High Performance Computing on Vector Systems 2011 by Yoshiei Sato, Akihiro Musa, Ryusuke Egawa, Hiroyuki

By Yoshiei Sato, Akihiro Musa, Ryusuke Egawa, Hiroyuki Takizawa, Koki Okabe (auth.), Michael Resch, Xin Wang, Wolfgang Bez, Erich Focht, Hiroaki Kobayashi, Sabine Roller (eds.)

The e-book provides the cutting-edge in excessive functionality computing and simulation on glossy supercomputer architectures. It covers tendencies in and software program improvement as a rule and particularly the way forward for vector-based structures and heterogeneous architectures. the applying contributions disguise computational fluid dynamics, fabric technological know-how, scientific purposes and weather study. cutting edge fields like coupled multi-physics or multi-scale simulations are awarded. All papers have been selected from displays given on the thirteenth Teraflop Workshop held in October 2010 at Tohoku college, Japan.

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Additional info for High Performance Computing on Vector Systems 2011

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However, the high development effort for a scalable parallel application has been a major drawback towards the efficient application of HPC to the applications designed for a serial execution only. The Message-Passing Interface (MPI) is a well-known programming standard for large-scale parallel applications development. However, MPI has found its most wide use in the applications written in C and Fortran. We show how MPI can be beneficially applied for the parallelization of the Java applications as well.

A) An OpenCL C code. (b) An automatically-generated C++ code. Fig. 2 An example of OpenCL-to-C translation. The body of a kernel function is divided into two partitions. Then, each partition is translated to a thread loop. (a) An OpenCL C code (b) An automatically-generated C++ code For example, in the kernel code of CP, double-precision floating-point (doubletype) values are stored in a float-type variable. In this case, unnecessary type casts are inserted to a thread loop and inhibit the compiler from vectorizing the loop.

So far, a standard way to program their collaboration has not been established yet. In addition to general-purpose scalar processors (CPUs), graphics processing units (GPUs) are recently used as accelerators for fine-grain data-parallel workloads. High-performance computing with GPUs is called GPU computing. Since a GPU is not a general-purpose processor, an external CPU fully controls the GPU. Therefore, programming frameworks for GPU computing, such as CUDA [3] and OpenCL [9], are by nature designed to achieve effective collaboration of CPUs and GPUs.

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