Multi-dimensional characterization of temporal data mining on graphics processors

My first foray into GPU research, at least in terms of publication. We opened a big can of worms with this paper, asking where some of these anomalies came from, and the future work explaining it never really happened. If nothing else, this paper serves as a reminder that just because we think we know how something works doesn’t mean we always know how it will behave

Abstract:

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, we present a characterization of a MapReduced-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 ldquoone-size-fits-allrdquo 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.

BibTex:

@INPROCEEDINGS{5161049, 
    author={Archuleta, J. and Yong Cao and Scogland, T. and Wu-chun Feng}, 
    booktitle={Parallel Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on}, 
    title={Multi-dimensional characterization of temporal data mining on graphics processors}, 
    year={2009}, 
    month={may}, 
    volume={}, 
    number={}, 
    pages={1 -12}, 
    keywords={MapReduce-based data-mining;data parallelism;data-access
    method;general-purpose GPU;graphics processing unit;graphics
    processors;multi-dimensional characterization;multidimensional performance
    evaluation;task parallelism;temporal data mining;biology computing;computer
    graphic equipment;data mining;neurophysiology;parallel processing;software
    performance evaluation;}, 
    doi={10.1109/IPDPS.2009.5161049}, 
    ISSN={1530-2075},
}

Multi-dimensional characterization of temporal data mining on graphics processors

By: J Archuleta, Yong Cao, T Scogland, and Wu-chun Feng.

In: IPDPS 2009 IEEE International Symposium on Parallel & Distributed Processing