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CaLRS: A Critical-Aware Shared LLC Request Scheduling Algorithm on GPGPU

机译:CaLRS:GPGPU上的关键感知共享LLC请求调度算法

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摘要

Ultra high thread-level parallelism in modern GPUs usually introduces numerous memory requests simultaneously. So there are always plenty of memory requests waiting at each bank of the shared LLC (L2 in this paper) and global memory. For global memory, various schedulers have already been developed to adjust the request sequence. But we find few work has ever focused on the service sequence on the shared LLC. We measured that a big number of GPU applications always queue at LLC bank for services, which provide opportunity to optimize the service order on LLC. Through adjusting the GPU memory request service order, we can improve the schedulability of SM. So we proposed a critical-aware shared LLC request scheduling algorithm (CaLRS) in this paper. The priority representative of memory request is critical for CaLRS. We use the number of memory requests that originate from the same warp but have not been serviced when they arrive at the shared LLC bank to represent the criticality of each warp. Experiments show that the proposed scheme can boost the SM schedulability effectively by promoting the scheduling priority of the memory requests with high criticality and improves the performance of GPU indirectly.
机译:现代GPU中的超高线程级并行性通常会同时引入大量内存请求。因此,在共享LLC(在本文中为L2)的每个存储区和全局内存中总会有大量的内存请求在等待。对于全局存储器,已经开发了各种调度程序来调整请求顺序。但是我们发现很少有工作集中在共享LLC上的服务序列上。我们测量到,大量GPU应用程序总是在LLC银行排队等待服务,这为优化LLC上的服务顺序提供了机会。通过调整GPU内存请求服务顺序,可以提高SM的可调度性。因此,本文提出了一种关键感知的共享LLC请求调度算法(CaLRS)。代表内存请求的优先级对于CaLRS至关重要。我们使用源自同一扭曲的内存请求数量,但当它们到达共享LLC银行时尚未得到服务,以表示每个扭曲的严重性。实验表明,该方案通过提高存储请求的调度优先级,可以有效提高SM的可调度性,间接提高了GPU的性能。

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