Scogland - Multi-Dimensional Characterization of Temporal Data Mining on Graphics Processors

Through the algorithmic design patterns of data paral-lelism 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, we present a characterization of a MapReduce-based data-mining appli-cation on a general-purpose GPU (GPGPU). Using neuro-science as the application vehicle, the results of our multi-dimensional performance evaluation show that a “one-size-fits-all ” approach maps poorly across different GPGPU cards. Rather, a high-performance implementation on the GPGPU should factor in the 1) problem size, 2) type of GPU, 3) type of algorithm, and 4) data-access method when determining the type and level of parallelism. To guide the GPGPU programmer towards optimal performance within such a broad design space, we provide eight general perfor-mance characterizations of our data-mining application. 1