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Ctrl-C: Instruction-Aware Control Loop Based Adaptive Cache Bypassing for GPUs

机译:Ctrl-C:针对GPU的基于指令感知控制环的自适应缓存绕过

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The performance of general-purpose graphics processing units (GPGPUs) is often limited by the efficiency of the memory subsystems, particularly the L1 data caches. Because of the massive multithreading computation paradigm, significant memory resource contention and cache thrashing are often observed in GPGPU workloads. This leads to high cache miss rates and substantial pipeline stall time. In order to improve the efficiency of GPU caches, we propose an instruction-aware control loop based adaptive cache bypassing design (Ctrl-C). Ctrl-C applies an instruction-aware algorithm to dynamically identify per-memory instruction cache reuse behavior. Ctrl-C then adopts feedback control loops to bypass memory requests probabilistically in order to protect cache lines with short reuse distances from early eviction. GPGPU-sim simulation based evaluation shows that Ctrl-C improves the performance of cache sensitive GPGPU workloads by 41.5%, leading to higher cache and interconnect bandwidth utilization with only an insignificant 3.5% area overhead.
机译:通用图形处理单元(GPGPU)的性能通常受内存子系统(尤其是L1数据高速缓存)的效率限制。由于存在大量的多线程计算范例,因此在GPGPU工作负载中经常会观察到显着的内存资源争用和缓存颠簸。这导致较高的高速缓存未命中率和大量的管道停顿时间。为了提高GPU缓存的效率,我们提出了一种基于指令感知控制环的自适应缓存旁路设计(Ctrl-C)。 Ctrl-C应用指令感知算法来动态识别每个内存的指令缓存重用行为。 Ctrl-C然后采用反馈控制循环以概率方式绕过内存请求,以保护重用距离较短的缓存行免遭早期驱逐。基于GPGPU-sim仿真的评估表明,Ctrl-C可以将对缓存敏感的GPGPU工作负载的性能提高41.5%,从而导致缓存和互连带宽利用率更高,而区域开销仅为3.5%。

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