Towards General Purpose Acceleration by Exploiting Common Data-Dependence Forms
Vidushi Dadu, Jian Weng, Sihao Liu, Tony Nowatzki · 2019
With slowing technology scaling, specialized accelerators are increasingly attractive solutions to continue expected generational scaling of performance. However, in order to accelerate more advanced algorithms or those from challenging domains, supporting data-dependence becomes necessary. This manifests as either data-dependent control (eg. join two sparse lists), or data-dependent memory accesses (eg. hash-table access). These forms of data-dependence inherently couple compute with memory, and also preclude efficient vectorization -- defeating the traditional mechanisms of programmable accelerators (eg. GPUs).