In this paper, we propose an OpenCL framework for heterogeneous CPU/GPU clusters, and show that the framework achieves both high performance and ease of programming. The framework provides an illusion of a single system for the user. It allows the application to utilize multiple heterogeneous compute devices, such as multicore CPUs and GPUs, in a remote node as if they were in a local node. No communication API, such as the MPI library, is required in the application source. We implement the OpenCL framework and evaluate its performance on a heterogeneous CPU/GPU cluster that consists of one host node and nine compute nodes using eleven OpenCL benchmark applications.
OpenCL is an open standard to write parallel applications for heterogeneous computing systems. Since its usage is restricted to a single operating system instance, programmers need to use a mix of OpenCL and MPI to program a heterogeneous cluster. In this paper, we introduce an MPI-OpenCL implementation of the LINPACK benchmark for a cluster with multi-GPU nodes. The LINPACK benchmark is one of the most widely used benchmark applications for evaluating high performance computing systems. Our implementation is based on High Performance LINPACK (HPL) and uses the blocked LU decomposition algorithm. We address that optimizations aimed at reducing the overhead of CPUs are necessary to overcome the performance gap between the CPUs and the multiple GPUs. Our LINPACK implementation achieves 93.69 Tflops (46 percent of the theoretical peak) on the target cluster with 49 nodes, each node containing two eight-core CPUs and four GPUs.
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