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• W2987123212 abstract "GPUs are known to benefit structured applications with ample parallelism, such as deep learning in a datacenter. Recently, GPUs have shown promise for irregular streaming network tasks. However, the GPU's co-processor dependence on a CPU for task management, inefficiencies with fine-grained tasks, and limited multiprogramming capabilities introduce challenges with efficiently supporting latency-sensitive streaming tasks. This paper proposes an event-driven GPU execution model, EDGE, that enables non-CPU devices to directly launch preconfigured tasks on a GPU without CPU interaction. Along with freeing up the CPU to work on other tasks, we estimate that EDGE can reduce the kernel launch latency by 4.4xcompared to the baseline CPU-launched approach. This paper also proposes a warp-level preemption mechanism to further reduce the end-to-end latency of fine-grained tasks in a shared GPU environment. We evaluate multiple optimizations that reduce the average warp preemption latency by 35.9x over waiting for a preempted warp to naturally flush the pipeline. When compared to waiting for the first available resources, we find that warp-level preemption reduces the average and tail warp scheduling latencies by 2.6x and 2.9x, respectively, and improves the average normalized turnaround time by 1.4x." @default.
• W2987123212 created "2019-11-22" @default.
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• W2987123212 date "2019-09-01" @default.
• W2987123212 modified "2023-10-06" @default.
• W2987123212 title "EDGE: Event-Driven GPU Execution" @default.
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• W2987123212 doi "https://doi.org/10.1109/pact.2019.00034" @default.
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