Multi-dimensional characterization of temporal data mining on graphics processors

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 datamining 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 p...

• Type: Conference
• Year: 2009
• Where: IPPS
• Authors: Jeremy S. Archuleta, Yong Cao, Thomas Scogland, Wu-chun Feng
• Keywords: Data Parallelism, Distributed And Parallel Computing, IPPS 2009, MapReduce-based Datamining Application, Task Parallelism