Budgeted region sampling (BeeRS): do not separate sampling from warm-up, and then spend wisely your simulation budget

Pérez, Daniel; Berry, Hugues; Temam, Olivier

doi:10.1109/isspit.2005.1577060

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Many studies have been done to evaluate how many instructions need to be simulated in order to accurately estimate performance. Phase-based sampling and statistical-based sampling are the two basic methods of reducing the number of instructions that need to be simulated while still gathering accurate performance statistics [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Thermal-aware sampling in architectural simulation

Ardestani

Ebrahimi

Southern

et al. 2012

Proceedings of the 2012 ACM/IEEE International Symposium on Low Power Electronics and Design

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Thermal behavior of modern processors is a first-order design constraint. However, accurate estimation of thermal behavior is time consuming, and techniques for accelerating performance simulations often yield inaccurate results when directly applied to thermal simulation, or do not reduce the thermal computation at all. This paper is the first to propose thermal sampling techniques. It can be integrated with existing phase-based and statistical-based architectural simulator sampling. The resulting simulator can perform accurate performance, power, and thermal characterization at close to 30 MIPS, on average, instead of 5 MIPS for the fastest sampling technique without thermal-aware sampling.

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Section: Introductionmentioning

confidence: 99%

Thermal-aware sampling in architectural simulation

Ardestani

Ebrahimi

Southern

et al. 2012

Proceedings of the 2012 ACM/IEEE International Symposium on Low Power Electronics and Design

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Transparent sampling

Khan

Pérez

Temam

2010

2010 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation

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A Sampling Method Focusing on Practicality

2006

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In the past few years, several research works have demonstrated that sampling can drastically speed up architecture simulation, and several of these sampling techniques are already largely used. However, for a sampling technique to be both easily and properly used, i.e., plugged and reliably used into many simulators with little or no effort or knowledge from the user, it must fulfill a number of conditions: it should require no hardware-dependent modification of the functional or timing simulator, it should simultaneously consider warm-up and sampling, while still delivering high speed and accuracy.The motivation for this article is that, with the advent of generic and modular simulation frameworks like ASIM, Sys-temC, LSE, MicroLib or UniSim, there is a need for sampling techniques with the aforementioned properties, i.e., which are almost entirely transparent to the user and simulator agnostic. In this article, we propose a sampling technique focused more on transparency than on speed and accuracy, though the technique delivers almost state-of-the-art performance. Our sampling technique is a hardware-independent and integrated approach to warm-up and sampling; it requires no modification of the functional simulator and solely relies on the performance simulator for warm-up. We make the following contributions:(1) a technique for splitting the execution trace into a potentially very large number of variable-size regions to capture program dynamic control flow, (2) a clustering method capable of efficiently coping with such a large number of regions, (3) a budget-based method for jointly considering warm-up and sampling costs, presenting them as a single parameter to the user, and for distributing the number of simulated instructions between warmup and sampling based on the region partitioning and clustering information.Overall, the method achieves an accuracy/time tradeoff that is close to the best reported results using clustering-based sampling (though usually with perfect or hardware-dependent warm-up), with an average CPI error of 1.68% and an average number of simulated instructions of 288 million instructions over the Spec benchmarks. The technique/tool can be readily applied to a wide range of benchmarks, architectures and simulators, and will be * This article is a modified version of the article originally published at IEEE Micro. In the IEEE Micro version, we compared our clustering technique against the technique used in SimPoint 2.0; in this version, we compare against SimPoint 3.0, where the speed of clustering was largely improved.used as a sampling option of the UniSim modular simulation framework.

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Budgeted region sampling (BeeRS): do not separate sampling from warm-up, and then spend wisely your simulation budget

Cited by 3 publications

References 15 publications

Thermal-aware sampling in architectural simulation

Thermal-aware sampling in architectural simulation

Transparent sampling

A Sampling Method Focusing on Practicality

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