Learning-based analytical cross-platform performance prediction

Zheng, Xinnian; Ravikumar, Pradeep; John, Lizy K.; Gerstlauer, Andreas

doi:10.1109/samos.2015.7363659

Cited by 26 publications

(10 citation statements)

References 16 publications

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“…Cross-architecture models can predict the performance of an application from one CPU to another and can overcome ISA and microarchitectural differences with 90 % accuracy on computationally-intensive embedded kernels [36], and up to 97 % accuracy for both performance and power consumption when prediction is performed on the granularity of program phases [37]. Like HALWPE, the predictive features are performance counter measurements obtained from direct execution, which lends credence to our approach.…”

Section: A Predictive Models For Cpusmentioning

confidence: 59%

Hardware-Assisted Cross-Generation Prediction of GPUs Under Design

O’Neal

Brisk

Shriver

et al. 2019

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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This paper introduces a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the nextgeneration device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved impressively low out-of-sample-errors ranging from 7.45 % to 8.91 %, while running 29,481 to 44,214 times faster than cycle-accurate simulations. A detailed ranking of the most impactful features selected for these models provides an insight as to which microarchitectural subsystems have the greatest impact on performance from one generation to the next.

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Section: A Predictive Models For Cpusmentioning

confidence: 59%

Hardware-Assisted Cross-Generation Prediction of GPUs Under Design

O’Neal

Brisk

Shriver

et al. 2019

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…These models are generated from mechanics where parameters are derived by regressions, and thus benefit from both mechanistic modeling (i.e., interpretability) and empirical modeling (i.e., ease of implementation). Zheng et al [259,260] explore two approaches to cross-platform predictions of program execution time, where program profiling results on Intel Core i7 and AMD Phenom processors are used to estimate the execution time on a target ARM processor. The first one [260] relaxes the assumption of global linearity to local linearity in the feature space, to apply constrained locally sparse linear regression; the other one [259] applies lasso linear regression with phase-level performance features.…”

Section: Single-core Processormentioning

confidence: 99%

A Survey of Machine Learning for Computer Architecture and Systems

Wu,

Xie

2021

Preprint

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It has been a long time that computer architecture and systems are optimized to enable efficient execution of machine learning (ML) algorithms or models. Now, it is time to reconsider the relationship between ML and systems, and let ML transform the way that computer architecture and systems are designed. This embraces a twofold meaning: the improvement of designers' productivity, and the completion of the virtuous cycle. In this paper, we present a comprehensive review of work that applies ML for system design, which can be grouped into two major categories, ML-based modelling that involves predictions of performance metrics or some other criteria of interest, and ML-based design methodology that directly leverages ML as the design tool. For ML-based modelling, we discuss existing studies based on their target level of system, ranging from the circuit level to the architecture/system level. For ML-based design methodology, we follow a bottom-up path to review current work, with a scope of (micro-)architecture design (memory, branch prediction, NoC), coordination between architecture/system and workload (resource allocation and management, data center management, and security), compiler, and design automation. We further provide a future vision of opportunities and potential directions, and envision that applying ML for computer architecture and systems would thrive in the community. CCS Concepts: • Computing methodologies → Machine learning; • Computer systems organization → Architectures; • General and reference → Surveys and overviews.

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“…Machine learning techniques for performance and power modeling of single-core processors were discussed in [87]. More specifically, to predict the performance of a workload on a target platform, the study in [88] introduced a statistical learning approach. The model was verified for the ARM CPU model (five-stage in-order) and the authors reported an average accuracy of 90 percent.…”

Section: Additional Machine Learning Approachesmentioning

confidence: 99%