节点内内存安全的GPU协同调度
Intra-node Memory Safe GPU Co-Scheduling
摘要：
高性能计算系统中的GPU仍然没有得到充分利用，这是因为无法安全地调度多个应用程序共享同一GPU。本文中报告的研究旨在通过提出一个框架来提高GPU的利用率，我们称之为schedGPU，以促进节点内GPU协同调度，从而通过考虑内存约束，可以在多个应用程序之间安全地共享GPU。两种方法，即客户端-服务器和共享内存的方法进行了探讨。然而，与前一种方法相比，共享内存方法更适合，因为其开销更低。schedGPU中提出了四种策略来处理等待访问GPU的应用程序，其中两种占优先级。在三个实际应用中验证了schedGPU的可行性。关键的观察结果是实现了性能增益。对于单个应用程序，获得超过10倍的增益，如通过GPU利用率和GPU内存利用率测量的。对于包含多个应用程序的工作负载，总执行时间的加速高达5倍。此外，平均GPU利用率和平均GPU内存利用率分别提高了5倍和12倍。
英文摘要：
GPUs in High-Performance Computing systems remain under-utilised due to the unavailability of schedulers that can safely schedule multiple applications to share the same GPU. The research reported in this paper is motivated to improve the utilisation of GPUs by proposing a framework, we refer to as schedGPU, to facilitate intra-node GPU co-scheduling such that a GPU can be safely shared among multiple applications by taking memory constraints into account. Two approaches, namely a client-server and a shared memory approach are explored. However, the shared memory approach is more suitable due to lower overheads when compared to the former approach. Four policies are proposed in schedGPU to handle applications that are waiting to access the GPU, two of which account for priorities. The feasibility of schedGPU is validated on three real-world applications. The key observation is that a performance gain is achieved. For single applications, a gain of over 10 times, as measured by GPU utilisation and GPU memory utilisation, is obtained. For workloads comprising multiple applications, a speed-up of up to 5x in the total execution time is noted. Moreover, the average GPU utilisation and average GPU memory utilisation is increased by 5 and 12 times, respectively.
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