Paper Info
Title
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.
Year
DOI
Venue
2007
10.1109/FCCM.2007.58
Napa, CA
Keywords
Field
DocType
kernel,computer applications,limiting factor,acceleration,computational modeling,hardware,floating point,parallel processing,computer architecture,registers,integer programming
Integer,Kernel (linear algebra),Suite,Floating point,Computer science,Parallel computing,Dataflow,Integer programming,Computer Applications,Computation
Conference
ISBN
Citations 
PageRank 
0-7695-2940-2
11
0.97
References 
Authors
25
3
Name
Order
Citations
PageRank
Kyle Rupnow125021.49
Keith D. Underwood284777.39
Katherine Compton3110.97