GPU/Coprocessors

PBS Works supports scheduling to GPUs and coprocessors so users can take advantage of these powerful architectures to accelerate processing.

With the advent of the Graphical Processing Unit (GPU) as a general-purpose computing unit, more and more HPC users are moving toward GPU-based clusters to run scientific and engineering applications. This model allows users to use a CPU and GPU together in a heterogeneous computing model, where the sequential part of the application runs on the CPU and the computationally intensive part runs on the GPU. By exploiting the massive parallelism in GPUs, users can run applications almost forty percent faster, compared to the traditional CPU-based mode.
Quote
“At JAIST, it is imperative to provide our world-class scientists with the best available technology resources... The pioneering Cray XC30 supercomputer with PBS Professional [running Intel® Xeon Phi™ coprocessors, and NVIDIA® Tesla® GPU accelerators] will allow our users to expand the scope of their research efforts with a proven, well-integrated solution they can rely on.”
-Director of Research Center for Advanced Computing Infrastructure at JAIST

See Press Release

Although basic GPU scheduling will meet the needs of 95 percent of customers, PBS Professional also supports more advanced GPU scheduling: the ability for a job to separately allocate (request and/or identify) each individual GPU on a node. This capability is useful for sharing a single node among multiple jobs, where each job requires its own GPUs.

In addition to support for GPUs and other accelerators, PBS Professional also supports the Intel® Xeon Phi™ coprocessor which offers breakthrough performance capabilities for highly parallel applications. Altair was one of the first workload management companies to offer Xeon Phi support.

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