Abstract. Many scientific codes consist of memory bandwidth bound kernels -the dominating factor of the runtime is the speed at which data can be loaded from memory into the Arithmetic Logic Units, before results are written back to memory. One major advantage of many-core devices such as General Purpose Graphics Processing Units (GPGPUs) and the Intel Xeon Phi is their focus on providing increased memory bandwidth over traditional CPU architectures. However, as with CPUs, this peak memory bandwidth is usually unachievable in practice and so benchmarks are required to measure a practical upper bound on expected performance.The choice of one programming model over another should ideally not limit the performance that can be achieved on a device. GPU-STREAM has been updated to incorporate a wide variety of the latest parallel programming models, all implementing the same parallel scheme. As such this tool can be used as a kind of Rosetta Stone which provides both a cross-platform and cross-programming model array of results of achievable memory bandwidth.

Abstract-Although the OpenMP 4.0 standard has been available since 2013, support for GPUs has been absent up until very recently, with only a handful of experimental compilers available. In this work we evaluate the performance of Cray's new NVIDIA GPU targeting implementation of OpenMP 4.0, with the mini-apps TeaLeaf, CloverLeaf and BUDE. We successfully port each of the applications, using a simple and consistent design throughout, and achieve performance on an NVIDIA K20X that is comparable to Cray's OpenACC in all cases. BUDE, a compute bound code, required 2.2x the runtime of an equivalently optimised CUDA code, which we believe is caused by an inflated frequency of control flow operations and less efficient arithmetic optimisation. Impressively, both TeaLeaf and CloverLeaf, memory bandwidth bound codes, only required 1.3x the runtime of hand-optimised CUDA implementations. Overall, we find that OpenMP 4.0 is a highly usable open standard capable of performant heterogeneous execution, making it a promising option for scientific application developers.

In this work we directly evaluate several emerging parallel programming models: Kokkos, RAJA, OpenACC, and OpenMP 4.0, against the mature CUDA and OpenCL APIs. Each model has been used to port TeaLeaf, a miniature proxy application, or miniapp, that solves the heat conduction equation, and belongs to the Mantevo suite of applications. We find that the best performance is achieved with device-tuned implementations but that, in many cases, the performance portable models are able to solve the same problems to within a 5-20% performance penalty. The models expose varying levels of complexity to the developer, and they all present reasonable performance. We believe that complexity will become the major influencer in the long-term adoption of such models.

Many scientific codes consist of memory bandwidth bound kernels-the dominating factor of the runtime is the speed at which data can be loaded from memory into the Arithmetic Logic Units, before results are written back to memory. One major advantage of many-core devices such as General Purpose Graphics Processing Units (GPGPUs) and the Intel Xeon Phi is their focus on providing increased memory bandwidth over traditional CPU architectures. However, as with CPUs, this peak memory bandwidth is usually unachievable in practice and so benchmarks are required to measure a practical upper bound on expected performance. We augment the standard set of STREAM kernels with a dot product kernel to investigate the performance of simple reduction operations on large arrays. Such kernels are usually present in scientific codes and are still memory bandwidth bound. The choice of one programming model over another should ideally not limit the performance that can be achieved on a device. BabelStream (formally GPU-STREAM) has been updated to incorporate a wide variety of the latest parallel programming models, all implementing the same parallel scheme. As such this tool can be used as a kind of Rosetta Stone which provides both a cross-platform and cross-programming model array of results of achievable memory bandwidth.

No abstract