@article{gupta_gao_zhou_2013, title={Adaptive Cache Bypassing for Inclusive Last Level Caches}, ISSN={["1530-2075"]}, DOI={10.1109/ipdps.2013.16}, abstractNote={Cache hierarchy designs, including bypassing, replacement, and the inclusion property, have significant performance impact. Recent works on high performance caches have shown that cache bypassing is an effective technique to enhance the last level cache (LLC) performance. However, commonly used inclusive cache hierarchy cannot benefit from this technique because bypassing inherently breaks the inclusion property. This paper presents a solution to enabling cache bypassing for inclusive caches. We introduce a bypass buffer to an LLC. Bypassed cache lines skip the LLC while their tags are stored in this bypass buffer. When a tag is evicted from the bypass buffer, it invalidates the corresponding cache lines in upper level caches to ensure the inclusion property. Our key insight is that the lifetime of a bypassed line, assuming a well-designed bypassing algorithm, should be short in upper level caches and is most likely dead when its tag is evicted from the bypass buffer. Therefore, a small bypass buffer is sufficient to maintain the inclusion property and to reap most performance benefits of bypassing. Furthermore, the bypass buffer facilitates bypassing algorithms by providing the usage information of bypassed lines. We show that a top performing cache bypassing algorithm, which is originally designed for non-inclusive caches, performs comparably for inclusive caches equipped with our bypass buffer. The usage information collected from the bypass buffer also significantly reduces the cost of hardware implementation compared to the original design.}, journal={IEEE 27TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2013)}, author={Gupta, Saurabh and Gao, Hongliang and Zhou, Huiyang}, year={2013}, pages={1243–1253} }
 @article{gupta_xiang_yang_zhou_2013, title={Locality principle revisited: A probability-based quantitative approach}, volume={73}, ISSN={["1096-0848"]}, DOI={10.1016/j.jpdc.2013.01.010}, abstractNote={This paper revisits the fundamental concept of the locality of references and proposes to quantify it as a conditional probability: in an address stream, given the condition that an address is accessed, how likely the same address (temporal locality) or an address within its neighborhood (spatial locality) will be accessed in the near future. Previous works use reuse distance histograms as a measure of temporal locality. For spatial locality, some ad hoc metrics have been proposed as a quantitative measure. In contrast, our conditional probability-based locality measure has a clear mathematical meaning and provides a theoretically sound and unified way to quantify both temporal and spatial locality. We showcase that our quantified locality measure can be used to evaluate compiler optimizations, to analyze the locality at different levels of memory hierarchy, to optimize the cache architecture to effectively leverage the locality, and to examine the effect of data prefetching mechanisms.}, number={7}, journal={JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING}, author={Gupta, Saurabh and Xiang, Ping and Yang, Yi and Zhou, Huiyang}, year={2013}, month={Jul}, pages={1011–1027} }
 @inproceedings{gupta_xiang_yang_huiyang_2012, title={Locality principle revisited: A probability-based quantitative approach}, DOI={10.1109/ipdps.2012.93}, abstractNote={This paper revisits the fundamental concept of the locality of references and proposes to quantify it as a conditional probability: in an address stream, given the condition that an address is accessed, how likely the same address (temporal locality) or an address within its neighborhood (spatial locality) will be accessed in the near future. Based on this definition, spatial locality is a function of two parameters, the neighborhood size and the scope of near future, and can be visualized with a 3D mesh. Temporal locality becomes a special case of spatial locality with the neighborhood size being zero byte. Previous works on locality analysis use stack/reuse distances to compute distance histograms as a measure of temporal locality. For spatial locality, some ad-hoc metrics have been proposed as a quantitative measure. In contrast, our conditional probability-based locality measure has a clear mathematical meaning, offers justification for distance histograms, and provides a theoretically sound and unified way to quantify both temporal and spatial locality. The proposed locality measure clearly exhibits the inherent application characteristics, from which we can easily derive information such as the sizes of the working data sets and how locality can be exploited. We showcase that our quantified locality visualized in 3D-meshes can be used to evaluate compiler optimizations, to analyze the locality at different levels of memory hierarchy, to optimize the cache architecture to effectively leverage the locality, and to examine the effect of data prefetching mechanisms. A GPU-based parallel algorithm is also presented to accelerate the locality computation for large address traces.}, booktitle={2012 ieee 26th international parallel and distributed processing symposium (ipdps)}, author={Gupta, S. and Xiang, P. and Yang, Y. and Huiyang}, year={2012}, pages={995–1009} }