Towards General-Purpose Acceleration: Finding Structure in Irregularity

Programmable hardware accelerators (e.g., vector processors, GPUs) have been extremely successful at targeting algorithms with regular control and memory patterns to achieve order-of-magnitude performance and energy efficiency improvements. However, they perform far under the peak on important irregular algorithms, like those from graph processing, database querying, genomics, advanced machine learning, and others. This work posits that the primary culprit is specific forms of irregular control flow and memory access. By capturing the problematic behavior at a domain-agnostic level, we propose an accelerator that is sufficiently general, matches domain-specific accelerator performance, and significantly outperforms traditional CPUs and GPUs.