GPU vs FPGA: A Comparative Analysis for Non-standard Precision

  • Umar Ibrahim Minhas
  • Samuel Bayliss
  • George A. Constantinides
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8405)


FPGAs and GPUs are increasingly used in a range of high performance computing applications. When implementing numerical algorithms on either platform, we can choose to represent operands with different levels of accuracy. A trade-off exists between the numerical accuracy of arithmetic operators and the resources needed to implement them. Where algorithmic requirements for numerical stability are captured in a design description, this trade-off can be exploited to optimize performance by using high-accuracy operators only where they are most required. Support for half and double-double floating point representations allows additional flexibility to achieve this. The aim of this work is to study the language and hardware support, and the achievable peak performance for non-standard precisions on a GPU and an FPGA. A compute intensive program, matrix-matrix multiply, is selected as a benchmark and implemented for various different matrix sizes. The results show that for large-enough matrices, GPUs out-perform FPGA-based implementations but for some smaller matrix sizes, specialized FPGA floating-point operators for half and double-double precision can deliver higher throughput than implementation on a GPU.


GPU FPGA High Performance Computing (HPC) Non-standard Precision Half Precision Double-double Precision 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Umar Ibrahim Minhas
    • 1
  • Samuel Bayliss
    • 1
  • George A. Constantinides
    • 1
  1. 1.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

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