Abstract
As CPU processing speed has slowed down year-on-year, heterogeneous “CPU-GPU” architectures combining multi-core CPU and GPU accelerators have become increasingly attractive. Under this backdrop, the Heterogeneous System Architecture (HSA) standard was released in 2012. New Accelerated Processing Unit (APU) architectures – AMD Kaveri and Carrizo – were released in 2014 and 2015 respectively, and are compliant with HSA. These architectures incorporate two technologies central to HSA, hUMA (heterogeneous Unified Memory Access) and hQ (heterogeneous Queuing). This paper realizes radix sort and matrix-vector multiplication – two data-parallel applications on Kaveri platform. By analyzing the performance, a dynamic task scheduling stratgy is proposed. The experimental results show that the running efficiency of algorithm can be greatly improved by using APU with reasonable task scheduling. In the same way, the other data-parallel algorithm would also be optimized on these heterogeneous multi-core architecture.
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Acknowledgement
This work was supported by the significant special project for Core electronic devices, high-end general chips and basic software products (2012ZX01039-004), and also supported by Beijing Key Laboratory on Integration and Analysis of Large Scale Stream Data.
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Bao, Z., Chen, C., Zhang, W. (2018). Task Scheduling of Data-Parallel Applications on HSA Platform. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_35
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DOI: https://doi.org/10.1007/978-981-13-2203-7_35
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