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This edited book aims to present the state of the art in research and development of the convergence of high-performance computing and parallel programming for various engineering and scientific applications. The book has consolidated algorithms, techniques, and methodologies to bridge the gap between the theoretical foundations of academia and implementation for research, which might be used in business and other real-time applications in the future.The book outlines techniques and tools used for emergent areas and domains, which include acceleration of large-scale electronic structure simulations with heterogeneous parallel computing, characterizing power and energy efficiency of a data-centric high-performance computing runtime and applications, security applications of GPUs, parallel implementation of multiprocessors on MPI using FDTD, particle-based fused rendering, design and implementation of particle systems for mesh-free methods with high performance, and evolving topics on heterogeneous computing. In the coming days the need to converge HPC, IoT, cloud-based applications will be felt and this volume tries to bridge that gap.
Information technology. --- Parallel processing (Electronic computers) --- Parallel programming (Computer science) --- Computer programming --- High performance computing --- Multiprocessors --- Supercomputers --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Physical Sciences --- Engineering and Technology --- Computer and Information Science --- Distributed Computing --- Computer Science and Engineering
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XcalableMP is a directive-based parallel programming language based on Fortran and C, supporting a Partitioned Global Address Space (PGAS) model for distributed memory parallel systems. This open access book presents XcalableMP language from its programming model and basic concept to the experience and performance of applications described in XcalableMP. XcalableMP was taken as a parallel programming language project in the FLAGSHIP 2020 project, which was to develop the Japanese flagship supercomputer, Fugaku, for improving the productivity of parallel programing. XcalableMP is now available on Fugaku and its performance is enhanced by the Fugaku interconnect, Tofu-D. The global-view programming model of XcalableMP, inherited from High-Performance Fortran (HPF), provides an easy and useful solution to parallelize data-parallel programs with directives for distributed global array and work distribution and shadow communication. The local-view programming adopts coarray notation from Coarray Fortran (CAF) to describe explicit communication in a PGAS model. The language specification was designed and proposed by the XcalableMP Specification Working Group organized in the PC Consortium, Japan. The Omni XcalableMP compiler is a production-level reference implementation of XcalableMP compiler for C and Fortran 2008, developed by RIKEN CCS and the University of Tsukuba. The performance of the XcalableMP program was used in the Fugaku as well as the K computer. A performance study showed that XcalableMP enables a scalable performance comparable to the message passing interface (MPI) version with a clean and easy-to-understand programming style requiring little effort.
Programming languages (Electronic computers). --- Programming Languages, Compilers, Interpreters. --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Electronic data processing --- Languages, Artificial --- Programming Languages, Compilers, Interpreters --- PGAS model --- Partitioned Global Address Space model --- Coarray --- parallel programming language --- high performance computing --- Open Access --- Programming & scripting languages: general --- Compilers & interpreters
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This open access book constitutes the refereed proceedings of the 7th Asian Conference Supercomputing Conference, SCFA 2022, which took place in Singapore in March 2022. The 8 full papers presented in this book were carefully reviewed and selected from 21 submissions. They cover a range of topics including file systems, memory hierarchy, HPC cloud platform, container image configuration workflow, large-scale applications, and scheduling.
Computer networking & communications --- Software Engineering --- Operating systems --- Computer architecture & logic design --- Network hardware --- cloud computing --- computer networks --- computer programming --- computer systems --- CUDA --- distributed computer systems --- gpu --- gpus --- hpc --- microprocessor chips --- mpi --- parallel algorithms --- parallel architectures --- parallel processing systems --- parallel programming --- programming languages --- signal processing --- telecommunication systems --- algorithms --- high performance computing
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Learn how to accelerate C++ programs using data parallelism. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. This book teaches data-parallel programming using C++ and the SYCL standard from the Khronos Group and walks through everything needed to use SYCL for programming heterogeneous systems. The book begins by introducing data parallelism and foundational topics for effective use of SYCL and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. You will learn: • How to accelerate C++ programs using data-parallel programming • How to target multiple device types (e.g. CPU, GPU, FPGA) • How to use SYCL and SYCL compilers • How to connect with computing’s heterogeneous future via Intel’s oneAPI initiative.
Programming languages (Electronic computers). --- Computer input-output equipment. --- Programming Languages, Compilers, Interpreters. --- Hardware and Maker. --- Computer hardware --- Computer I/O equipment --- Computers --- Electronic analog computers --- Electronic digital computers --- Hardware, Computer --- I/O equipment (Computers) --- Input equipment (Computers) --- Input-output equipment (Computers) --- Output equipment (Computers) --- Computer systems --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Electronic data processing --- Languages, Artificial --- Input-output equipment --- Programming Languages, Compilers, Interpreters --- Hardware and Maker --- Maker --- heterogenous --- FPGA programming --- GPU programming --- Parallel programming --- Data parallelism --- SYCL --- Intel One API --- Programming & scripting languages: general --- Compilers & interpreters --- Heterogeneous computing. --- C++ (Computer program language) --- OpenCL (Computer program language) --- Open CL (Computer program language) --- Open Computing Language (Computer program language) --- Programming languages (Electronic computers) --- Heterogeneous processing (Computers) --- High performance computing --- Parallel processing (Electronic computers)
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