Microarchitectural design space exploration made fast

Guo, Qi; Chen, Tianshi; Li, Ling; Hu, Weiwu

doi:10.1016/j.micpro.2012.07.006

Cited by 25 publications

(27 citation statements)

References 26 publications

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“…In this work, we model the memory access pattern in terms of reuse distance probability distribution for the memory references [19]. We focus on the cumulative distribution function (CDF).…”

Section: Predicting the Memory Access Patternmentioning

confidence: 99%

Scaling Properties of Parallel Applications to Exascale

Mariani

Anghel

Jongerius

et al. 2016

Int J Parallel Prog

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A detailed profile of exascale applications helps to understand the computation, communication and memory requirements for exascale systems and provides the insight necessary for fine-tuning the computing architecture. Obtaining such a profile is challenging as exascale systems will process unprecedented amounts of data. Profiling applications at the target scale would require the exascale machine itself. In this work we propose a methodology to extrapolate the exascale profile from experimental observations over datasets feasible for today's machines. Extrapolation models are carefully selected by means of statistical techniques and a high-level complexity analysis is included in the selection process to speed up the learning phase and to improve the accuracy of the final model. We extrapolate run-time properties of the target applications including information about the instruction mix, memory access pattern, instruction-level parallelism, and communication requirements. Compared to state-of-the-art techniques, the proposed methodology reduces the prediction error by an order of magnitude on the instruction count and improves the accuracy by up to 1.3× for the memory access pattern, and by more than 2× for the communication requirements.

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Section: Predicting the Memory Access Patternmentioning

confidence: 99%

Scaling Properties of Parallel Applications to Exascale

Mariani

Anghel

Jongerius

et al. 2016

Int J Parallel Prog

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“…To address this problem, recently some works proposed to build cross-program predictors [9,10,11,12]. Khan et al [9] proposed a reaction-based cross-program predictive model by employing ANN as the base predictor.…”

Section: Predictive Modelmentioning

confidence: 99%

Efficient design space exploration by knowledge transfer

Wang

Yao

et al. 2016

Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

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Due to the exponentially increasing size of design space of microprocessors and time-consuming simulations, predictive models have been widely employed in design space exploration (DSE). Traditional approaches mostly build a program-specific predictor that needs a large number of program-specific samples. Thus considerable simulation cost is required for each program.In this paper, we study the novel problem of transferring knowledge from the labeled samples of previous programs to help predict the responses of the new target program whose labeled samples are very sparse. Inspired by the recent advances of transfer learning, we propose a transfer learning based DSE framework TrDSE to build a more efficient and effective predictive model for the target program with only a few simulations by borrowing knowledge from previous programs. Specifically, TrDSE includes two phases: 1) clustering the programs based on the proposed orthogonal array sampling and the distribution related features, and 2) with the guidance of clustering results, predicting the responses of configurations in design space of the target program by a transfer learning based regression algorithm. We evaluate the proposed TrDSE on the benchmarks of SPEC CPU 2006 suite. The results demonstrate that the proposed framework is more efficient and effective than state-of-art DSE techniques.

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“…These models are analytic approximations of system performance that are learned after an initial training phase [36] and used to determine where to focus the costly evaluations [5], [6], [37]. Widely used methods are linear regressions [38], radial basis functions [5], [39], neural networks [40], regression trees [28], [41], and statistical techniques such as Gaussian processes [42] and Kriging interpolation [11], [43].…”

Section: Related Workmentioning

confidence: 99%

“…So far, several works proposed to speedup the DSE process by pruning the number of simulations to be executed by means of an approximate system model [6], [11], [28], [37], [41]. This approach is promising when considering a single simulation node in the environment hosting the DSE process.…”

Section: Motivating Examplementioning

confidence: 99%

DeSpErate++: An Enhanced Design Space Exploration Framework Using Predictive Simulation Scheduling

Mariani

Palermo

Zaccaria

et al. 2015

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

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Exploring the design space of computer architectures generally consists of a trial-and-error procedure where several architectural configurations are evaluated by using simulation techniques. The final goal of the multiobjective design space exploration (DSE) process is the identification of architectural configurations optimal for a set of target objective functions, typically power consumption, and performance. Simulations are computationally expensive making it rather hard to efficiently explore the design space to identify high-quality configurations in an acceptable exploration time when relying solely on a single-core machine to run simulations. To tackle this problem, engineers proposed solutions based on either: 1) the use of approximate analytic performance models to prune the suboptimal regions of the design space by reducing the number of simulations to run or 2) the use of parallel computing resources to run different simulations concurrently. In this paper we demonstrate that, to efficiently speedup the DSE process while fully exploiting the parallel computing infrastructure, we need to combine the two techniques together in a structured manner. In this paper, we investigate this issue and we propose a DSE solution that exploits approximate analytic prediction models to improve the simulation schedule on a parallel computing environment rather than to prune the number of simulations. Experimental results demonstrate that the proposed technique provides a speedup from 1.26× to 4× with respect to other parallel state-of-the art DSE techniques.

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Microarchitectural design space exploration made fast

Cited by 25 publications

References 26 publications

Scaling Properties of Parallel Applications to Exascale

Scaling Properties of Parallel Applications to Exascale

Efficient design space exploration by knowledge transfer

DeSpErate++: An Enhanced Design Space Exploration Framework Using Predictive Simulation Scheduling

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