Conference opencl benchmark
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Many of our kernels reach near peak-performance with moderately sized tuning spaces that can be searched at runtime with acceptable overhead.
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Although it is generally believed that autotuning spaces tend to be too large to be searched during application runtime, we show that it is not necessarily the case when tuning spaces are designed rationally. With dynamic tuning, the Kernel Tuning Toolkit enables applications to re-tune performance-critical kernels at runtime whenever needed, for example, when input data changes.
#Conference opencl benchmark code
In addition to offline tuning, we also introduce dynamic autotuning of code optimization parameters during application runtime. OpenCL greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including professional creative tools, scientific and medical software, vision processing, and neural network training and inferencing. Our evaluation also demonstrates that autotuning is key to performance portability. Using our Kernel Tuning Toolkit, we show that with autotuning most of the kernels reach near-peak performance on various GPUs and outperform baseline implementations on CPUs and Xeon Phis. In increasingly AI-driven cloud data centres, in telecommunications and many other high-performance applications, field programmable gate arrays, or FPGAs. In this paper, we introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA. Autotuning of performance-relevant source-code parameters allows to automatically tune applications without hard coding optimizations and thus helps with keeping the performance portable. computing with FPGAs, in Proceedings of the International Conference for High Performance. However, there are no benchmarks for evaluating these frameworks. Addressing the challenges associated with performance optimization and performance portability, autotuning has gained a lot of interest. Since OpenCL performance estimation is non-trivial due to. Programming FPGAs with OpenCL-based high-level synthesis frameworks is gaining attention with a number of commercial and research frameworks announced. However, due to the complexity of heterogeneous architectures, optimization of codes for a certain type of architecture as well as porting codes across different architectures, while maintaining a comparable level of performance, can be extremely challenging. Accelerators, such as GPUs or Intel Xeon Phi co-processors, are often key to improving speed and energy efficiency of highly-parallel codes. 2021 IEEE 28th International Conference on High Performance Computing, Data. In recent years, the heterogeneity of both commodity and supercomputers hardware has increased sharply. Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity.