LARGE-SCALE VISUALIZATION OF SPARSE MATRICES

被引：2

作者：

Langr, D. ^{[1
]}

Simecek, I. ^{[1
]}

Tvrdiki, P. ^{[1
]}

Dytrych, T. ^{[2
]}

机构：

[1] Czech Tech Univ, Fac Informat Technol, Dept Comp Syst, Thakurova 9, Prague 16000, Czech Republic

[2] Louisiana State Univ, Dept Phys & Astron, Baton Rouge, LA 70803 USA

来源：

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE | 2014年 / 15卷 / 01期

基金：

美国国家科学基金会;

关键词：

visualization; sparse matrices; parallel system; distributed algorithm; data acquisition;

D O I：

10.12694/scpe.v15i1.963

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

An efficient algorithm for parallel acquisition of visualization data for large sparse matrices is presented and evaluated both analytically and empirically. The algorithm was designed to be application-independent, i.e., it works with any matrix-processors mapping and with any sparse storage format/scheme. The empirical scalability study of the algorithm was carried on using multiple modern HPC systems. In our largest experiment, we utilized 262144 processors for 73 seconds to gather and store to a file the visualization data for a matrix with 1.17 . 10(13) nonzero elements. Using the proposed algorithm, one can thus visualize large sparse matrices with a minimal runtime overhead imposed on executed HPC codes.

引用

页码：21 / 31

页数：11

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