An algorithm-architecture co-design framework for gridding reconstruction using FPGAs
Proceedings of the 48th Design Automation Conference
Peak performance model for a custom precision floating-point dot product on FPGAs
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Journal of Parallel and Distributed Computing
Processor array design with the use of genetic algorithm
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Optimization schemes and performance evaluation of Smith–Waterman algorithm on CPU, GPU and FPGA
Concurrency and Computation: Practice & Experience
A performance and energy comparison of convolution on GPUs, FPGAs, and multicore processors
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
Parallel architectures for the kNN classifier -- design of soft IP cores and FPGA implementations
ACM Transactions on Embedded Computing Systems (TECS) - Special issue on application-specific processors
Hi-index | 0.00 |
High Performance Computing (HPC) or scientific codes are being executed across a wide variety of computing platforms from embedded processors to massively parallel GPUs. We present a comparison of the Basic Linear Algebra Subroutines (BLAS) using double-precision floating point on an FPGA, CPU and GPU. On the CPU and GPU, we utilize standard libraries on state-of-the-art devices. On the FPGA, we have developed parameterized modular implementations for the dot-product and Gaxpy or matrix-vector multiplication. In order to obtain optimal performance for any aspect ratio of the matrices, we have designed a high-throughput accumulator to perform an efficient reduction of floating point values. To support scalability to large data-sets, we target the BEE3 FPGA platform. We use performance and energy efficiency as metrics to compare the different platforms. Results show that FPGAs offer comparable performance as well as 2.7 to 293 times better energy efficiency for the test cases that we implemented on all three platforms.