We propose the TEMPORANA framework foranalysis of integrated scenarios aiming at studying the possibleeffects on the society of sustainable innovation policies. Theframework consists of a set of advanced temporal quer...
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High utilization of hardware resources is the key for designing performance and power optimized GPU applications. The efficiency of applications and kernels, which do not fully utilize the GPU resources, can be improv...
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ISBN:
(纸本)9781509036837
High utilization of hardware resources is the key for designing performance and power optimized GPU applications. The efficiency of applications and kernels, which do not fully utilize the GPU resources, can be improved through concurrent execution with independent kernels and/or applications. Hyper-Q enables multiple CPU threads or processes to launch work on a single GPU simultaneously for increased GPU utilization. However, without careful design, false serialization may occur due to the contention for shared hardware resources such as direct memory access (DMA) engines. In this paper, we reveal the impact of such contention on performance and assess a method for overcoming the limitation with minimal algorithmic overhead. We demonstrate a method to achieve up to 31.8% improvement in performance and 10.4%reduction in energy on average for a finite set of application tasks when maximizing GPU execution concurrency.
Shared memory, one of the most popular models for programming parallel platforms, is becoming ubiquitous both in low-end workstations and high-end servers. With the advent of low-latency networking hardware, clusters ...
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