An effective matrix compression method for GPU-accelerated thermal analysis

VLSI Design, Automation and Test(VLSI-DAT) Pub Date : 2015-04-27 DOI:10.1109/VLSI-DAT.2015.7114505

L. Chiou, L. Lu, Chieh-Yu Lin

引用次数: 0

Abstract

Three-dimensional integrated circuits are expected to face increasingly severe thermal challenges and cost issues as the number of stacked ICs increases. Thermal analysis for 3D ICs is urgently required to assist system designers at the early phase of design to identify hot zones. Most thermal analyses obtain detailed temperature distribution by large matrix operations, and hence reduce analysis performance. Accordingly, we propose a compressed and combined sparse row (CCSR) matrix format to be used in the proposed effective matrix compression (EMC) method for matrix multiplication on GPU. The experimental results show EMC using CCSR is on average 44.93 times faster than matrix multiplication without any special compression format and on average at least 3.09 times faster than other compression formats.

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一种用于gpu加速热分析的有效矩阵压缩方法

随着堆叠集成电路数量的增加，三维集成电路预计将面临日益严峻的热挑战和成本问题。迫切需要对3D集成电路进行热分析，以帮助系统设计师在设计的早期阶段识别热区。大多数热分析通过大矩阵运算获得详细的温度分布，从而降低了分析性能。在此基础上，提出了一种压缩组合稀疏行(CCSR)矩阵格式，用于GPU上矩阵乘法的有效矩阵压缩(EMC)方法。实验结果表明，采用CCSR的电磁兼容比没有特殊压缩格式的矩阵乘法平均快44.93倍，比其他压缩格式平均快3.09倍以上。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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VLSI Design, Automation and Test(VLSI-DAT)

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