The rise of general-purpose computing on GPUs has influenced architectural innovation on them. The introduction of an on-chip cache hierarchy is one such innovation. High L1 miss rates on GPUs, however, indicate inefficient cache usage due to myriad factors, such as cache thrashing and extensive multithreading. Such high L1 miss rates in turn place high demands on the shared L2 bandwidth. Extensive congestion in the L2 access path therefore results in high memory access latencies. In memory-intensive applications, these latencies get exposed due to a lack of active compute threads to mask such high latencies. In this article, we aim to reduce the pressure on the shared L2 bandwidth, thereby reducing the memory access latencies that lie in the critical path. We identify significant replication of data among private L1 caches, presenting an opportunity to reuse data among L1s. We further show how this reuse can be exploited via an L1 Cooperative Caching Network (CCN), thereby reducing the bandwidth demand on L2. In the proposed architecture, we connect the L1 caches with a lightweight ring network to facilitate intercore communication of shared data. We show that this technique reduces traffic to the L2 cache by an average of 29%, freeing up the bandwidth for other accesses. We also show that the CCN reduces the average memory latency by 24%, thereby reducing core stall cycles by 26% on average. This translates into an overall performance improvement of 14.7% on average (and up to 49%) for applications that exhibit reuse across L1 caches. In doing so, the CCN incurs a nominal area and energy overhead of 1.3% and 2.5%, respectively. Notably, the performance improvement with our proposed CCN compares favorably to the performance improvement achieved by simply doubling the number of L2 banks by up to 34%.

Abstract-GPUs are often limited by off-chip memory bandwidth. With the advent of general-purpose computing on GPUs, a cache hierarchy has been introduced to filter the bandwidth demand to the off-chip memory. However, the cache hierarchy presents its own bandwidth limitations in sustaining such high levels of memory traffic.In this paper, we characterize the bandwidth bottlenecks present across the memory hierarchy in GPUs for generalpurpose applications. We quantify the stalls throughout the memory hierarchy and identify the architectural parameters that play a pivotal role in leading to a congested memory system. We explore the architectural design space to mitigate the bandwidth bottlenecks and show that performance improvement achieved by mitigating the bandwidth bottleneck in the cache hierarchy can exceed the speedup obtained by a memory system with a baseline cache hierarchy and High Bandwidth Memory (HBM) DRAM. We also show that addressing the bandwidth bottleneck in isolation at specific levels can be sub-optimal and can even be counter-productive. Therefore, we show that it is imperative to resolve the bandwidth bottlenecks synergistically across different levels of the memory hierarchy. With the insights developed in this paper, we perform a cost-benefit analysis and identify costeffective configurations of the memory hierarchy that effectively mitigate the bandwidth bottlenecks. We show that our final configuration achieves a performance improvement of 29% on average with a minimal area overhead of 1.6%.

GPUs are often limited by the off-chip memory bandwidth. With the advent of general-purpose computing on GPUs, cache hierarchy has been introduced to filter the bandwidth demand to the off-chip memory. However, the cache hierarchy presents its own bandwidth limitations in sustaining such high levels of memory traffic. In this work, we characterize the bandwidth bottleneck in GPUs present across the memory hierarchy for general-purpose applications. We show that the improvement in performance achieved by mitigating the bandwidth bottleneck in the cache hierarchy can exceed the speedup obtained by a memory system with a baseline cache hierarchy and high bandwidth off-chip memory. We also show that addressing the bandwidth bottleneck in isolation at specific levels can be sub-optimal and can even be counter-productive. Therefore, we show that it is imperative to resolve the bandwidth bottleneck synergistically across different levels of the memory hierarchy.

GPUs employ a high degree of thread-level parallelism (TLP) to hide the long latency of memory operations. However, the consequent increase in demand on the memory system causes pathological effects such as cache thrashing and bandwidth bottlenecks. As a result, high degrees of TLP can adversely affect system throughput. In this paper, we present Poise, a novel approach for balancing TLP and memory system performance in GPUs. Poise has two major components: a machine learning framework and a hardware inference engine. The machine learning framework comprises a regression model that is trained offline on a set of profiled kernels to learn best warp scheduling decisions. At runtime, the hardware inference engine uses the previously learned model to dynamically predict best warp scheduling decisions for unseen applications. Therefore, Poise helps in optimizing entirely new applications without posing any profiling, training or programming burden on the end-user. Across a set of benchmarks that were unseen during training, Poise achieves a speedup of up to 2.94× and a harmonic mean speedup of 46.6%, over the baseline greedythen-oldest warp scheduler. Poise is extremely lightweight and incurs a minimal hardware overhead of around 41 bytes per SM. It also reduces the overall energy consumption by an average of 51.6%. Furthermore, Poise outperforms the prior state-ofthe-art warp scheduler by an average of 15.1%. In effect, Poise solves a complex hardware optimization problem with considerable accuracy and efficiency.

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