Scientific Application Acceleration with Reconfigurable Functional Units

Abstract

While scientific applications in the past were limited by floating point computations, modern scientific applications use more unstructured formulations. These applications have a significant percentage of integer computation - increasingly a limiting factor in scientific application performance. In real scientific applications employed at Sandia National Labs, integer computations constitute on average 37% of the application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze application traces of Sandia's scientific applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the acceleration potential of the applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia applications show an improvement of 5% or more.