No abstract

http://www.sciencedirect.com/science/article/pii/S1877050914006024International audienceData mining tools may be computationally demanding, so there is an increasing interest on parallel computing strategies to improve their performance. The popularization of Graphics Processing Units (GPUs) increased the computing power of current desktop computers, but desktop-based data mining tools do not usually take full advantage of these architectures. This paper exploits an approach to improve the performance of Weka, a popular data mining tool, through parallelization on GPU-accelerated machines. From the profiling of Weka object-oriented code, we chose to parallelize a matrix multiplication method using state-of-the-art tools. The implementation was merged into Weka so that we could analyze the impact of parallel execution on its performance. The results show a significant speedup on the target parallel architectures, compared to the original, sequential Weka code

International audienceData mining tools may be computationally demanding, which leads to an increasing interest on par- allel computing strategies in order to improve their per- formance. While multi-core processors and Graphics Processing Units (GPUs) accelerators increased the com- puting power of current desktop computers, we observe that desktop-based data mining tools do not take full advantage of these architectures yet. This paper investi- gates strategies to improve the performance of Weka, a popular data mining tool, through multi-core and GPU acceleration. Using performance profiling of Weka, we identify operations that could improve the data mining performance when parallelized. We selected two of these operations, and analyze the impact of their parallel exe- cution on Weka’s performance. These experiments demonstrate that while significant speedups can be achieved, all operations are not prone to be parallelized, which reinforces the need for a careful and well-studied selection of the candidates

No abstract