Plasma performance in JET Achievements and projections

Paper at 15. European Conf. on Controlled Fusion and Plasma Heating Dubrovnik (YU) 16-20 May 1988Available from British Library Document Supply Centre- DSC:4672.262(JET-P--(88)26) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

OmpSs-OpenCL programming model for heterogeneous systems

The advent of heterogeneous computing has forced programmers to use platform specific programming paradigms in order to achieve maximum performance. This approach has a steep learning curve for programmers and also has detrimental influence on productivity and code re-usability. To help with this situation, OpenCL an open-source, parallel computing API for cross platform computations was conceived. OpenCL provides a homogeneous view of the computational resources (CPU and GPU) thereby enabling software portability across different platforms. Although OpenCL resolves software portability issues, the programming paradigm presents low programmability and additionally falls short in performance. In this paper we focus on integrating OpenCL framework with the OmpSs task based programming model using Nanos run time infrastructure to address these shortcomings. This would enable the programmer to skip cumbersome OpenCL constructs including OpenCL plaform creation, compilation, kernel building, kernel argument setting and memory transfers, instead write a sequential program with annotated pragmas. Our proposal mainly focuses on how to exploit the best of the underlying hardware platform with greater ease in programming and to gain significant performance using the data parallelism offered by the OpenCL run time for GPUs and multicore architectures. We have evaluated the platform with important benchmarks and have noticed substantial ease in programming with comparable performance.Postprint (published version

OmpSs-OpenCL programming model for heterogeneous systems

The advent of heterogeneous computing has forced programmers to use platform specific programming paradigms in order to achieve maximum performance. This approach has a steep learning curve for programmers and also has detrimental influence on productivity and code re-usability. To help with this situation, OpenCL an open-source, parallel computing API for cross platform computations was conceived. OpenCL provides a homogeneous view of the computational resources (CPU and GPU) thereby enabling software portability across different platforms. Although OpenCL resolves software portability issues, the programming paradigm presents low programmability and additionally falls short in performance. In this paper we focus on integrating OpenCL framework with the OmpSs task based programming model using Nanos run time infrastructure to address these shortcomings. This would enable the programmer to skip cumbersome OpenCL constructs including OpenCL plaform creation, compilation, kernel building, kernel argument setting and memory transfers, instead write a sequential program with annotated pragmas. Our proposal mainly focuses on how to exploit the best of the underlying hardware platform with greater ease in programming and to gain significant performance using the data parallelism offered by the OpenCL run time for GPUs and multicore architectures. We have evaluated the platform with important benchmarks and have noticed substantial ease in programming with comparable performance

Scalability and parallel execution of OmpSs-OpenCL tasks on heterogeneous CPU-GPU environment

With heterogeneous computing becoming mainstream, researchers and software vendors have been trying to exploit the best of the underlying architectures like GPUs or CPUs to enhance performance. Parallel programming models play a crucial role in achieving this enhancement. One such model is OpenCL, a parallel computing API for cross platform computations targeting heterogeneous architectures. However, OpenCL is a low-level programming language, therefore it can be time consuming to directly develop OpenCL code. To address this shortcoming, OpenCL has been integrated with OmpSs, a task-based programming model to provide abstraction to the user thereby reducing programmer effort. OmpSs-OpenCL programming model deals with a single OpenCL device either a CPU or a GPU. In this paper, we upgrade OmpSs-OpenCL programming model by supporting parallel execution of tasks across multiple CPU-GPU heterogeneous platforms. We discuss the design of the programming model along with its asynchronous runtime system. We investigated scalability of four OmpSs-OpenCL benchmarks across 4 GPUs gaining speedup of up to 4x. Further, in order to achieve effective utilization of the computing resources, we present static and work-stealing scheduling techniques. We show results of parallel execution of applications using OmpSs-OpenCL model and use heterogeneous workloads to evaluate our scheduling techniques on a heterogeneous CPU-GPU platform.We thankfully acknowledge the support of the European Commission through the TERAFLUX project (FP7-249013) and the HiPEAC-2 Network of Excellence (FP7/ICT 217068),the support of the Spanish Ministry of Education (TIN-2007-60625, TIN-2012-34557, CSD2007-00050 and FI program) and the Generalitat de Catalunya (2009-SGR-980)Peer Reviewe