Application-aware Memory System for Fair and Efficient Execution of Concurrent GPGPU Applications

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The available computing resources in modern GPUs are growing with each new generation. However, as many general purpose applications with limited thread-scalability are tuned to take advantage of GPUs, available compute resources might not be optimally utilized. To address this, modern GPUs will need to execute multiple kernels simultaneously. As current generations of GPUs (e.g., NVIDIA Kepler, AMD Radeon) already enable concurrent execution of kernels from the same application, in this paper we address the next logical step: executing multiple concurrent applications in GPUs. We show that while this paradigm has a potential to improve the overall system performance, negative interactions among concurrently executing applications in the memory system can severely hamper the performance and fairness among applications. We show that the current application agnostic GPU memory system design can (1) lead to sub-optimal GPU performance; and (2) create significant imbalance in performance slowdowns across kernels. Thus, we argue that GPU memory system should be augmented with application awareness. As one example to the applicability of this concept, we augment the memory system hardware with application awareness such that requests from different applications can be scheduled in a round robin (RR) fashion while still preserving the benefits of the current first-ready FCFS (FR-FCFS) memory scheduling policy. Evaluations with different multi-application workloads demonstrate that the proposed memory scheduling policy, first-ready round-robin FCFS (FR-RR-FCFS), improves fairness and delivers better system performance compared to the existing FR-FCFS memory scheduling scheme.


Adwait Jog (Pennsylvania State University)
Evgeny Bolotin (NVIDIA)
Zvika Guz (NVIDIA)
Mike Parker (Intel)
Mahmut T. Kandemir (Pennsylvania State University)
Chita R. Das (Pennsylvania State University)

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