压缩矩阵计算
Compressed Matrix Computations
摘要：
出于环境原因，节约计算正在成为一个重要的主题。在这种情况下，已经提出了几种技术，通过专门为浮点数数组定制的专用压缩方法来减少科学数据的存储。虽然这些技术在节省内存方面非常有效，但它们引入了额外的计算，以便在处理数据之前压缩和重定向数据。在这篇文章中，我们介绍了一种新的有损，固定速率压缩技术的二维数组的浮点数，它允许一个计算直接对压缩数据，而不解压。我们获得了重要的加速提升，因为在压缩数据中进行计算所需的操作更少，并且不需要解压缩和重新压缩。更确切地说，我们的技术使得有可能执行基本的线性代数运算，如加法，乘法压缩矩阵之间的常数和点积和部分未压缩矩阵之间的矩阵乘法。这项工作已被实施到一个名为blaz的工具，并与著名的压缩器zfp在执行时间和精度方面的比较。
英文摘要：
Frugal computing is becoming an important topic for environmental reasons. In this context, several techniques have been proposed to reduce the storage of scientific data by dedicated compression methods specially tailored for arrays of floating-point numbers. While these techniques are quite efficient to save memory, they introduce additional computations to compress and decompress the data before processing them. In this article, we introduce a new lossy, fixed-rate compression technique for 2D-arrays of floating-point numbers which allows one to compute directly on the compressed data, without decompressing them. We obtain important speedups since less operations are needed to compute among the compressed data and since no decompression and re-compression is needed. More precisely, our technique makes it possible to perform basic linear algebra operations such as addition, multiplication by a constant among compressed matrices and dot product and matrix multiplication among partly uncompressed matrices. This work has been implemented into a tool named blaz and a comparison with the well-known compressor zfp in terms of execution-time and accuracy is presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
