Multi-Dimensional Characterization of Temporal Data Mining on Graphics Processors
Through the algorithmic design patterns of data parallelism and task parallelism, the Graphics Processing Unit (GPU) offers the potential to vastly accelerate discovery and innovation across a multitude of disciplines. For example, the exponential growth in data volume now presents an obstacle for high-throughput data mining in fields such as neuroscience and bioinformatics. As such, one presents a characterization of a MapReduce-based data-mining application on a General-Purpose GPU (GPGPU). Using neuroscience as the application vehicle, the results of the multidimensional performance evaluation show that a "One-size-fits-all" approach maps poorly across different GPGPU cards.