ACM Transactions on Modeling and Computer Simulation最新文献_第4页

Divergence Reduction in Monte Carlo Neutron Transport with On-GPU Asynchronous Scheduling 基于gpu异步调度的蒙特卡罗中子传输散度减小

4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-10-19 DOI: 10.1145/3626957

Braxton Cuneo, Mike Bailey

{"title":"Divergence Reduction in Monte Carlo Neutron Transport with On-GPU Asynchronous Scheduling","authors":"Braxton Cuneo, Mike Bailey","doi":"10.1145/3626957","DOIUrl":"https://doi.org/10.1145/3626957","url":null,"abstract":"While Monte Carlo Neutron Transport (MCNT) is near-embarrasingly parallel, the effectively unpredictable lifetime of neutrons can lead to divergence when MCNT is evaluated on GPUs. Divergence is the phenomenon of adjacent threads in a warp executing different control flow paths; on GPUS, it reduces performance because each work group may only execute one path at a time. The process of Thread Data Remapping (TDR) resolves these discrepancies by moving data across hardware such that data in the same warp will be processed through similar paths. A common issue among prior implementations of TDR is the synchronous nature of its remapping and processing cycles, which exhaustively sort data produced by prior processing passes and exhaustively evaluate the sorted data. In another paper, we defined a method of remapping data through an asynchronous scheduler which allows for work to be stored in shared memory and deferred arbitrarily until that work is a viable option for low-divergence evaluation. This paper surveys a wider set of cases, with the goal of characterizing performance trends across a more comprehensive set of parameters. These parameters include cross sections of scattering/capturing/fission, use of implicit capture, source neutron counts, simulation time spans, and tuned memory allocations. Across these cases, we have recorded minimum and average execution times, as well as a heuristically-tuned near-optimal memory allocation size for both synchronous and asynchronous scheduling. Across the collected data, it is shown that the asynchronous method is faster and more memory efficient in the majority of cases, and that it requires less tuning to achieve competitive performance.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135728822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Cache or Credit for Parallel Ranking and Selection 使用缓存或信用进行并行排序和选择

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-09-04 DOI: 10.1145/3618299

Harun Avci, Barry L. Nelson, Eunhye Song, Andreas Wächter

{"title":"Using Cache or Credit for Parallel Ranking and Selection","authors":"Harun Avci, Barry L. Nelson, Eunhye Song, Andreas Wächter","doi":"10.1145/3618299","DOIUrl":"https://doi.org/10.1145/3618299","url":null,"abstract":"In this paper, we focus on ranking and selection procedures that sequentially allocate replications to systems by applying some acquisition function. We propose an acquisition function, called gCEI, which exploits the gradient of the complete expected improvement with respect to the number of replications. We prove that the gCEI procedure, which adopts gCEI as the acquisition function in a serial computing environment, achieves the asymptotically optimal static replication allocation of Glynn and Juneja in the limit under a normality assumption. We also propose two procedures, called caching and credit, that extend any acquisition-function-based procedure in a serial environment into both synchronous and asynchronous parallel environments. While allocating replications to systems, both procedures use persistence forecasts for the unavailable outputs of the currently running replications, but differ in usage of the available outputs. We prove that under certain assumptions, the caching procedure achieves the same asymptotic allocation as in the serial environment. A similar result holds for the credit procedure using gCEI as the acquisition function. In terms of efficiency and effectiveness, the credit procedure empirically performs as well as the caching procedure despite not carefully controlling the output history as the caching procedure does, and is faster than the serial version without any number-of-replications penalty due to using persistence forecasts. Both procedures are designed to solve small-to-medium-sized problems on computers with a modest number of processors, such as laptops and desktops as opposed to high-performance clusters, and are superior to state-of-the-art parallel procedures in this setting.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44825472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data 流输入数据多周期仿真优化的随机逼近

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-08-29 DOI: 10.1145/3617595

Linyun He, U. Shanbhag, Eunhye Song

{"title":"Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data","authors":"Linyun He, U. Shanbhag, Eunhye Song","doi":"10.1145/3617595","DOIUrl":"https://doi.org/10.1145/3617595","url":null,"abstract":"We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA (ReSA) reinitializes the stepsize sequence in each period, whereas Warm-start SA (WaSA) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions, ReSA and WaSA achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while WaSA accrues significantly lower computational cost as the number of periods increases. In addition, we present the regularized ReSA which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45117582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version DSMC评估阶段:在深度强化学习中培养稳健和安全的行为-扩展版

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-07-12 DOI: https://dl.acm.org/doi/10.1145/3607198

Timo P. Gros, Joschka Groß, Daniel Höller, Jörg Hoffmann, Michaela Klauck, Hendrik Meerkamp, Nicola J. Müller, Lukas Schaller, Verena Wolf

{"title":"DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version","authors":"Timo P. Gros, Joschka Groß, Daniel Höller, Jörg Hoffmann, Michaela Klauck, Hendrik Meerkamp, Nicola J. Müller, Lukas Schaller, Verena Wolf","doi":"https://dl.acm.org/doi/10.1145/3607198","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3607198","url":null,"abstract":"Neural networks (NN) are gaining importance in sequential decision-making. Deep reinforcement learning (DRL), in particular, is extremely successful in learning action policies in complex and dynamic environments. Despite this success, however, DRL technology is not without its failures, especially in safety-critical applications: (i) the training objective maximizes average rewards, which may disregard rare but critical situations and hence lack local robustness; (ii) optimization objectives targeting safety typically yield degenerated reward structures which for DRL to work must be replaced with proxy objectives. Here we introduce a methodology that can help to address both deficiencies. We incorporate evaluation stages (ES) into DRL, leveraging recent work on deep statistical model checking (DSMC), which verifies NN policies in Markov decision processes. Our ES apply DSMC at regular intervals to determine state space regions with weak performance. We adapt the subsequent DRL training priorities based on the outcome, (i) focusing DRL on critical situations, and (ii) allowing to foster arbitrary objectives. We run case studies on two benchmarks. One of them is the Racetrack, an abstraction of autonomous driving that requires navigating a map without crashing into a wall. The other is MiniGrid, a widely used benchmark in the AI community. Our results show that DSMC-based ES can significantly improve both (i) and (ii).","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":"13 10","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version DSMC评估阶段:在深度强化学习中培养稳健和安全的行为-扩展版

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-07-12 DOI: 10.1145/3607198

Timo P. Gros, D. Höller, Jörg Hoffmann, M. Klauck, Hendrik Meerkamp, Verena Wolf

{"title":"DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version","authors":"Timo P. Gros, D. Höller, Jörg Hoffmann, M. Klauck, Hendrik Meerkamp, Verena Wolf","doi":"10.1145/3607198","DOIUrl":"https://doi.org/10.1145/3607198","url":null,"abstract":"Neural networks (NN) are gaining importance in sequential decision-making. Deep reinforcement learning (DRL), in particular, is extremely successful in learning action policies in complex and dynamic environments. Despite this success, however, DRL technology is not without its failures, especially in safety-critical applications: (i) the training objective maximizes average rewards, which may disregard rare but critical situations and hence lack local robustness; (ii) optimization objectives targeting safety typically yield degenerated reward structures which for DRL to work must be replaced with proxy objectives. Here we introduce a methodology that can help to address both deficiencies. We incorporate evaluation stages (ES) into DRL, leveraging recent work on deep statistical model checking (DSMC), which verifies NN policies in Markov decision processes. Our ES apply DSMC at regular intervals to determine state space regions with weak performance. We adapt the subsequent DRL training priorities based on the outcome, (i) focusing DRL on critical situations, and (ii) allowing to foster arbitrary objectives. We run case studies on two benchmarks. One of them is the Racetrack, an abstraction of autonomous driving that requires navigating a map without crashing into a wall. The other is MiniGrid, a widely used benchmark in the AI community. Our results show that DSMC-based ES can significantly improve both (i) and (ii).","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43824767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Optimizing reachability probabilities for a restricted class of Stochastic Hybrid Automata via Flowpipe-Construction 基于流动管道构造的受限类随机混合自动机可达概率优化

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-07-11 DOI: https://dl.acm.org/doi/10.1145/3607197

Carina da Silva, Stefan Schupp, Anne Remke

{"title":"Optimizing reachability probabilities for a restricted class of Stochastic Hybrid Automata via Flowpipe-Construction","authors":"Carina da Silva, Stefan Schupp, Anne Remke","doi":"https://dl.acm.org/doi/10.1145/3607197","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3607197","url":null,"abstract":"Stochastic hybrid automata (SHA) are a powerful tool to evaluate the dependability and safety of critical infrastructures. However, the resolution of nondeterminism, which is present in many purely hybrid models, is often only implicitly considered in SHA. This paper instead proposes algorithms for computing maximum and minimum reachability probabilities for singular automata with urgent transitions and random clocks which follow arbitrary continuous probability distributions. We borrow a well-known approach from hybrid systems reachability analysis, namely flowpipe construction, which is then extended to optimize nondeterminism in the presence of random variables. Firstly, valuations of random clocks which ensure reachability of specific goal states are extracted from the computed flowpipes and secondly, reachability probabilities are computed by integrating over these valuations. We compute maximum and minimum probabilities for history-dependent prophetic and non-prophetic schedulers using set-based methods. The implementation featuring the library HyPro and the complexity of the approach are discussed in detail. Two case studies featuring nondeterministic choices show the feasibility of the approach.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":"28 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing reachability probabilities for a restricted class of Stochastic Hybrid Automata via Flowpipe-Construction 用Flowpipe构造优化一类受限随机混合自动机的可达性概率

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-07-11 DOI: 10.1145/3607197

Carina Pilch, Stefan Schupp, Anne Remke

{"title":"Optimizing reachability probabilities for a restricted class of Stochastic Hybrid Automata via Flowpipe-Construction","authors":"Carina Pilch, Stefan Schupp, Anne Remke","doi":"10.1145/3607197","DOIUrl":"https://doi.org/10.1145/3607197","url":null,"abstract":"Stochastic hybrid automata (SHA) are a powerful tool to evaluate the dependability and safety of critical infrastructures. However, the resolution of nondeterminism, which is present in many purely hybrid models, is often only implicitly considered in SHA. This paper instead proposes algorithms for computing maximum and minimum reachability probabilities for singular automata with urgent transitions and random clocks which follow arbitrary continuous probability distributions. We borrow a well-known approach from hybrid systems reachability analysis, namely flowpipe construction, which is then extended to optimize nondeterminism in the presence of random variables. Firstly, valuations of random clocks which ensure reachability of specific goal states are extracted from the computed flowpipes and secondly, reachability probabilities are computed by integrating over these valuations. We compute maximum and minimum probabilities for history-dependent prophetic and non-prophetic schedulers using set-based methods. The implementation featuring the library HyPro and the complexity of the approach are discussed in detail. Two case studies featuring nondeterministic choices show the feasibility of the approach.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46721864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction 基于知识的数据中心数字孪生模型校正与约简

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-06-10 DOI: https://dl.acm.org/doi/10.1145/3604283

Ruihang Wang, Deneng Xia, Zhiwei Cao, Yonggang Wen, Rui Tan, Xin Zhou

{"title":"Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction","authors":"Ruihang Wang, Deneng Xia, Zhiwei Cao, Yonggang Wen, Rui Tan, Xin Zhou","doi":"https://dl.acm.org/doi/10.1145/3604283","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3604283","url":null,"abstract":"Computational fluid dynamics (CFD) models have been widely used for prototyping data centers. Evolving them into high-fidelity and real-time digital twins is desirable for online operations of data centers. However, CFD models often have unsatisfactory accuracy and high computation overhead. Manually calibrating the CFD model parameters is tedious and labor-intensive. Existing automatic calibration approaches apply heuristics to search the model configurations. However, each search step requires a long-lasting process of repeatedly solving the CFD model, rendering them impractical especially for complex CFD models. This paper presents Kalibre, a knowledge-based neural surrogate approach that calibrates a CFD model by iterating four steps of i) training a neural surrogate model, ii) finding the optimal parameters through neural surrogate retraining, iii) configuring the found parameters back to the CFD model, and iv) validating the CFD model using sensor-measured data. Thus, the parameter search is offloaded to the lightweight neural surrogate. To speed up Kalibre’s convergence, we incorporate prior knowledge in training data initialization and surrogate architecture design. With about ten hours computation on a 64-core processor, Kalibre achieves mean absolute errors (MAEs) of 0.57°C and 0.88°C in calibrating the CFD models of two production data halls hosting thousands of servers. To accelerate CFD-based simulation, we further propose Kalibreduce that incorporates the energy balance principle to reduce the order of the calibrated CFD model. Evaluation shows the model reduction only introduces 0.1°C to 0.27°C extra errors, while accelerating the CFD-based simulations by thousand times.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":"77 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction 基于知识的数据中心数字孪生模型校正与约简

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-06-10 DOI: 10.1145/3604283

Ruihang Wang, Deneng Xia, Zhi-Ying Cao, Yonggang Wen, Rui Tan, Xiaoxia Zhou

{"title":"Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction","authors":"Ruihang Wang, Deneng Xia, Zhi-Ying Cao, Yonggang Wen, Rui Tan, Xiaoxia Zhou","doi":"10.1145/3604283","DOIUrl":"https://doi.org/10.1145/3604283","url":null,"abstract":"Computational fluid dynamics (CFD) models have been widely used for prototyping data centers. Evolving them into high-fidelity and real-time digital twins is desirable for online operations of data centers. However, CFD models often have unsatisfactory accuracy and high computation overhead. Manually calibrating the CFD model parameters is tedious and labor-intensive. Existing automatic calibration approaches apply heuristics to search the model configurations. However, each search step requires a long-lasting process of repeatedly solving the CFD model, rendering them impractical especially for complex CFD models. This paper presents Kalibre, a knowledge-based neural surrogate approach that calibrates a CFD model by iterating four steps of i) training a neural surrogate model, ii) finding the optimal parameters through neural surrogate retraining, iii) configuring the found parameters back to the CFD model, and iv) validating the CFD model using sensor-measured data. Thus, the parameter search is offloaded to the lightweight neural surrogate. To speed up Kalibre’s convergence, we incorporate prior knowledge in training data initialization and surrogate architecture design. With about ten hours computation on a 64-core processor, Kalibre achieves mean absolute errors (MAEs) of 0.57°C and 0.88°C in calibrating the CFD models of two production data halls hosting thousands of servers. To accelerate CFD-based simulation, we further propose Kalibreduce that incorporates the energy balance principle to reduce the order of the calibrated CFD model. Evaluation shows the model reduction only introduces 0.1°C to 0.27°C extra errors, while accelerating the CFD-based simulations by thousand times.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42854944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty-aware Simulation of Adaptive Systems 自适应系统的不确定性感知仿真

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-05-13 DOI: https://dl.acm.org/doi/10.1145/3589517

Jean-Marc Jézéquel, Antonio Vallecillo

引用次数: 0