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Extreme Data-Intensive Scientific Computing

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1 Author(s)
Alex Szalay ; The Johns Hopkins University

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.

Published in:

Computing in Science & Engineering  (Volume:13 ,  Issue: 6 )