Extreme Data-Intensive Scientific Computing

Authors:
Alex Szalay
Affiliations:
The Johns Hopkins University
Venue:
Computing in Science and Engineering
Year:
2011

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Abstract

Scientific computing increasingly involves massive data; in astronomy, observations and numerical simulations are on the verge of generating petabytes. This new, data-centric computing requires a new look at computing architectures and strategies. Using Amdahl's law to characterize architectures and workloads, it's possible to use existing commodity parts to build systems that approach an ideal Amdahl machine.