Platform-aware dynamic configuration support for efficient text processing on heterogeneous system

Significant efforts have been made in accelerating computer vision and machine learning algorithms by utilizing parallel processors such as multi-core CPUs and GPUs. Although the suitability of GPU is well-known for computer graphics and image processing applications which require massively parallel floating-point computations, recent research movement towards general purpose computing on-GPU (GPGPU) makes it possible to take advantage of parallel processors to accelerate text processing applications as well. However, how to fully leverage different types of parallel processor architectures to obtain optimal performance (especially with text) without making specific efforts to each platform still remains a great challenge. We applied performance and accuracy enhancements to Naive Bayes algorithm to develop a practically sound implementation of text classification. A platform-aware dynamic configuration support automation flow is also proposed to support the seamless execution of our work across platforms. Experiments on various (integrated graphics, dedicated multiple GPUs) platforms demonstrate that our proposed approach improves both accuracy and performance of text classification.