Back to the Future: Leveraging Belady's Algorithm for Improved Cache Replacement

Jain, Anshul; Lin, Calvin

doi:10.1109/isca.2016.17

Cited by 95 publications

(113 citation statements)

References 31 publications

Supporting

Mentioning

111

Contrasting

Unclassified

Order By: Relevance

“…SHiP [14], an insertion policy, has the lowest storage cost at the expense of not predicting blocks that are reused. Among DBPs that also predict reused blocks, the preferred Leeway NRU configuration requires 44KB of storage in total (including NRU bits), compared to 38.75KB for Hawkeye [5] and 31KB for SDBP [2], considering same number of sample sets and predictor table entries for all techniques. While Leeway is slightly more expensive, we observe that the storage requirements for all techniques are in a similar range of several tens of KBs.…”

Section: E Cost and Complexity Analysismentioning

confidence: 99%

“…In contrast, state-of-the-art DBPs, such as SDBP [2] and Hawkeye [5], use a PC-indexed prediction table that is probed on every LLC access (including hits) to inform the block's eviction priority. For example, Hawkeye incurs ∼2.3x more accesses to its prediction table when compared to Leeway (SPEC average).…”

Section: E Cost and Complexity Analysismentioning

confidence: 99%

“…We evaluate the performance of SPEC CPU 2006 benchmarks using a modified version of CMP$im [15] provided with the JILP Cache Replacement Championship [16] and used in prior research in dead block prediction [2], [4], [5], [14]. Table III summarizes the features of the simulated processor.…”

Section: Spec Cpu 2006mentioning

confidence: 99%

“…Note that prior work in this space has used only a single simpoint in their evaluation [2], [5], [14]. We use SimPoint tool to generate the weights for each simpoint that are then used to calculate the overall performance.…”

Section: Spec Cpu 2006mentioning

confidence: 99%

“…Hawkeye [5] learns a block's behavior by simulating Belady's optimal algorithm [23] and trains the predictor that, on each cache access, updates the block's eviction priority. The authors kindly provided the source code of their technique, which we use for evaluation of SPEC workloads.…”

Section: B Evaluated Prediction Schemesmentioning

confidence: 99%

See 4 more Smart Citations

Leeway: Addressing Variability in Dead-Block Prediction for Last-Level Caches

Faldu¹,

Grot²

2017

2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT)

View full text Add to dashboard Cite

Abstract-The looming breakdown of Moore's Law and the end of voltage scaling are ushering a new era where neither transistors nor the energy to operate them is free. This calls for a new regime in computer systems, one in which every transistor counts. Caches are essential for processor performance and represent the bulk of modern processor's transistor budget. To get more performance out of the cache hierarchy, future processors will rely on effective cache management policies.This paper identifies variability in generational behavior of cache blocks as a key challenge for cache management policies that aim to identify dead blocks as early and as accurately as possible to maximize cache efficiency. We show that existing management policies are limited by the metrics they use to identify dead blocks, leading to low coverage and/or low accuracy in the face of variability. In response, we introduce a new metric -Live Distance -that uses the stack distance to learn the temporal reuse characteristics of cache blocks, thus enabling a dead block predictor that is robust to variability in generational behavior. Based on the reuse characteristics of an application's cache blocks, our predictor -Leeway -classifies application's behavior as streaming-oriented or reuse-oriented and dynamically selects an appropriate cache management policy. By leveraging live distance for LLC management, Leeway outperforms state-of-the-art approaches on single-and multi-core SPEC and manycore CloudSuite workloads.

show abstract

Section: E Cost and Complexity Analysismentioning

confidence: 99%

Section: E Cost and Complexity Analysismentioning

confidence: 99%

Section: Spec Cpu 2006mentioning

confidence: 99%

Section: Spec Cpu 2006mentioning

confidence: 99%

Section: B Evaluated Prediction Schemesmentioning

confidence: 99%

See 3 more Smart Citations

Leeway: Addressing Variability in Dead-Block Prediction for Last-Level Caches

Faldu¹,

Grot²

2017

2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT)

View full text Add to dashboard Cite

show abstract

ECR: Eviction‐cost‐aware cache management policy for page‐level flash‐based SSDs

Chen

2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary Cache management policy plays a key role in offering low latency access to flash‐based SSDs. Most existing solutions including LRU and its successors only focus on improving the cache hit ratio, but rarely consider to reduce the waiting time of the eviction operation in the page‐level mapping FTLs. As the workloads spreading across internal chips of modern flash‐based SSDs are often highly imbalanced when workloads are write‐intensive, the time cost of evicting a dirty page from cache varies in a wide range. In this paper, we propose a novel eviction‐cost‐aware cache management policy, called ECR, to minimize the eviction cost in write‐dominant applications. ECR gives a higher probability to evict a page, which causes the shortest waiting time in the corresponding chip queue. To achieve this, we introduce a monitor module to keep track of states of all chip queues, and a multi‐LRU list structure to accelerate the selection of a victim chip and a target page in cache to perform an eviction. Our experimental results show that ECR can significantly reduce the average response time by as much as 59.55% and 44.84% compared to LRU and GCaR‐CFLRU, respectively, where GCaR‐CFLRU is the combination of state‐of‐the‐art algorithm GCaR and CFLRU.

show abstract

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

Liang

Deng

et al. 2021

Softw Pract Exp

View full text Add to dashboard Cite

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.

show abstract

Back to the Future: Leveraging Belady's Algorithm for Improved Cache Replacement

Cited by 95 publications

References 31 publications

Leeway: Addressing Variability in Dead-Block Prediction for Last-Level Caches

Leeway: Addressing Variability in Dead-Block Prediction for Last-Level Caches

ECR: Eviction‐cost‐aware cache management policy for page‐level flash‐based SSDs

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

Contact Info

Product

Resources

About