The Periodic-Linear Model of Program Behavior Capture

Clauss, Philippe; Kenmei, Benedicte; Beyler, Jean Christophe

doi:10.1007/11549468_38

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…However, we found very few works on explicitly forming symbolic structures representing loops. One notable paper on this topic is [3], where the authors have the exact same objectives but use completely different means. They use periodic numbers to model loops, and build loops after having decomposed the input sequence into a hierarchy of periodic numbers.…”

Section: Related Workmentioning

confidence: 99%

Prediction and trace compression of data access addresses through nested loop recognition

Ketterlin

Clauss²

2008

Proceedings of the 6th Annual IEEE/ACM International Symposium on Code Generation and Optimization

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International audienceThis paper describes an algorithm that takes a trace (i.e., a sequence of numbers or vectors of numbers) as input, and from that produces a sequence of loop nests that, when run, produces exactly the original sequence. The input format is suitable for any kind of program execution trace, and the output conforms to standard models of loop nests. The first, most obvious, use of such an algorithm is for program behavior modeling for any measured quantity (memory accesses, number of cache misses, etc.). Finding loops amounts to detecting periodic behavior and provides an explanatory model. The second application is trace compression, i.e., storing the loop nests instead of the original trace. Decompression consists of running the loops, which is easy and fast. A third application is value prediction. Since the algorithm forms loops while reading input, it is able to extrapolate the loop under construction to predict further incoming values. Throughout the paper, we provide examples that explain our algorithms. Moreover, we evaluate trace compression and value prediction on a subset of the SPEC2000 benchmarks

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Section: Related Workmentioning

confidence: 99%

Prediction and trace compression of data access addresses through nested loop recognition

Ketterlin

Clauss²

2008

Proceedings of the 6th Annual IEEE/ACM International Symposium on Code Generation and Optimization

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“…This section organizes related work according to their ultimate goal, also discussing the potential applications of the exploration engine proposed in this paper. Clauss et al [6] characterized program behavior using polynomial piecewise periodic and linear interpolations separated into adjacent program phases to reduce function complexity. The model can be recursively applied, interpreting coefficients of the periodic interpolation as traces in themselves.…”

Section: Related Work and Applicationsmentioning

confidence: 99%

Trace-based affine reconstruction of codes

Rodriguez

Andión

Kandemir

et al. 2016

Proceedings of the 2016 International Symposium on Code Generation and Optimization

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Complete comprehension of loop codes is desirable for a variety of program optimizations. Compilers perform static code analyses and transformations, such as loop tiling or memory partitioning, by constructing and manipulating formal representations of the source code. Runtime systems observe and characterize application behavior to drive resource management and allocation, including dependence detection and parallelization, or scheduling. However, the source codes of target applications are not always available to the compiler or runtime system in an analyzable form. It becomes necessary to find alternate ways to model application behavior. This paper presents a novel mathematical framework to rebuild loops from their memory access traces. An exploration engine traverses a tree-like solution space, driven by the access strides in the trace. It is guaranteed that the engine will find the minimal affine nest capable of reproducing the observed sequence of accesses by exploring this space in a brute force fashion, but most real traces will not be tractable in this way. Methods for an efficient solution space traversal based on mathematical properties of the equation systems which model the solution space are proposed. The experimental evaluation shows that these strategies achieve efficient loop reconstruction, processing hundreds of gigabytes of trace data in minutes. The proposed approach is capable of correctly and minimally reconstructing 100% of the static control parts in PolyBench/C applications. As a side effect, the trace reconstruction process can be used to efficiently compress trace files. The proposed tool can also be used for dynamic access characterization, predicting over 99% of future memory accesses.

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“…Compression of memory reference traces has been extensively studied in the literature and falls into three broad categories -inferring loops with a variety of properties [11], [12], [13], [14], [15], inferring grammars [16], [17], [18], and employing predictors such as value predictors [19], [20], [21]. Each category has its benefits and limitations and work on addressing and exploiting them has been done in the past [22], [23], [24].…”

mentioning

confidence: 99%

Scalable Communication Trace Compression

Krishnamoorthy

Agarwal

2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

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Characterizing the communication behavior of parallel programs through tracing can help understand an application's characteristics, model its performance, and predict behavior on future systems. However, lossless communication traces can get prohibitively large, causing programmers to resort to variety of other techniques. In this paper, we present a novel approach to lossless communication trace compression. We augment the sequitur compression algorithm to employ it in communication trace compression of parallel programs. We present optimizations to reduce the memory overhead, reduce size of the trace files generated, and enable compression across multiple processes in a parallel program. The evaluation shows improved compression and reduced overhead over other approaches, with up to 3 orders of magnitude improvement for the NAS MG benchmark. We also observe that, unlike existing schemes, the trace files sizes and the memory overhead incurred are less sensitive to, if not independent of, the problem size for the NAS benchmarks.

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The Periodic-Linear Model of Program Behavior Capture

Cited by 8 publications

References 6 publications

Prediction and trace compression of data access addresses through nested loop recognition

Prediction and trace compression of data access addresses through nested loop recognition

Trace-based affine reconstruction of codes

Scalable Communication Trace Compression

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