Tag-Split Cache for Efficient GPGPU Cache Utilization

Lingda Li, Ari B. Hayes, S. Song, E. Zhang
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引用次数: 11

Abstract

Modern GPUs employ cache to improve memory system efficiency. However, large amount of cache space is underutilized due to irregular memory accesses and poor spatial locality which exhibited commonly in GPU applications. Our experiments show that using smaller cache lines could improve cache space utilization, but it also frequently suffers from significant performance loss by introducing large amount of extra cache requests. In this work, we propose a novel cache design named tag-split cache (TSC) that enables fine-grained cache storage to address the problem of cache space underutilization while keeping memory request number unchanged. TSC divides tag into two parts to reduce storage overhead, and it supports multiple cache line replacement in one cycle. TSC can also automatically adjust cache storage granularity to avoid performance loss for applications with good spatial locality. Our evaluation shows that TSC improves the baseline cache performance by 17.2% on average across a wide range of applications. It also out-performs other previous techniques significantly.
标签分割缓存高效GPGPU缓存利用率
现代gpu采用缓存来提高内存系统的效率。然而,由于不规则的内存访问和较差的空间局部性,大量的缓存空间未被充分利用,这在GPU应用中很常见。我们的实验表明,使用较小的缓存线可以提高缓存空间的利用率,但是由于引入了大量额外的缓存请求,它也经常遭受显著的性能损失。在这项工作中,我们提出了一种新的缓存设计,称为标签分割缓存(TSC),它使细粒度缓存存储能够解决缓存空间利用率不足的问题,同时保持内存请求数不变。TSC将标签分成两部分,以减少存储开销,并支持在一个周期内更换多条缓存线。TSC还可以自动调整缓存存储粒度,以避免具有良好空间局部性的应用程序的性能损失。我们的评估表明,在广泛的应用程序中,TSC将基准缓存性能平均提高了17.2%。它的性能也明显优于以前的其他技术。
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