The Parallel Universe Magazine


DPC++ for Intel® Processor Graphics Architecture: How to Offload Compute-Intensive Architecture to Intel® CPUs

Modern vector CPUs are practically accelerators. But many Intel® CPUs also have an integrated graphics processor that can be used for general-purpose computing. If you’re curious about offloading computations to this low-power accelerator, don’t miss this issue’s feature article for a step-by-step case study.

Read This Issue

Filtros aplicados

Contents: Letter from the Editor: The Parallel Universe Turns 10 by Henry A. Gabb, Senior Principal Engineer, Intel Corporation GPU-Quicksort: How to Move from OpenCL™ to Data Parallel C++ by Robert Ioffe, Senior Exascale Performance Software Engineer, Intel Corporation Optimizing the Performance of oneAPI Applications: Getting the Most from this Unified, Standards-Based Programming Model by Kevin O’Leary, Software Technical Consulting Engineer, Intel Corporation Speeding Up Monte Carlo Simulation with Intel® oneMKL: Intel® oneAPI Math Kernel Library (Beta) Data Parallel C++ Usage Models by Alina Elizarova and Pavel Dyakov, Math Algorithm Engineers, and Gennady Fedorov, Software Technical Consulting Engineer, Intel Corporation Bringing Accelerated Analytics at Scale to Intel® Architecture: Unifying Data Science with Traditional Analytics on Modern Hardware by Venkat Krishnamurthy, Product Vice President, and Kathryn Vandiver, Senior Director, Platform and Core Engineering, OmniSci A New Approach to Parallel Computing Using Automatic Differentiation: Getting Top Performance on Modern Multicore Systems by Dmitri Goloubentsev, Head of Automatic Adjoint Differentiation, Matlogica, and Evgeny Lakshtanov, Principal Researcher, Department of Mathematics, University of Aveiro, Portugal and Matlogica LTD 8 Rules for Parallel Programming for Multicore: There are Some Consistent Rules that can Help you Solve the Parallelism Challenge and Tap Into the Potential of Multicore by James Reinders, Founding Editor and Editor Emeritus of The Parallel Universe Book Review: The OpenMP Common Core Making OpenMP Simple Again by Ruud van der Pas, Senior Principal Software Engineer, Oracle Corporation

Featured: Heterogeneous Programming Using oneAPI

Featured: Accelerating XGBoost* for Intel® Xeon® Processors

Featured: Leadership Performance with 2nd-Generation Intel® Xeon® Scalable Processors

Featured: Effectively Train and Execute Machine Learning and Deep Learning Projects on CPUs

Featured: Intel® Rendering Framework Using Software-Defined Visualization

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.

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.

Featured: Computer Vision for the Masses

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.

Featured: Driving Code Performance with the Intel® Advisor Flow Graph Analyzer

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.