A Modal Model of Memory

Mitchell, N.; Carter, Larry; Ferrante, Jeanne

doi:10.1007/3-540-45545-0_18

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…As a result it suffers from limited accuracy in many situations. The modal model of memory [Mitchell et al 2001] requires not only static analysis but also runtime experimentation (potentially thousands of experiments) in order to generate performance formulas. Such formulas can guide code transformation decisions by means of relative performance predictions, but they cannot predict code performance in terms of miss rates or execution time.…”

Section: Related Workmentioning

confidence: 99%

“…In the past few years, accurate and automatable analytical models able to analyze whole programs with regular access patterns have appeared [Ghosh et al 1999;Chatterjee et al 2001;Vera and Xue 2002;Fraguela et al 2003]. Nevertheless, none of the approaches to model the cache behavior of codes with irregular access patterns because of indirections proposed so far [Fraguela et al 1998;Ladner et al 1999;Mitchell et al 2001] is both automatable and reasonably accurate. This is despite the fact that the analysis of the cache behavior in the presence of irregular access patterns is very important, since many relevant applications exhibit them, and cache performance usually drops in their presence as a result of their lack of locality.…”

Section: Introductionmentioning

confidence: 99%

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Precise automatable analytical modeling of the cache behavior of codes with indirections

Andrade

Fraguela

Doallo

2007

ACM Trans. Archit. Code Optim.

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The performance of memory hierarchies, in which caches play an essential role, is critical in nowadays general-purpose and embedded computing systems because of the growing memory bottleneck problem. Unfortunately, cache behavior is very unstable and difficult to predict. This is particularly true in the presence of irregular access patterns, which exhibit little locality. Such patterns are very common, for example, in applications in which pointers or compressed sparse matrices give place to indirections. Nevertheless, cache behavior in the presence of irregular access patterns has not been widely studied. In this paper we present an extension of a systematic analytical modeling technique based on PMEs (probabilistic miss equations), previously developed by the authors, that allows the automated analysis of the cache behavior for codes with irregular access patterns resulting from indirections. The model generates very accurate predictions despite the irregularities and has very low computing requirements, being the first model that gathers these desirable characteristics that can automatically analyze this kind of codes. These properties enable this model to help drive compiler optimizations, as we show with an example.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Precise automatable analytical modeling of the cache behavior of codes with indirections

Andrade

Fraguela

Doallo

2007

ACM Trans. Archit. Code Optim.

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“…Mitchell, et al, proposed a scheme in which models of various types of memory access patterns are measured for a given machine when the compiler is installed [61]. At analysis time, memory references within loop nests are decomposed and modeled by functions of these canonical patterns.…”

Section: Self-tuning Compilersmentioning

confidence: 99%

Statistical Models for Empirical Search-Based Performance Tuning

Vuduc

Demmel

Bilmes

2004

The International Journal of High Performance Computing Applica

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Achieving peak performance from the computational kernels that dominate application performance often requires extensive machine-dependent tuning by hand. Automatic tuning systems have emerged in response, and they typically operate by (1) generating a large number of possible, reasonable implementations of a kernel, and (2) selecting the fastest implementation by a combination of heuristic modeling, heuristic pruning, and empirical search (i.e., actually running the code). This paper presents quantitative data that motivates the development of such a search-based system, using dense matrix multiply as a case study. The statistical distributions of performance within spaces of reasonable implementations, when observed on a variety of hardware platforms, lead us to pose and address two general problems which arise during the search process. First, we develop a heuristic for stopping an exhaustive compile-time search early if a near-optimal implementation is found. Second, we show how to construct run-time decision rules, based on run-time inputs, for selecting from among a subset of the best implementations when the space of inputs can be described by continuously varying features. We address both problems by using statistical modeling techniques that exploit the large amount of performance data collected during the search. We demonstrate these methods on actual performance data collected by the PHiPAC tuning system for dense matrix multiply.We close with a survey of recent projects that use or otherwise advocate an empirical search-based approach to code generation and algorithm selection, whether at the level of computational kernels, compiler and run-time systems, or problem-solving environments. Collectively, these efforts suggest a number of possible software architectures for constructing platform-adapted libraries and applications.

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“…Modal model of memory [11] categorize data accesses as constant, strided and non-monotonic modes. Yan et al [15] classify memory access patterns into three types: migratory, group and unpredictable patterns.…”

Section: Data Access Patternsmentioning

confidence: 99%