The Parallel Universe Magazine


 

Issue 37

Leadership Performance with 2nd Generation Intel® Xeon® Scalable Processors: New Features & Tools to Maximize Your HPC, AI, and Analytics Applications

Meet the new 2nd generation Intel® Xeon® Scalable processor family that’s already set 95 performance world records with new features including Intel® Deep Learning Boost for AI deep learning inference acceleration and support for Intel® Optane™ DC persistent memory for data centers.

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Parallel Universe Magazine - Issue 37, July 2019

Contents:

  • Letter from the Editor: Black Holes and High-Performance Computing by Henry A. Gabb, Senior Principal Engineer, Intel Corporation
     
  • Leadership Performance with 2nd-Generation Intel® Xeon® Scalable Processors: New Features and Tools to Maximize Your HPC, AI, and Analytics Applications by Amarpal S. Kapoor, Technical Consulting Engineer; Rama Kishan V. Malladi, Performance Modeling Engineer; and Avinash Karani and Nitya Hariharan, Application Engineers; Intel Corporation
     
  • Using the Latest Performance Analysis Tools to Prepare for Intel® Optane™ DC Persistent Memory: Getting Past Bottlenecks and Storage Issues by Jackson Marusarz, Technical Consulting Engineer, and Kevin O’Leary, Senior Technical Consulting Engineer, Intel Corporation
     
  • Measuring the Impact of NUMA Migrations on Performance: Weighing the Tradeoffs to Maximize Performance by Gurbinder Gill, Graduate Research Assistant, University of Texas at Austin, and Ramesh V. Peri, Senior Principal Engineer, Intel Corporation
     
  • Parallelism in Python: Directing Vectorization with NumExpr*: Boosting Performance for Computing with Arrays and Numerical Expressions by Fabio Baruffa, PhD, Technical Consulting Engineer, Intel Corporation
     
  • Turbo-Charged Open Shading Language on Intel® Xeon® Processors with Intel® Advanced Vector Extensions 512: Up to 2x Faster Full Renders Speed Digital Content Creation by Steena Monteiro, Software Engineer, and Alex M. Wells, Principal Engineer, Intel Corporation
     
  • The Performance Optimization and Productivity (PoP) Project: Pursuing the Never-Ending Quest for Performance by Mike Croucher, Developer Advocate, Numerical Algorithms Group (NAG)
     
  • Seven Ways HPC Software Developers Can Benefit from Intel® Software Investments: Taking Another Look at Intel and HPC Software by James Reinders, Editor Emeritus, The Parallel Universe
     

Parallel Universe Magazine - Issue 36, April 2019

Contents:

  • Letter from the Editor: Onward to Exascale Henry A. Gabb, Senior Principal Engineer, Intel Corporation
     
  • Effectively Train and Execute Machine Learning and Deep Learning Projects on CPUs Nathan Greeneltch and Jing Xu, Software Technical Consulting Engineers, Intel Corporation
     
  • Parallelism in Python* Using Numba* David Liu, Software Technical Consulting Engineer, Intel Corporation
     
  • Boosting the Performance of Graph Analytics Workloads Stijn Eyerman, Wim Heirman, and Kristof Du Bois, Research Scientists, and Joshua B. Fryman and Ibrahim Hur, Principal Engineers, Intel Corporation
     
  • How Effective is Your Vectorization? Kevin O’Leary, Technical Consulting Engineer, Intel Corporation
     
  • Improving Performance using Vectorization for Particle-in-Cell Codes Bei Wang, HPC Software Engineer, Princeton University; Carlos Rosales-Fernandez, Software Technical Consulting Engineer, Intel Corporation; and William Tang, Professor, Princeton Plasma Physics Laboratory
     
  • Boost Performance for Hybrid Applications with Multiple Endpoints in Intel® MPI Library Rama Kishan Malladi, Graphics Performance Modeling Engineer, and Dr. Amarpal Singh Kapoor, Technical Consulting Engineer, Intel Corporation
     
  • Innovate System and IoT Apps Ramya Chandrasekaran and Thorsten Moeller, Product Marketing Engineers, Intel Corporation
     

Parallel Universe Magazine - Issue 35, January 2019

Contents:

  • Letter from the Editor: Happy New Year...and May 2019 Bring You High Performance by Henry A. Gabb, Senior Principal Engineer, Intel Corporation
     
  • Intel® Rendering Framework Using Software-Defined Visualization by Rob Farber, Global Technology Consultant, TechEnablement
    Why Intel® Xeon® processors excel at visualization
     
  • Unifying AI, Analytics, and HPC on a Single Cluster by Allene Bhasker and Keith Mannthey, Solution Architects, Data Center Group, Intel Corporation
    Maximizing efficiency and lowering costs for tomorrow's enterprise
     
  • Advancing OpenCL™ for FPGAs by Martin C. Herbordt, Professor, Department of Electrical and Computer Engineering, Boston University
    Boosting performance with Intel® FPGA SDK for OpenCL™ software technology
     
  • Parallelism in Python* by David Liu, Software Technical Consulting Engineer, and Anton Malakhov, Software Development Engineer, Intel Corporation
    Dispelling the myths with tools to achieve parallelism
     
  • Remove Memory Bottlenecks Using Intel® Advisor by Kevin O’Leary and Alex Shinsel, Technical Consulting Engineers, Intel Corporation
    Understanding how your program is accessing memory helps you get more from your hardware
     
  • MPI-3 Non-Blocking I/O Collectives in Intel® MPI Library by Nitya Hariharan, Amarpal Singh Kapoor, and Rama Kishan Malladi, Technical Marketing Engineers, Core and Visual Computing Group, Intel Corporation; Md Vasimuddin, Research Scientist, Parallel Computing Lab, Intel Labs
    Speeding up I/O for HPC applications

Parallel Universe issue 34 cover

Parallel Universe Magazine - Issue 34, October 2018

Contents:

  • Letter from the Editor: Edge-to-Cloud Heterogeneous Parallelism with openVINO™ Toolkit by Henry A. Gabb
     
  • OpenVINO ToolKit and FPGAs by James Reinders
    A look at the FPGA targeting of this versatile visual computing toolkit.
     
  • Floating-Point Reproducibility in Intel® Software Tools by Martyn Corden, Xiaoping Duan, and Barbara Perz
    Getting beyond the uncertainty.
     
  • Comparing C++ Memory Allocation Libraries by Rama Kishan Malladi and Nikhil Prasad
    Boosting performance with better dynamic memory allocation.
     
  • LIBXSMM*: An Open Source-Based Inspiration for Hardware and Software Development at Intel by Hans Pabst, Greg Henry, and Alexander Heinecke
    Meet the library that targets Intel® architecture for specialized dense and sparse matrix operations.

  • Advancing the Performance of Astrophysics Simulations with ECHO-3DHPC by Matteo Bugli, Luigi Iapichino, and Fabio Baruffa
    Using the latest Intel® Software Development Tools to make more efficient use of hardware.
     
  • Your Guide to Understanding System Performance by Bhanu Shankar and Munara Tolubaeva
    Meet the Platform Profiler in Intel® VTune™ Amplifier.

Parallel Universe Magazine - Issue 33, July 2018

Contents:

  • Letter from the Editor: What's the Big Deal about BigDL? by Henry A. Gabb
     
  • Advancing Artificial Intelligence on Apache Spark* with BigDL by Jason Dai and Radhika Rangarajan
    Features, use-cases, and the future.
     
  • Why WebAssembly Is the Future of Computing on the Web by Rich Winterton, Deepti Aggarwal, Tuyet-Trang (Snow), Lam Piel, Brittney Coons, and Nathan Johns
    The history and new direction of processing in the browser.
     
  • Code Modernization in Action: Threading, Memory, and Vectorization Optimizations by Dmitry Prohorov, Cedric Andreolli, and Philippe Thierry
    Using the latest Intel® Software Development Tools to make more efficient use of hardware.
     
  • In-Persistent Memory Computing with Java* by Eric Kaczmarek and Preetika Tyagi
    The key to adaptability in modern application programming.

  • Faster Gradient-Boosting Decision Trees by Ying Hu, Oleg Kremnyov, and Ivan Kuzmin
    How to lift machine learning performance using Intel® Data Analytics Acceleration Library (Intel® DAAL).
     
  • Hiding Communication Latency Using MPI-3 Non-Blocking Collectives by Amarpal Singh Kapoor, Rama Kishan Malladi, Nitya Hariharan, and Srinivas Sridharan
    Improving HPC performance by overlapping communication and computation.

Parallel Universe Magazine - Issue 32, March 2018

Contents:

  • Letter from the Editor: Computer Vision Coming Soon to a Browser Near You by Henry A. Gabb
     
  • Computer Vision for the Masses by Sajjad Taheri, Alexeandru Nicolau, Alexeander Vedienbaum, Ningxin Hu, and Mohammad Reza Haghighat
    Bringing computer vision to the Open Web Platform*.
     
  • Up Your Game by Giselle Gomez
    How to optimize your game development―no matter what your role―using Intel® Graphics Performance Analyzers.
     
  • Harp-DAAL for High-Performance Big Data Computing by Judy Qiu
    The key to simultaneously boosting productivity and performance.
     
  • Understanding the Instruction Pipeline by Alex Shinsel
    The key to adaptability in modern application programming,
     
  • Parallel CFD with the HiFUN* Solver on the Intel® Xeon® Scalable Processor by Rama Kishan Malladi, S.V. Vinutha, and Austin Cherian
    Maximizing HPC platforms for fast numerical simulations.
     
  • Improving VASP* Materials Simulation Performance by Fedor Vasilev, Dmitry Sivkov, and Jeongnim Kim
    Using the latest Intel® Software Development Tools to make more efficient use of hardware.

Parallel Universe Magazine - Issue 31, January 2018

Contents:

  • Letter from the Editor: Happy New Year, Happy Parallel Computing, by Henry A. Gabb
    Henry A. Gabb is a longtime high-performance and parallel computing practitioner who has published numerous articles on parallel programming.
     
  • FPGA Programming with the OpenCL™ Platform, by James Reinders and Tom Hill
    Knowing how to program an FPGA is a skill you need―and here’s how to start.
     
  • Accelerating the Eigen Math Library for Automated Driving Workloads, by Steena Monteiro and Gaurav Bansal
    Meeting the need for speed with Intel® Math Kernel Library.
     
  • Speeding Algebra Computations with the Intel® Math Kernel Library Vectorized Compact Matrix Functions, by Kirana Bergstrom, Eugene Chereshnev, and Timothy B. Costa
    Maximizing the performance benefits of the compact data layout.
     
  • Boosting Java* Performance in Big Data Applications, by Kumar Shiv and Rahul Kandu
    How new enhancements enable faster and better numerical computing.
     
  • Gaining Performance Insights Using the Intel® Advisor Python* API, by Kevin O’Leary and Egor Kazachkov
    Getting good data to make code tuning decisions.
     
  • Welcome to the Intel® AI Academy, by Niven Singh
    AI education for all.

Parallel Universe Magazine - Issue 30, October 2017

Contents:

  • Letter from the Editor: Meet Intel® Parallel Studio XE 2018, by Henry A. Gabb
    Henry A. Gabb is a long-time high-performance and parallel computing practitioner and has published numerous articles on parallel programming.
     
  • Driving Code Performance with Intel® Advisor’s Flow Graph Analyzer, by Vasanth Tovinkere, Pablo Reble, Farshad Akhbari, and Palanivel Guruvareddiar
    Optimizing performance for an autonomous driving application.
     
  • Welcome to the Adult World, OpenMP*, by Barbara Chapman
    After 20 years, it’s more relevant than ever.
     
  • Enabling FPGAs for Software Developers, by Bernhard Friebe, and James Reinders
    Boosting efficiency and performance for automotive, networking, and cloud computing.
     
  • Modernize Your Code for Performance, Portability, and Scalability, by Jackson Marusarz
    What’s new in Intel® Parallel Studio XE.
     
  • Dealing with Outliers, by Oleg Kremnyov, Mikhail Averbukh, and Ivan Kuzmin
    How to find fraudulent transactions in a real-world dataset.
     
  • Tuning for Success with the Latest SIMD Extensions and Intel® Advanced Vector Extensions 512, by Xinmin Tian, Hideki Saito, Sergey Kozhukhov, and Nikolay Panchenko
    Best practices for taking advantage of the latest architectural features.
     
  • Effectively Using Your Whole Cluster, by Rama Kishan Malladi
    Optimizing SPECFEM3D_GLOBE* performance on Intel® architecture.
     
  • Is Your Cluster Healthy?, by Brock A. Taylor
    Must-have cluster diagnostics in Intel® Cluster Checker.
     
  • Optimizing HPC Clusters, by Michael Hebenstreit
    Enabling on-demand BIOS configuration changes in HPC clusters.

Parallel Universe Magazine - Issue 29, July 2017

Contents:

  • Letter from the Editor: Old and New, by Henry A. Gabb
    Henry A. Gabb is a longtime high-performance and parallel computing practitioner and has published numerous articles on parallel programming.
     
  • Tuning Autonomous Driving Using Intel® System Studio, by Lavanya Chockalingam
    Intel® GO™ Automotive SDK offers automotive solution developers an integrated solutions environment.
     
  • OpenMP* Is Turning 20!, by Bronis R. de Supinski
    Making parallel programming accessible to C/C++ and Fortran programmers.
     
  • Julia*: A High-Level Language for Supercomputing, by Ranjan Anantharaman, Viral Shah, and Alan Edelman
    The Julia Project continues to break new boundaries in scientific computing.
     
  • Vectorization Becomes Important—Again, by Robert H. Dodds Jr.
    Open source code WARP3D exemplifies renewed interest in vectorization.
     
  • Building Fast Data Compression Code for Cloud and Edge Applications, by Chao Yu and Sergey Khlystov
    How to optimize your compression with Intel® Integrated Performance Primitives (Intel® IPP).
     
  • MySQL* Optimization with Intel® C++ Compiler, by Huixiang Tao, Ying Hu, and Ming Gao
    Leverage MySQL* to deliver peak service.
     
  • Accelerating Linear Regression in R* with Intel® DAAL, by Steena Monteiro and Shaojuan Zhu
    Make better predictions with this highly optimized open source package.

Parallel Universe Magazine - Issue 28, April 2017

Contents:

  • Letter from the Editor: Parallel Languages, Language Extensions, and Application Frameworks, by Henry A. Gabb
    Henry A. Gabb is a long-time high-performance and parallel computing practitioner and has published numerous articles on parallel programming.
     
  • Parallel STL: Boosting Performance of C++ STL Code, by Vladimir Polin and Mikhail Dvorskiy
    C++ and the evolution toward natively parallel languages.
     
  • Happy 20th Birthday, OpenMP*, by Rob Farber
    Making parallel programming accessible to C/C++ and Fortran programmers—and providing a software path to exascale computation.
     
  • Solving Real-World Machine Learning Problems with Intel® Data Analytics Acceleration Library, by Oleg Kremnyov, Ivan Kuzmin, and Gennady Fedorov
    Models are put to the test in Kaggle* competitions.
     
  • HPC with R*: The Basics, by Drew Schmidt
    Satisfying the need for speed in data analytics,
     
  • BigDL: Optimized Deep Learning on Apache Spark*, by Jason Dai and Radhika Rangarajan
    Making deep learning more accessible,

Parallel Universe Magazine - Issue 27, January 2017

Contents:

  • Letter from the Editor: The Changing HPC Landscape Still Looks the Same, by Henry A. Gabb
    Henry A. Gabb is a long-time high-performance and parallel computing practitioner and has published numerous articles on parallel programming.
     
  • The Present and Future of the OpenMP* API Specification, by Michael Klemm, Alejandro Duran, Ravi Narayanaswamy, Xinmin Tian, and Terry Wilmarth
    How the gold standard parallel programming language has improved with each new version.
     
  • Reducing Packing Overhead in Matrix-Matrix Multiplication, by Kazushige Goto, Murat Efe Guney, and Sarah Knepper
    Improve performance on multicore and many-core Intel® architectures, particularly for deep neural networks.
     
  • Identify Scalability Problems in Parallel Applications, by Vladimir Tsymbal
    How to improve scalability for Intel® Xeon® and Intel® Xeon Phi™ Processors using new Intel® VTune™ Amplifier memory analysis.
     
  • Vectorization Opportunities for Improved Performance with Intel® AVX-512, by Martyn Corden
    Examples of how Intel® Compilers can vectorize and speed up loops.
     
  • Intel® Advisor Roofline Analysis, by Kevin O’Leary, Ilyas Gazizov, Alexandra Shinsel, Zakhar Matveev, and Dmitry Petunin
    A new way to visualize performance optimization trade-offs.
     
  • Intel-Powered Deep Learning Frameworks, by Pubudu Silva
    Your path to deeper insights.

Parallel Universe Magazine - Issue 26, October 2016

Contents:

  • Letter from the Editor: What Will Machines Learn from You?, by Mike Lee
     
  • Modernize Your Code for Intel® Xeon Phi™ Processors, by Yolanda Chen and Udit Patidar
    Explore new Intel® Parallel Studio XE 2017 capabilities
     
  • Unleash the Power of Big Data Analytics and Machine Learning, by Vadim Pirogov, Ivan Kuzmin, and Sarah Knepper
    Solve big data era application challenges with Intel® Performance Libraries.
     
  • Overcome Python* Performance Barriers for Machine Learning, by Vasily Litvinov, Viktoriya Fedotova, Anton Malakhov, Aleksei Fedotov, Ruslan Israfilov, and Christopher Hogan
    Accelerate and optimize Python* machine learning applications.
     
  • Profiling Java* and Python* Code using Intel® VTune™ Amplifier, by Sukruv Hv
    Get more CPU capability for Java*- and Python*-based applications
     
  • Lightning-Fast R* Machine Learning Algorithms, by Zhang Zhang
    Get results with the Intel® Data Analytics Acceleration Library and the latest Intel® Xeon Phi™ processor
     
  • A Performance Library for Data Analytics and Machine Learning, by Shaojuan Zhu
    See how the Intel® Data Analytics Acceleration Library impacts C++ coding for handwritten digit recognition.
     
  • MeritData Speeds Up its Tempo* Big Data Platform Using Intel® High-Performance Libraries, by Jin Qiang, Ying Hu, and Ning Wang
    Case study finds performance improvements and potential for big data algorithms and visualization.

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The benchmark results reported above may need to be revised as additional testing is conducted. The results depend on the specific platform configurations and workloads utilized in the testing, and may not be applicable to any particular user’s components, computer system, or workloads. The results are not necessarily representative of other benchmarks and other benchmark results may show greater or lesser impact from mitigations.

Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more information, see Performance Benchmark Test Disclosure.