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Titlebook: Euro-Par 2012: Parallel Processing Workshops; BDMC, CGWS, HeteroPa Ioannis Caragiannis,Michael Alexander,Josef Weiden Conference proceeding

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樓主: 劉興旺
51#
發(fā)表于 2025-3-30 09:07:20 | 只看該作者
OpenCL - Supporting Distributed Heterogeneous Computing in HPC Clustersl (homogeneous) cluster computing, allowing to leverage the performance of parallel applications. In this paper we introduce .OpenCL, a platform that supports the simple deployment and efficient running of OpenCL-based parallel applications that may span several cluster nodes, expanding the original
52#
發(fā)表于 2025-3-30 12:32:57 | 只看該作者
Mastering Software Variant Explosion for GPU Acceleratorstems hosting accelerators like graphics cards. While algorithm developers have profound knowledge of the application domain, they often lack detailed insight into the underlying hardware of accelerators in order to exploit the provided processing power. Therefore, this paper introduces a rule-based,
53#
發(fā)表于 2025-3-30 19:48:47 | 只看該作者
54#
發(fā)表于 2025-3-30 21:42:08 | 只看該作者
Weighted Block-Asynchronous Iteration on GPU-Accelerated Systemseighting techniques similar to those applied in block-smoothers for multigrid methods. For test matrices taken from the University of Florida Matrix Collection we report the convergence behavior and the total runtime for the different techniques. Analyzing the results, we observe that using weights
55#
發(fā)表于 2025-3-31 04:07:13 | 只看該作者
An Optimized Parallel IDCT on Graphics Processing Unitsing OpenCL. By exploiting that most of the input data of the IDCT for real videos are zero valued coefficients a new compacted data representation is created that allows for several optimizations. Experimental evaluations conducted on different GPUs show average speedups from 1.7× to 7.4× compared t
56#
發(fā)表于 2025-3-31 08:29:19 | 只看該作者
Multi-level Parallelization of Advanced Video Coding on Hybrid CPU+GPU Platformselized on both the CPU and the GPU, and a computationally efficient model is proposed to dynamically distribute the computational load among these processing devices on hybrid platforms. The presented model includes both dependency aware task scheduling and load balancing algorithms. According to th
57#
發(fā)表于 2025-3-31 10:33:51 | 只看該作者
https://doi.org/10.1007/978-3-642-36949-0GPU; big data; cloud computing; grids; high-performance computing; algorithm analysis and problem complex
58#
發(fā)表于 2025-3-31 16:08:59 | 只看該作者
59#
發(fā)表于 2025-3-31 21:05:16 | 只看該作者
,Die hellenistische und r?mische Kunst,ng from adaptive and on-demand fault-tolerance to new fault-tolerance models. However, realistic benchmarks are still missing to analyze and compare the effectiveness of these proposals. To date, most MapReduce fault-tolerance solutions have been evaluated using microbenchmarks in an ad-hoc and over
60#
發(fā)表于 2025-3-31 22:01:29 | 只看該作者
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