Reaping the Processing Potential of FPGA on Double-Precision Floating-Point Operations: An Eigenvalue Solver Case Study

Many scientific applications such as electromagnetics require their operations carried out in double-precision floating-point format. The efficiency of these applications is mainly subject to the floating-point processing performance on the target processors. In this work, we use an eigenvalue solver application as a case study to demonstrate the processing potential of an FPGA device when dealing with floating-point operations. Relying on deep pipelines and large local memory directly accessible within the FPGA device, more than 20 × performance improvement has been achieved for this particular case whose computation is intrinsically sequential. The methodology described in this paper can be conveniently applied to other domain applications that share similar data processing characteristics, demonstrating an impact on a broad range of scientific fields, e.g., numerical linear algebra.