Multi-Dimensional Characterization of Temporal Data Mining on Graphics Processors
2009) Multi-Dimensional Characterization of Temporal Data Mining on Graphics Processors. Technical Report TR-09-01, Computer Science, Virginia Tech. (
Through the algorthmic 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 neuroinformatics and bioinformatics. As such, we present a characterization of a MapReduce-based data-mining application on a general-purpose GPU (GPGPU). Using neuroscience 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 performance characterizations of our data-mining application.
|Item Type:||Departmental Technical Report|
|Subjects:||Computer Science > Algorithms and Data Structure|
|Deposited By:||Administrator, Eprints|
|Deposited On:||13 January 2009|