FPGA design space exploration for scientific HPC applications using a fast and accurate cost model based on roofline analysis

High-performance computing on heterogeneous platforms in general and those with FPGAs in particular presents a significant programming challenge. We contend that compiler technology has to evolve to automatically optimized applications by transforming a given original program. We are developing a novel methodology based on type transformations on a functional description of a given scientific kernel, for generating correct-by-construction design variants. An associated lightweight costing mechanism for evaluating these variants is a cornerstone of our methodology, and the focus of this paper. We discuss our use of the roofline model to work with our optimizing compiler to enable us to quickly derive accurate estimates of performance from the design’s representation in our custom intermediate language. We show results confirming the accuracy of our cost model by validating it on different scientific kernels. A case study is presented to demonstrate that a solution created from our optimizing framework outperforms commercial high-level synthesis tools both in terms of throughput and power efficiency