Compile-Time Based Performance Prediction

Caşcaval, Călin; Rose, Luiz De; Padua, David; Reed, Daniel A.

doi:10.1007/3-540-44905-1_23

Cited by 46 publications

(48 citation statements)

References 19 publications

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“…al [33] to perform compile-time based performance predictions. This application of the stack distance model has no requirement for address trace storage, but this work may be complementary in that the PSnaP profiles can be partially generated from compile-time statistics and yields higher accuracy than the stack distance model.…”

Section: Related Workmentioning

confidence: 99%

PSnAP: Accurate Synthetic Address Streams through Memory Profiles

Olschanowsky

Tikir

Carrington

et al. 2010

Languages and Compilers for Parallel Computing

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Abstract. Memory address traces are an important information source; they drive memory simulations for performance modeling, systems design and application tuning. For long running applications, the direct use of an address trace is complicated by its size. Previous attempts to reduce trace size incurred a substantial penalty with respect to trace accuracy. We propose a novel method of memory profiling that enables the generation of highly accurate synthetic traces with space requirements typically under 1% of the original traces. We demonstrate the synthetic trace accuracy in terms of cache hit rates, spatial-temporal locality scores and locality surfaces. Simulated cache hit rates from synthetic traces are within 3.5% of observed and on average are within 1.0% for L1 cache. Our profiles are on average 60 times smaller than compressed traces. The combination of small profile sizes and high similarity to original traces makes our technique uniquely applicable to performance modeling and trace driven simulation of large-scale parallel scientific applications.

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Section: Related Workmentioning

confidence: 99%

PSnAP: Accurate Synthetic Address Streams through Memory Profiles

Olschanowsky

Tikir

Carrington

et al. 2010

Languages and Compilers for Parallel Computing

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“…Sharing of processor execution units, address translation caches, external processor buses and similar features found in contemporary hardware architectures potentially impacts performance. These are only rarely captured by performance models [10], and usually only for evaluating hardware design rather than software system. Typical performance models assume resources are mostly independent.…”

mentioning

confidence: 99%

Validating Model-Driven Performance Predictions on Random Software Systems

Babka

Tůma

Bulej

2010

Research Into Practice – Reality and Gaps

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Abstract. Software performance prediction methods are typically validated by taking an appropriate software system, performing both performance predictions and performance measurements for that system, and comparing the results. The validation includes manual actions, which makes it feasible only for a small number of systems. To significantly increase the number of systems on which software performance prediction methods can be validated, and thus improve the validation, we propose an approach where the systems are generated together with their models and the validation runs without manual intervention. The approach is described in detail and initial results demonstrating both its benefits and its issues are presented.Key words: performance modeling, performance validation, MDD MotivationState of the art in model-driven software performance prediction builds on three related factors: the availability of architectural and behavioral software models, the ability to solve performance models, and the ability to transform the former models into the latter. This is illustrated for example by the survey of modeldriven software performance prediction [3], which points out that the typical approach is to use UML diagrams for specifying both the architecture and the behavior of the software system, and to transform these diagrams into performance models based on queueing networks.Both the models and the methods involved in the prediction process necessarily include simplifying assumptions that help abstract away from some of the complexities of the modeled system, e.g., approximating real operation times with probability distributions or assuming independence of operation times. These simplifications are necessary to make the entire prediction process tractable, but the complexity of the modeled system usually makes it impossible to say how the simplifications influence the prediction precision.Self-archived copy. The original publication is available at www.springerlink.com,

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“…...... This is clearly a simplistic estimate, but it can be improved using existing compiler-driven modeling techniques for sequential code (e.g., [9]). The specific choice of this model is orthogonal to the optimizations we have proposed.…”

Section: Lookahead Extractionmentioning

confidence: 99%