Abstract
During recent years, GPU micro-architectures have changed dramatically, evolving into powerful many-core deep-multithreaded platforms for parallel workloads. While important micro-architectural modifications continue to appear in every new generation of these processors, unfortunately, little is known about the details of these innovative designs. One of the key questions in understanding GPUs is how they deal with outstanding memory misses. Our goal in this study is to find answers to this question. To this end, we develop a set of micro-benchmarks in CUDA to understand the outstanding memory requests handling resources. Particularly, we study two NVIDIA GPGPUs (Fermi and Kepler) and estimate their capability in handling outstanding memory requests. We show that Kepler can issue nearly 32X higher number of outstanding memory requests, compared to Fermi. We explain this enhancement by Kepler's architectural modifications in outstanding memory request handling resources.
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