Limits of FPGA acceleration of 3D Green's Function computation for geophysical applications

FPGA-based accelerators can outperform multi-core, GPU and Xeon Phi based platforms by at as much as 2.8× for 3D Green's Function processing in geophysics while delivering superior energy efficiency. FPGAs can efficiently implement a complex mixture of compute patterns that include data-paralle...

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Bibliographic Details
Main Authors: Kapre, Nachiket, Kumar, Jayakrishnan Selva, Gupta, Parjanya, Masuti, Sagar, Barbot, Sylvain
Other Authors: School of Computer Engineering
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
Online Access:https://hdl.handle.net/10356/81212
http://hdl.handle.net/10220/39186
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Institution: Nanyang Technological University
Language: English
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Summary:FPGA-based accelerators can outperform multi-core, GPU and Xeon Phi based platforms by at as much as 2.8× for 3D Green's Function processing in geophysics while delivering superior energy efficiency. FPGAs can efficiently implement a complex mixture of compute patterns that include data-parallelism, reductions, dataflow and streaming computations using spatial parallelism to deliver these speedups and power benefits. Since 3D Green's Function is highly-parallel but communication bound, we optimize the FPGA implementation by considering loop restructuring and tiling optimizations to minimize and regularize off-chip accesses. Furthermore, we configure the FPGA to implement the key compute intensive kernels at double-precision as well as single-precision to exploit the uncertainty in measurements of earthquake monitoring sensors. For 512×512×512 problem size, the Xilinx SX475T (Maxeler MAX3) outperforms the fastest architecture by 1.1-1.4× (double-precision), 2.2-2.8× (single-precision) with 1.2× better energy efficiency.