Abstract
Big data computing and analysis can uncover hidden patterns, correlations and other insights by examining large amounts of data. Comparing with the traditional processor, the new types of processors, just like digital signal processor (DSP), Field Programmable Gate Array (FPGA), graphics processing unit (GPU), could improve the speed of data analysis significantly. Heterogeneous multicores systems have become the primary architecture as devices are tasked to do more complicated functions faster. While, in most cases, these heterogeneous resources cannot be utilized sufficiently because the system software is provided by vendors, loaded pre-sale and doesn’t change. The cloud computing offers the capability of distributing infrastructures according to the requirements. We build a cloud-like heterogeneous computing platform which including PowerPC, DSP, GPU and FPGA. A task-driven dynamic loading scheme is proposed by making use of the virtualization and middleware technologies. The system can manage the entire lives of allocating, loading, using, and recovering. Taking this as a guide, a private cloud principle verification system including web application layer, main control layer, and computing service layer is designed and verified, which proves the feasibility of the computing platform. According to the test results of web system, the platform can well meet the design intention of acquiring the computing resources according to the task requirements.
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Yang, P., Wang, Q., Zhang, P., Wang, Z., Fan, L., Huang, C. (2018). A Task-Driven Reconfigurable Heterogeneous Computing Platform for Big Data Computing. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_35
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DOI: https://doi.org/10.1007/978-981-13-2922-7_35
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