#OPENCL BENCHMARK FOR 2016 AMD GPUS PORTABLE#
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Shen J et al (2012) Performance gaps between OpenMP and OpenCL for multi-core CPUs. Torres Y, Gonzalez-Escribano A, Llanos DR (2013) uBench: exposing the impact of CUDA block geometry in terms of performance. In: Proceedings of IEEE International Symposium on Workload Characterization 2009 (IISWC 2009), IEEE, pp 44–54 IEEE Press, USA, p 31Ĭhe S, Boyer M, Meng J et al (2009) Rodinia: a benchmark suite for heterogeneous computing. In: Proceedings of the 2008 ACM/IEEE conference on Supercomputing. Volkov V, Demmel JW (2008) Benchmarking GPUs to tune dense linear algebra. In: Proceedings of 2011 IEEE International Symposium on Workload Characterization (IISWC), IEEE, pp 137–148 Seo S, Jo G, Lee J (2011) Performance characterization of the NAS Parallel Benchmarks in OpenCL. We also compared the measuring results with existing benchmarks to demonstrate the reasonableness and correctness of our benchmark suite. NVIDIA GeForce GTX 460se and AMD Radeon HD 6850. AMD Athlon II X2 250 and Intel Pentium Dual-Core E5400, and two different GPUs, i.e.
#OPENCL BENCHMARK FOR 2016 AMD GPUS SOFTWARE#
In this paper, we introduce the implementations of our OpenCL micro benchmarks, and present the measuring results of hardware and software features like performance of mathematical operations, bus bandwidths, memory architectures, branch synchronizations and scalability, etc., on two multi-core CPUs, i.e. For this purpose, we design and implement an OpenCL micro-benchmark suite for GPUs and CPUs.
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To develop efficient OpenCL applications for the particular platform, we still need a more profound understanding of architecture features on the OpenCL model and computing devices. OpenCL is vendor independent and hence not specialized for any particular compute device. Open computing language (OpenCL) is a new industry standard for task-parallel and data-parallel heterogeneous computing on a variety of modern CPUs, GPUs, DSPs, and other microprocessor designs.