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Modern Graphic Processing Units (GPUs) provide sufficiently flexible programming models that understanding their performance can provide insight in designing tomorrow's manycore processors, whether those are GPUs or otherwise. The combination of multiple, multithreaded, SIMD cores makes studying these GPUs useful in understanding trade-offs among memory,(More)
Modern DRAM systems rely on memory controllers that employ out-of-order scheduling to maximize row access locality and bank-level parallelism, which in turn maximizes DRAM bandwidth. This is especially important in graphics processing unit (GPU) architectures, where the large quantity of parallelism places a heavy demand on the memory system. The logic(More)
As the number of cores and threads in many core compute accelerators such as Graphics Processing Units (GPU) increases, so does the importance of on-chip interconnection network design. This paper explores throughput-effective network-on-chips (NoC) for future many core accelerators that employ bulk-synchronous parallel (BSP) programming models such as CUDA(More)
There has been little work investigating the overall performance impact of on-chip communication in manycore compute accelerators. In this paper we evaluate performance of a GPU-like compute accelerator running CUDA workloads and consisting of compute nodes, interconnection network and the graphics DRAM memory system using detailed cycle-level simulation.(More)
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