Lorena A. Barba group

GPU computing

Rio Yokota, L Barba and Tsuyoshi Hamada, posing next to Degima, the do-it-yourself GPU cluster in Nagasaki, 2010

Rio Yokota, L Barba and Tsuyoshi Hamada, posing next to Degima, the do-it-yourself GPU cluster in Nagasaki, 2010

In the past few years, computational science has seen a paradigm shift in hardware architectures.  The IT industry, faced with a number of bottlenecks (memory, power, complexity), opted for on-chip parallelism and thus further increases in performance for simulation science will require parallel computing.

A compelling new trend is using graphics processors (GPUs) for scientific computing, perhaps the most exciting development since the debut of the Beowulf cluster in the ‘90s.

With this opportunity, however, comes the challenge of adapting our large toolbox of algorithms to the changes in computer architecture. There is continuing need for research into algorithms that exploit the new hardware, and we have been involved in this area since 2007.

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