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This research aims to develop a framework for accelerating large graph problems using FPGA technology.

December 1, 2011

A framework for FPGA acceleration of large graph problems: Graphlet counting case study

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This research aims to develop a framework for accelerating large graph problems using FPGA technology.
Developed a reconfigurable hardware architecture to manage off-chip memory latency.
Utilized a memory crossbar to connect parallel processing elements to off-chip memory.
Conducted a comparative performance analysis of a graphlet counting case study between hardware and software.
The hardware implementation achieved a speedup of 10 times compared to a quad-core software implementation.
Execution time for graphlet counting was reduced from 2 hours to 12 minutes.
Quantitative comparisons showed significant performance improvements across large graphs.

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Randomized trial demonstrates improved performance in graphlet counting using FPGA, suggesting efficient graph processing methods.

Betkaoui et al. (2011) studied this question.

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