FPGA vs. GPU for sparse matrix vector multiply

@article{Zhang2009FPGAVG,
  title={FPGA vs. GPU for sparse matrix vector multiply},
  author={Yan Zhang and Yasser Shalabi and Rishabh Jain and Krishna K. Nagar and Jason D. Bakos},
  journal={2009 International Conference on Field-Programmable Technology},
  year={2009},
  pages={255-262}
}
Sparse matrix-vector multiplication (SpMV) is a common operation in numerical linear algebra and is the computational kernel of many scientific applications. It is one of the original and perhaps most studied targets for FPGA acceleration. Despite this, GPUs, which have only recently gained both general-purpose programmability and native support for double precision floating-point arithmetic, are viewed by some as a more effective platform for SpMV and similar linear algebra computations. In… CONTINUE READING
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