Miss Rate Prediction Across Program Inputs and Cache Configurations

Zhong, Yutao; Dropsho, Steven; Shen, Xipeng; Studer, Ahren; Ding, Chen

doi:10.1109/tc.2007.50

Cited by 50 publications

(33 citation statements)

References 30 publications

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“…Another dimension is for different runs of a program on different inputs. Ding et al demonstrate the predictability of reuse distance histograms across program runs and use them to estimate cache misses of a program across different runs [9,22,29]. In the same way, we can use the approximated reuse distance histograms from the statistical model to serve that estimation.…”

Section: Uses For Cache Miss Rate Predictionmentioning

confidence: 99%

Locality approximation using time

Shen

Shaw²,

Meeker

et al. 2007

Proceedings of the 34th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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Locality, characterized by data reuses, determines caching performance. Reuse distance (i.e. LRU stack distance) precisely characterizes program locality and has been used in memory-related research since 1970s. However, the high cost of measuring it still urges a breakthrough before its practical uses in performance debugging, locality analysis and optimizations of long-running applications.In this work, we improve the efficiency by exploring the connection between time and locality. We propose a statistical model converting cheap time distance to costly reuse distance. Compared to the state-of-the-art technique, this approach reduces measuring time by 17 times, and approximates cache line reuses with over 99% accuracy. Experiments demonstrate the effective uses of the approximated reuse distance in cache miss rate prediction. This work, for the first time, reveals the strong correlations between time and locality. It makes precise locality as easy to obtain as data access frequency, removes the obstacles to reuse distance's practical uses, and opens new opportunities for program optimizations.

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Section: Uses For Cache Miss Rate Predictionmentioning

confidence: 99%

Locality approximation using time

Shen

Shaw²,

Meeker

et al. 2007

Proceedings of the 34th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

Self Cite

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“…Section II-D derives the relationship between our probability-based measures and reuse histograms and provides a theoretical justification for reuse histograms. Based on this relationship, previous works on reuse distances such as component-based locality analysis [8] [19] can also be adapted to our proposed measure. In [10], masscount disparity is used to show that most reuses are from accessing a small number of addresses.…”

Section: A Gpu-based Parallel Algorithm For Locality Computationmentioning

confidence: 99%

Locality Principle Revisited: A Probability-Based Quantitative Approach

Gupta

Xiang

Yang

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium

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Abstract-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 probabilitybased 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.

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“…2) Profile Analysis. Based on program profiles from training runs, we detect the patterns of the program's perobject data reuse, object sizes and access frequencies as polynomial functions, using a pattern recognition algorithm based on the work in [10].…”

Section: Overview Of the Approachmentioning

confidence: 99%

Soft-OLP: Improving Hardware Cache Performance through Software-Controlled Object-Level Partitioning

Jiang

Ding

et al. 2009

2009 18th International Conference on Parallel Architectures and Compilation Techniques

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Abstract-Performance degradation of memory-intensive programs caused by the LRU policy's inability to handle weaklocality data accesses in the last level cache is increasingly serious for two reasons. First, the last-level cache remains in the CPU's critical path, where only simple management mechanisms, such as LRU, can be used, precluding some sophisticated hardware mechanisms to address the problem. Second, the commonly used shared cache structure of multi-core processors has made this critical path even more performance-sensitive due to intensive inter-thread contention for shared cache resources. Researchers have recently made efforts to address the problem with the LRU policy by partitioning the cache using hardware or OS facilities guided by run-time locality information. Such approaches often rely on special hardware support or lack enough accuracy. In contrast, for a large class of programs, the locality information can be accurately predicted if access patterns are recognized through small training runs at the data object level.To achieve this goal, we present a system-software framework referred to as Soft-OLP (Software-based Object-Level cache Partitioning). We first collect per-object reuse distance histograms and inter-object interference histograms via memory-trace sampling. With several low-cost training runs, we are able to determine the locality patterns of data objects. For the actual runs, we categorize data objects into different locality types and partition the cache space among data objects with a heuristic algorithm, in order to reduce cache misses through segregation of contending objects. The object-level cache partitioning framework has been implemented with a modified Linux kernel, and tested on a commodity multi-core processor. Experimental results show that in comparison with a standard L2 cache managed by LRU, Soft-OLP significantly reduces the execution time by reducing L2 cache misses across inputs for a set of single-and multi-threaded programs from the SPEC CPU2000 benchmark suite, NAS benchmarks and a computational kernel set.

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Miss Rate Prediction Across Program Inputs and Cache Configurations

Cited by 50 publications

References 30 publications

Locality approximation using time

Locality approximation using time

Locality Principle Revisited: A Probability-Based Quantitative Approach

Soft-OLP: Improving Hardware Cache Performance through Software-Controlled Object-Level Partitioning

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