| FPGAs are becoming an attractive platform for accelerating many computations including scientific applications. However, their adoption has been limited by the large development cost and short life span of FPGA designs. We believe that FPGA-based scientific computation would become far more practical if there were hardware libraries that were portable to any FPGA with performance that could scale with the resources of the FPGA. To illustrate this idea we have implemented one common supercomputing function: the LU factorization method for solving linear systems. This dissertation discusses issues in making the design both portable and scalable. The design is automatically generated to match the FPGA's capabilities and external memory through the use of parameters. We compared the performance of the design on the FPGA to a single processor core and found that it performs 2.2 times faster, and that the energy dissipated per computation is 5 times less. |
