Abstract
Current application of GPU processors for parallel computing tasks shows excellent results in terms of speedups compared to CPU processors. However, there is no existing middleware framework that enables automatic distribution of data and processing across heterogeneous computing resources for structured and unstructured Big Data applications. Thus, we propose a middleware framework for “Big Data” analytics that provides mechanisms for automatic data segmentation, distribution, execution, information retrieval across multiple cards (CPU and GPU) and machines, a modular design for easy addition of new GPU kernels at both analytic and processing layer, and information presentation. The architecture and components of the framework such as multi-card data distribution and execution, data structures for efficient memory access, algorithms for parallel GPU computation, and results for various test configurations are shown. Our results show proposed middleware framework, providing alternative and cheaper HPC solution to users. Data cleansing algorithms on GPU show a speedup of over two orders of magnitude compared to the same operation done in MySQL on a multi-core machine. Our framework is also capable of processing more than 120 million of health data within 11 s.
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Notes
- 1.
It is a CUDA binding for the Java language, which exploiting the features of NVIDIA GPU computing from Java-based applications.
- 2.
Parallel Thread Execution (PTX) is a pseudo-assembly language used in NVIDIA CUDA programming environment.
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Acknowledgement
This research was done under joint lab of “NVIDIA-HP-MIMOS GPU R&D and Solution Center.” This is the first GPU solution center in Southeast Asia established in October 2012. Funding for the work came from MOSTI, Malaysia. The authors would like to thank Prof. Simon See and Pradeep Gupta from NVIDIA for their support.
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Karuppiah, E.K., Kok, Y.K., Singh, K. (2015). A Middleware Framework for Programmable Multi-GPU-Based Big Data Applications. In: Cai, Y., See, S. (eds) GPU Computing and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-287-134-3_12
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