Mixing multi-core CPUs and GPUs for scientific simulation software

Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA's CUDA and Khronos consortium's open compute language (OpenCL) support a number of individual parallel application programming paradigms. To scale up the performance of some complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica- tions using threading approaches and multi-core CPUs to control independent GPU devices. We present speed-up data and discuss multi-threading software issues for the applications level programmer and o er some suggested areas for language development and integration between coarse-grained and ne-grained multi-thread systems. We discuss results from three common simulation algorithmic areas including: partial di erential equations; graph cluster metric calculations and random number generation. We report on programming experiences and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs; a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and trends in multi-core programming for scienti c applications developers

An investigation of the performance portability of OpenCL

This paper reports on the development of an MPI/OpenCL implementation of LU, an application-level benchmark from the NAS Parallel Benchmark Suite. An account of the design decisions addressed during the development of this code is presented, demonstrating the importance of memory arrangement and work-item/work-group distribution strategies when applications are deployed on different device types. The resulting platform-agnostic, single source application is benchmarked on a number of different architectures, and is shown to be 1.3–1.5× slower than native FORTRAN 77 or CUDA implementations on a single node and 1.3–3.1× slower on multiple nodes. We also explore the potential performance gains of OpenCL’s device fissioning capability, demonstrating up to a 3× speed-up over our original OpenCL implementation

Astrophysical Supercomputing with GPUs: Critical Decisions for Early Adopters

General purpose computing on graphics processing units (GPGPU) is dramatically changing the landscape of high performance computing in astronomy. In this paper, we identify and investigate several key decision areas, with a goal of simplyfing the early adoption of GPGPU in astronomy. We consider the merits of OpenCL as an open standard in order to reduce risks associated with coding in a native, vendor-specific programming environment, and present a GPU programming philosophy based on using brute force solutions. We assert that effective use of new GPU-based supercomputing facilities will require a change in approach from astronomers. This will likely include improved programming training, an increased need for software development best-practice through the use of profiling and related optimisation tools, and a greater reliance on third-party code libraries. As with any new technology, those willing to take the risks, and make the investment of time and effort to become early adopters of GPGPU in astronomy, stand to reap great benefits.Comment: 13 pages, 5 figures, accepted for publication in PAS

A Comparison of some recent Task-based Parallel Programming Models

The need for parallel programming models that are simple to use and at the same time efficient for current ant future parallel platforms has led to recent attention to task-based models such as Cilk++, Intel TBB and the task concept in OpenMP version 3.0. The choice of model and implementation can have a major impact on the final performance and in order to understand some of the trade-offs we have made a quantitative study comparing four implementations of OpenMP (gcc, Intel icc, Sun studio and the research compiler Mercurium/nanos mcc), Cilk++ and Wool, a high-performance task-based library developed at SICS. Abstract. We use microbenchmarks to characterize costs for task-creation and stealing and the Barcelona OpenMP Tasks Suite for characterizing application performance. By far Wool and Cilk++ have the lowest overhead in both spawning and stealing tasks. This is reflected in application performance when many tasks with small granularity are spawned where Cilk++ and, in particular, has the highest performance. For coarse granularity applications, the OpenMP implementations have quite similar performance as the more light-weight Cilk++ and Wool except for one application where mcc is superior thanks to a superior task scheduler. Abstract. The OpenMP implemenations are generally not yet ready for use when the task granularity becomes very small. There is no inherent reason for this, so we expect future implementations of OpenMP to focus on this issue