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
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  and OpenCL , are by nature designed to achieve effective collaboration of CPUs and GPUs.