An OpenCL Framework for Fuzzy Associative Classification and its Application to Disease Prediction
Procedia Computer Science
Recently, the broad availability of online and soft real-time data has been attracting corporations and researchers towards data analytics. Though newer and faster algorithms are developed, as the available dataset sizes increase exponentially, computational processing has been falling behind. The trend on computational resources is mostly towards multiple cores and parallel processing, while the CPU clock speed improvements are slowing down. An important computational resource is the Graphics
... ce is the Graphics Processing Unit with multiple-cores, surpassing 2,000 processing elements in one processing unit and operating at almost 5 teraflops capability, such as the recently introduced GeForce GTX Titan. In this study, an Open Computing Language parallel processing framework for fuzzy associative classification is described. The hybrid CPU-GPU implementation is developed and employed for prediction of infectious disease outbreaks, specifically Influenza, using environmental and disease data readily available online. A comparison of the implemented performance with respect to another in-house developed Fuzzy Association Rule Mining operator, FARM, and the performances on four distinct parallel processing environments, specifically a four processor, 64 threads capable, Opteron server; a two processor, 24 threads capable, Xeon server; a GeForce GTX 680 GPU card; and a Radeon HD 7950 GPU card, is presented. The advantages and disadvantages of the OpenCL implementation on parallel processors are discussed.