high performance computing on graphics processing units: hgpu.org

This paper reports on our experience with a heterogeneous cluster execution environment, in which a distributed parallel application utilizes two types of compute devices: those employing general-purpose processors, and those based on computing accelerators known as Intel Xeon Phi coprocessors.

Unlike general-purpose graphics processing units (GPGPUs), Intel Xeon Phi coprocessors are able to execute native applications. In this mode, the application runs in the coprocessor’s operating system, and does not require a host process executing on the CPU and offloading data to the accelerator (coprocessor). Therefore, for an application in the MPI framework, it is possible to run MPI processes directly on coprocessors. In this case, coprocessors behave like independent compute nodes in the cluster, with an MPI rank, peer-to-peer communication capability, and access to a network-shared file system. With such configuration, there is no need to instrument data offload in the application in order to utilize a heterogeneous system comprised of processors and coprocessors. That said, an MPI application designed for a CPU-only cluster can be used on coprocessor-enabled clusters without code modification.

We discuss the issues of portable code design, load balancing and system configuration (networking and MPI) necessary in order for such a setup to be efficient. An example application used for this study carries out a Monte Carlo simulation for Asian option pricing. The paper includes the performance metrics of this application with CPU-only and heterogeneous cluster configurations.