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Hardware Technologies for High-Performance Data-Intensive Computing
April 2008 (vol. 41 no. 4)
pp. 60-68
Maya Gokhale, Lawrence Livermore National Laboratory
Jonathan Cohen, Lawrence Livermore National Laboratory
Andy Yoo, Lawrence Livermore National Laboratory
W. Marcus Miller, Lawrence Livermore National Laboratory
Arpith Jacob, Washington University in St. Louis
Craig Ulmer, Sandia National Laboratories
Roger Pearce, Texas A&M University
Data-intensive problems challenge conventional computing architectures with demanding CPU, memory, and I/O requirements. Experiments with three benchmarks suggest that emerging hardware technologies can significantly boost performance of a wide range of applications by increasing compute cycles and bandwidth and reducing latency.

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Index Terms:
data management, data science, GPUs, FPGAs, solid-state storage, semantic graphs, image resampling, language analysis, computer systems, data-intensive computing
Citation:
Maya Gokhale, Jonathan Cohen, Andy Yoo, W. Marcus Miller, Arpith Jacob, Craig Ulmer, Roger Pearce, "Hardware Technologies for High-Performance Data-Intensive Computing," Computer, vol. 41, no. 4, pp. 60-68, April 2008, doi:10.1109/MC.2008.125
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