A Middleware Framework for Programmable Multi-GPU-Based Big Data Applications

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. IntroductionNVIDIA CUDA-enabled GPGPU (general purpose graphic processing unit) has made its name by being part of world super computers to enable high-performance computation. Thus, GPGPUs are widely accepted and becoming common for many high-performance computing applications. GPGPUs are used for both specific and general purpose applications either running in large-scale system or desktop PCs.