Sampling Dead Block Prediction for Last-Level Caches

Khan, Samira; Tian, Yingying; Jiménez, Daniel

doi:10.1109/micro.2010.24

Cited by 168 publications

(135 citation statements)

References 20 publications

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“…Figure 4 shows that for a 16-way 2MB LLC, the optimal bypass with LRU (LRU+OB) reduces average misses by 23.5% compared to LRU, and it bridges roughly four-fifths of the gap between LRU and OPT+B. Compared to other recent proposals like DRRIP [12] and SDBP [19], it outperforms them dramatically. Although it is still impractical to implement the optimal bypass due to the need of future information, we can learn the past behavior of the optimal bypass to predict its future behavior.…”

Section: Motivationmentioning

confidence: 98%

“…For a 1024-entry BDCT, the lower 10 bits of PC are used to index the BDCT. Like all PC based methods [17,19,20,21,23,35,37], the shortened PC is delivered along with the request through the cache hierarchies.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Figure 7 shows misses per thousand instructions (MPKI) normalized to LRU for OBM with NRU (NRU+OBM) and other techniques. Besides the baseline NRU and NRU+OBM, we also investigate the recently proposed techniques including DIP [27], DRRIP 3 [12], sampling dead block prediction (SDBP) [19], and dueling segmented LRU with adaptive bypassing (DSB) 4 [6]. While the MPKI of NRU is similar to that of LRU, NRU+OBM reduces MPKI by 15.5% compared to L-RU on average, and the best reduction is 65.1% for sphinx3.…”

Section: Obm With Nrumentioning

confidence: 99%

“…In order to achieve such goals, various replacement policies [12,27,38] attempt to insert distant reuse blocks into the LRU position. Dead block prediction techniques [10,19,20,21,22] try to identify distant reuse blocks to evict them earlier. Several adaptive methods [12,27,28,32] dynamically change between different replacement policies to accommodate to the changing access patterns.…”

Section: Introductionmentioning

confidence: 99%

“…On average it outperforms LRU by 9.7% and 8.9% for single-thread and multi-programmed workloads respectively in the absence of prefetching, and it can also improve performance significantly in the presence of prefetching. Compared to other stateof-the-art proposals including DRRIP [12], SDBP [19], and DSB [6], OBM with NRU has superior performance while requiring less storage overhead.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal bypass monitor for high performance last-level caches

Tong

Xie

et al. 2012

Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques

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In the last-level cache, large amounts of blocks have reuse distances greater than the available cache capacity. Cache performance and efficiency can be improved if some subset of these distant reuse blocks can reside in the cache longer. The bypass technique is an effective and attractive solution that prevents the insertion of harmful blocks.Our analysis shows that bypass can contribute significant performance improvement, and the optimal bypass can achieve similar performance compared to OPT+B, which is the theoretical optimal replacement policy. Thus, we propose a bypass technique called Optimal Bypass Monitor (OBM), which makes bypass decisions by learning and predicting the behavior of the optimal bypass. OBM keeps a short global track of the incoming-victim block pairs. By detecting the first reuse block in each pair, the behavior of the optimal bypass on the track can be asserted to guide the bypass choice.Any existing replacement policy can be extended with OBM while requiring negligible design modification. Our experimental results show that using less than 1.5KB extra memory, OBM with the NRU replacement policy outperforms LRU by 9.7% and 8.9% for single-thread and multiprogrammed workloads respectively. Compared with other state-of-the-art proposals such as DRRIP and SDBP, it achieves superior performance with less storage overhead.

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Section: Motivationmentioning

confidence: 98%

Section: Implementation Detailsmentioning

confidence: 99%

Section: Obm With Nrumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal bypass monitor for high performance last-level caches

Tong

Xie

et al. 2012

Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques

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RUE: A caching method for identifying and managing hot data by leveraging resource utilization efficiency

Liang

Deng

et al. 2021

Softw Pract Exp

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In this study, we propose a caching method called RUE for dynamic large‐scale data streams. We define a data model to facilitate hot data identification and management. At the heart of RUE model is hot degree that takes into account two factors data resource utilization efficiency and reuse distance, aiming to quantitatively reflect data popularity in a dynamic data stream. Based on data's hot degree, RUE classifies data into four types, each of which is assigned with an associated cache residence time. Guided by RUE model, we develop HM algorithm to identify and manage hot data in a dynamic data stream. HM algorithm is implemented by four stacks, namely, new stack, short stack, long stack, and temp stack. Moreover, an eviction and a migration algorithms are integrated into HM to facilitate block replacement and migration. To evaluate the performance of HM algorithm, we quantitatively compare the performance of RUE with three state‐of‐art algorithms, namely, LRU, LIRS, and ARC under various replacement policies, operations, and workloads. Experimental results show that RUE outperforms these three existing algorithms in terms of both read and write hit rates. Furthermore, we show that with the four stacks in place, the computing overhead of HM is negligible.

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