Fifer: Practical Acceleration of Irregular Applications on Reconfigurable Architectures

Résumé fourni par la source

Coarse-grain reconfigurable arrays (CGRAs) can achieve much higher performance and efficiency than general-purpose cores, approaching the performance of a specialized design while retaining programmability. Unfortunately, CGRAs have so far only been effective on applications with regular compute patterns. However, many important workloads like graph analytics, sparse linear algebra, and databases, are irregular applications with unpredictable access patterns and control flow. Since CGRAs map computation statically to a spatial fabric of functional units, irregular memory accesses and control flow cause frequent stalls and load imbalance.

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