysis. -
Driving Performance with Intel® Advisor's Flow Graph Analyzer
Software THE PARALLEL UNIVERSE Driving Code Performance with Intel® Advisor’s Flow Graph Analyzer Modernize your Code with Issue Intel® Parallel Studio XE 30 Enabling FPGAs for Software Developers 2017 CONTENTS Letter from the Editor 3 Meet Intel® Parallel Studio XE 2018 by Henry A. Gabb, Senior Principal Engineer, Intel Corporation Driving Performance with Intel® Advisor’s Flow Graph Analyzer 5 by Vasanth Tovinkere, Architect, Intel® Flow Graph Analyzer; Pablo Reble, Software Engineer; Farshad Akhbari, Perceptual Computing Technical Lead; and Palanivel Guruvareddiar, Perceptual Computing Software Architect; Intel Corporation FEATURE Welcome to the Adult World, OpenMP* 19 by Barbara Chapman, Professor, Stony Brook University, and Director of Computer Science and Mathematics, Brookhaven National Laboratory Enabling FPGAs for Software Developers 25 by Bernhard Friebe, Senior Director of FPGA Software Solutions Marketing, Intel Corporation, and James Reinders, HPC Enthusiast Modernize Your Code for Performance, Portability, and Scalability 37 by Jackson Marusarz, Technical Consulting Engineer, Intel Corporation Dealing with Outliers 45 by Oleg Kremnyov, QA Engineer; Mikhail Averbukh, Software Engineer; and Ivan Kuzmin, Software Engineering Manager; Intel Corporation Tuning for Success with the LatestSIMD Extensions 57 and Intel® Advanced Vector Extensions 512 by Xinmin Tian, Senior Principal Engineer; Hideki Saito, Principal Engineer; Sergey Kozhukhov, Senior Staff Engineer; and Nikolay Panchenko, Staff Engineer; Intel Compiler and Language Lab, Intel Corporation Effectively Using Your Whole Cluster 77 by Rama Kishan Malladi, Technical Marketing Engineer, Intel Corporation Is Your Cluster Healthy? 85 by Brock A. Taylor, HPC Solution Architect, Intel Corporation Optimizing HPC Clusters 89 by Michael Hebenstreit, Data Center Engineer, Intel Corporation For more complete information about compiler optimizations, see our Optimization Notice. -
Intel® Parallel Studio XE 2013 SP1 Product Brief
EFFICIENTLY PRODUCE FAST, SCALABLE, AND RELIABLE APPLICATIONS Intel® Parallel Studio XE 2013 SP1 Product Brief Top Features Leading Development Tools for Top Performance . Industry-leading application Deliver top application performance while minimizing development, tuning and testing time, and performance that scales as effort. Intel® Parallel Studio XE provides C/C++ and Fortran developers cutting edge performing processor core count and vector compilers and libraries, the right parallel programming models, and complementary and width increase compatible analysis tools. It plugs seamlessly into Microsoft Visual Studio* and the GNU tool chain to keep you productive while preserving your development environment investment. Efficiently scale on tomorrow’s Boost performance for your applications as they run on today’s and tomorrow’s IA-compatible hardware while preserving processors and coprocessors, including Intel® Xeon® processors and Intel® Xeon Phi™ investment in existing code coprocessors. Compatible with leading development environments Intel Parallel Studio XE includes the next generation of software development tools: . Intel® C, C++ and Fortran Compilers – Industry-Leading Compilers “If you need performance for your . Intel® Math Kernel Library (Intel® MKL) and Intel® Integrated Performance Primitives C++ and/or Fortran applications, (Intel® IPP) – Performance Libraries you need to try Intel software . Intel® Threading Building Blocks (Intel® TBB) and Intel® Cilk™ Plus – Parallel Programming development products." Models Dr. Artur Guzik, Senior Engineer, Neubrex. Co., . Intel® Advisor XE – Threading Prototyping Tool Ltd. Intel® VTune™ Amplifier XE – Advanced Threading and Performance Profiler . Intel® Inspector XE – Memory and Threading Debugger "The new VTune Amplifier XE Get more performance with less effort. Optimize performance by using the latest versions of brings even more capability to an broadly used Intel® Software Development Products. -
Intel® Technology Journal | Volume 18, Issue 2, 2014
Intel® Technology Journal | Volume 18, Issue 2, 2014 Publisher Managing Editor Content Architect Cory Cox Stuart Douglas Paul Cohen Program Manager Technical Editor Technical Illustrators Paul Cohen David Clark MPS Limited Technical and Strategic Reviewers Bruce J Beare Tracey Erway Michael Richmond Judi A Goldstein Kira C Boyko Miao Wei Paul De Carvalho Ryan Tabrah Prakash Iyer Premanand Sakarda Ticky Thakkar Shelby E Hedrick Intel® Technology Journal | 1 Intel® Technology Journal | Volume 18, Issue 2, 2014 Intel Technology Journal Copyright © 2014 Intel Corporation. All rights reserved. ISBN 978-1-934053-63-8, ISSN 1535-864X Intel Technology Journal Volume 18, Issue 2 No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4744. Requests to the Publisher for permission should be addressed to the Publisher, Intel Press, Intel Corporation, 2111 NE 25th Avenue, JF3-330, Hillsboro, OR 97124-5961. E-Mail: [email protected]. Th is publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in professional services. If professional advice or other expert assistance is required, the services of a competent professional person should be sought.