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2014 23rd International Conference on Parallel Architecture and Compilation (PACT) (2014)
Edmonton, Canada
Aug. 23, 2014 to Aug. 27, 2014
ISBN: 978-1-5090-6607-0
pp: 479-480
Deepak Majeti , Rice University
Kuldeep S. Meel , Rice University
Rajkishore Barik , Intel Labs
Vivek Sarkar , Rice University
Data layouts play a crucial role in determining the performance of a given application running on a given architecture. Existing parallel programming frameworks for both multicore and heterogeneous systems leave the onus of selecting a data layout to the programmer. Therefore, shifting the burden of data layout selection to optimizing compilers can greatly enhance programmer productivity and application performance. In this work, we introduce ADHA: a two-level hierarchal formulation of the data layout problem for modern heterogeneous architectures. We have created a reference implementation of ADHA in the Heterogeneous Habanero-C (H2C) parallel programming system. ADHA shows significant performance benefits of up to 6.92× compared to manually specified layouts for two benchmark programs running on a CPU+GPU heterogeneous platform.
Layout, Graphics processing units, Benchmark testing, Kernel, Computer architecture, Biomedical imaging,Heterogeneous Architectures, Compilers, Data Layout
Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar, "ADHA: Automatic data layout framework for heterogeneous architectures", 2014 23rd International Conference on Parallel Architecture and Compilation (PACT), vol. 00, no. , pp. 479-480, 2014, doi:10.1145/2628071.2628122
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