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A Task-centric Memory Model for Scalable Accelerator Architectures

John H. Kelm, Daniel R. Johnson, Steven S. Lumetta, Matthew I. Frank, Sanjay J. Patel
University of Illinois at Urbana-Champaign, Urbana, IL 61801
18th International Conference on Parallel Architectures and Compilation Techniques, 2009, PACT 2009, pp.77-87

@conference{kelm2009task,

   title={A task-centric memory model for scalable accelerator architectures},

   author={Kelm, J.H. and Johnson, D.R. and Lumetta, S.S. and Frank, M.I. and Patel, S.J.},

   booktitle={2009 18th International Conference on Parallel Architectures and Compilation Techniques},

   pages={77–87},

   issn={1089-795X},

   year={2009},

   organization={IEEE}

}

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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.
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