Hardware technologies for high-performance data-intensive computing

被引：33

作者：

Gokhale, Maya ^{[1
]}

Cohen, Jonathan ^{[1
]}

Yoo, Andy ^{[1
]}

Miller, W. Marcus ^{[1
]}

Jacob, Arpith ^{[2
]}

Ulmer, Craig ^{[3
]}

Pearce, Roger ^{[4
]}

机构：

[1] Lawrence Livermore Natl Lab, Livermore, CA 94551 USA

[2] Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 USA

[3] Sandia Natl Labs, Livermore, CA 94550 USA

[4] Texas A&M Univ, Dept Comp Sci, College Stn, TX 77843 USA

来源：

COMPUTER | 2008年 / 41卷 / 04期

关键词：

D O I：

10.1109/MC.2008.125

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Data-intensive problems challenge conventional computing architectures with demanding CPU, memory, and I/O requirements. Using benchmarks that draw on three data types - scientific imagery, unstructured text, and semantic graphs representing networks of relationships - the authors demonstrate that emerging hardware technologies to augment traditional microprocessor-based computing systems can deliver 2 to 17 times the performance of general-purpose computers on a wide range of data-intensive applications by increasing compute cycles and bandwidth and reducing latency.

引用

页码：60 / +

页数：10

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