A task-centric memory model for scalable accelerator architectures

This paper presents a task-centric memory model for 1000-core compute accelerators. Visual computing applications are emerging as an important class of workloads that can exploit 1000-core processors. In these workloads, we observe data sharing and communication patterns that can be leveraged in the design of memory systems for future 1000-core processors. Based on these insights, we propose a memory model that uses a software protocol, working in collaboration with hardware caches, to maintain a coherent, single-address space view of memory without the need for hardware coherence support. We evaluate the task-centric memory model in simulation on a 1024-core MIMD accelerator we are developing that, with the help of a runtime system, implements the proposed memory model. We evaluate coherence management policies related to the task-centric memory model and show that the overhead of maintaining a coherent view of memory in software can be minimal. We further show that, while software management may constrain speculative hardware prefetching into local caches, a common optimization, it does not constrain the more relevant use case of off-chip prefetching from DRAM into shared caches.