In-Hardware Moving Compute to Data Model to Accelerate Thread Synchronization on Large Multicores

Abstract

In this article, the moving computation to data model (MC2D) is proposed to accelerate thread synchronization by pinning shared data to dedicated cores, and utilize in-hardware core-to-core messaging to communicate critical code execution. The MC2D model optimizes shared data locality by eliminating unnecessary data movement, and alleviates contended synchronization using nonblocking communication between threads. This article evaluates task-parallel algorithms under their synchronization-centric classification to demonstrate that the effectiveness of the MC2D model to exploit performance correlates with the number and frequency of synchronizations. The evaluation on Tilera TILE-Gx72 multicore shows that the MC2D model delivers highest performance scaling gains for ordered and unordered algorithms that expose significant synchronizations due to task and data level dependencies. The MC2D model is also shown to deliver at par performance with the traditional atomic operations based model for highly data parallel algorithms from the unordered category.