An implementation of GPU accelerated mapreduce: using hadoop with openCL for breast cancer detection and compute-intensive jobs

Abstract

Abstract-In the realm of distributed computing for large-scale data processing, MapReduce stands out for its efficiency. However, as tasks become increasingly compute-intensive, it faces challenges in single-node performance. In the context of breast cancer detection, particularly with image data, a new approach has emerged to enhance MapReduce through GPU acceleration. This implementation, executed using Hadoop and OpenCL, targets a general and cost-effective hardware platform, seamlessly integrating into Apache Hadoop. Tailored for a heterogeneous multi-machine and multicore architecture, this solution addresses the compute-intensive nature of big data applications in breast cancer image analysis. Remarkably, the implementation has achieved a significant nearly 13-fold improvement in performance, without the need for additional optimizations.