Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation最新文献

{"title":"Session details: Session on Parallel Simulation 2","authors":"G. Riley","doi":"10.1145/3252260","DOIUrl":"https://doi.org/10.1145/3252260","url":null,"abstract":"","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116009216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Session details: Session on Data and Knowledge Discovery for Simulation","authors":"S. Rajendran","doi":"10.1145/3252255","DOIUrl":"https://doi.org/10.1145/3252255","url":null,"abstract":"","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121705888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Generation of Reversible C++ Code and Its Performance in a Scalable Kinetic Monte-Carlo Application","authors":"M. Schordan, T. Oppelstrup, D. Jefferson, P. Barnes, D. Quinlan","doi":"10.1145/2901378.2901394","DOIUrl":"https://doi.org/10.1145/2901378.2901394","url":null,"abstract":"The fully automatic generation of code that establishes the reversibility of arbitrary C/C++ code has been a target of research and engineering for more than a decade as reverse computation has become a central notion in large scale parallel discrete event simulation (PDES). The simulation models that are implemented for PDES are of increasing complexity and size and require various language features to support abstraction, encapsulation, and composition when building a simulation model. In this paper we focus on parallel simulation models that are written in C++ and present an approach and an evaluation for a fully automatically generated reversible code for a kinetic Monte-Carlo application implemented in C++. Although a significant runtime overhead is introduced with our technique, the assurance that the reverse code is generated automatically and correctly, is an enormous win that allows simulation model developers to write forward event code using the entire C++ language, and have that code automatically transformed into reversible code to enable parallel execution with the Rensselaer's Optimistic Simulation System (ROSS).","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125358589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Session details: Session on Parallel Simulation 1","authors":"D. Nicol","doi":"10.1145/3252258","DOIUrl":"https://doi.org/10.1145/3252258","url":null,"abstract":"","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116621503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Discovery for Pareto Based Multiobjective Optimization in Simulation","authors":"Patrick Lange, René Weller, G. Zachmann","doi":"10.1145/2901378.2901380","DOIUrl":"https://doi.org/10.1145/2901378.2901380","url":null,"abstract":"We present a novel knowledge discovery approach for automatic feasible design space approximation and parameter optimization in arbitrary multiobjective blackbox simulations. Our approach does not need any supervision of simulation experts. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually reducing the complexity and number of simulation runs by varying input parameters through educated assumptions and according to prior defined goals. This leads to a error-prone trial-and-error approach for determining suitable parameters for successful simulations.In contrast, our approach autonomously discovers unknown relationships in model behavior and approximates the feasible design space. Furthermore, we show how Pareto gradient information can be obtained from this design space approximation for state-of-the-art optimization algorithms. Our approach gains its efficiency from a novel spline-based sampling of the parameter space in combination within novel forest-based simulation dataflow analysis. We have applied our new method to several artificial and real-world scenarios and the results show that our approach is able to discover relationships between parameters and simulation goals. Additionally, the computed multiobjective solutions are close to the Pareto front.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116004336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Scalable Whole-Cell Modeling of Human Cells","authors":"A. P. Goldberg, Yin Hoon Chew, Jonathan R. Karr","doi":"10.1145/2901378.2901402","DOIUrl":"https://doi.org/10.1145/2901378.2901402","url":null,"abstract":"Whole-cell (WC) models comprehensively predict cellular phenotypes by simulating the biochemistry in individual cells. WC models have the potential to enable bioengineers and physicians to rationally design microorganisms and medical therapies. WC models are developed by combining multiple mathematically distinct pathway sub-models into a single multi-algorithm model. The only existing WC model represents a small bacterium. However, to enable medical therapy, new scalable methods are needed to model human cells that contain 100 times more molecular species and 10,000-100,000 times more molecules. We describe the design of a novel system for building and simulating WC models, including an expressive sequence- and rule-based modeling language and a multi-algorithm simulator that employs optimistic parallel discrete event simulation.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129137056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Case Study in Using Discrete-Event Simulation to Improve the Scalability of MG-RAST","authors":"Caitlin J. Ross, M. Mubarak, John Jenkins, P. Carns, C. Carothers, R. Ross, Wei Tang, Wolfgang Gerlach, Folker Meyer","doi":"10.1145/2901378.2901387","DOIUrl":"https://doi.org/10.1145/2901378.2901387","url":null,"abstract":"As the cost of DNA sequencing has decreased, computational biology data processing platforms are experiencing an increasingly large volume of data analysis requests. The metagenomics analysis server MG-RAST at Argonne National Laboratory, a computational biology data processing platform, is receiving several terabytes of data submissions per month. However, MG-RAST currently relies on a central object-based data store, Shock, for data access and storage that can become a bottleneck under high data transfer loads, adversely affecting the job response time for end users. In this work, we use a discrete-event simulation approach to explore the use of data proxies and an enhanced, proxy-aware scheduling methodology designed to reduce the movement of the intermediate data generated during workflow processing. In this approach, Shock is supplemented with proxy storage servers, employing solid state drives, to decentralize the management and hence reduce the movement of intermediate workflow results. Discrete-event simulation provides a way to evaluate the performance of MG-RAST with increased workloads without disrupting the production system. For our case study, we extrapolate scientific workflows obtained from MG-RAST to represent future usage trends. We demonstrate that the addition of proxies and the proxy-aware scheduling methodology significantly reduces the data movement overhead by distributing the data plane, leading to substantial improvement in end-user job response time.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"38 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120949046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DSSnet: A Smart Grid Modeling Platform Combining Electrical Power Distribution System Simulation and Software Defined Networking Emulation","authors":"Christopher Hannon, Jiaqi Yan, Dong Jin","doi":"10.1145/2901378.2901383","DOIUrl":"https://doi.org/10.1145/2901378.2901383","url":null,"abstract":"The successful operations of modern power grids are highly dependent on a reliable and efficient underlying communication network. Researchers and utilities have started to explore the opportunities and challenges of applying the emerging software-defined networking (SDN) technology to enhance efficiency and resilience of the Smart Grid. This trend calls for a simulation-based platform that provides sufficient flexibility and controllability for evaluating network application designs, and facilitating the transitions from in-house research ideas to real productions. In this paper, we present DSSnet, a hybrid testing platform that combines a power distribution system simulator with an SDN emulator to support high fidelity analysis of communication network applications and their impacts on the power systems. Our contributions lay in the design of a virtual time system with the tight controllability on the execution of the emulation system, i.e., pausing and resuming any specified container processes in the perception of their own virtual clocks, with little overhead scaling to 500 emulated hosts with an average of 70 ms overhead; and also lay in the efficient synchronization of the two sub-systems based on the virtual time. We evaluate the system performance of DSSnet, and also demonstrate the usability through a case study by evaluating a load shifting algorithm.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123749637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"InfoSymbioticSystems/DDDAS and Large-Scale Dynamic Data and Large-Scale Big Computing for Smart Systems","authors":"F. Darema","doi":"10.1145/2901378.2901405","DOIUrl":"https://doi.org/10.1145/2901378.2901405","url":null,"abstract":"The presentation will discuss InfoSymbiotics/DDDAS, a paradigm which unifies systems? modeling and instrumentation aspects, and is creating new and revolutionary capabilities for improved understanding, analysis, and optimized, autonomic management and decision support of operational of engineered and natural multi-entity systems, and including human and societal systems. Key underlying concept in DDDAS is the dynamic integration of instrumentation data and executing models of the system in a feedback control loop - that is on-line data are dynamically incorporated into the systems' executing model, to improve the modeling accuracy or to speed-up the simulation, and in reverse the executing model controls the instrumentation to selectively and adaptively target the data collection process, and dynamically manage collective sets of sensors and controllers. DDDAS is timely and in-line with the advent of Large-Scale-Dynamic-Data and Large-Scale-Big-Computing. Large-Scale-Dynamic-Data encompasses the traditional Big Data with next wave of Big Data, and namely dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems, through multitudes of heterogeneous sensors and controllers instrumenting these systems, and where the opportunities and challenges at these \"large-scales\" relate not only to the size of the data but the heterogeneity in data, data collection modalities, data fidelities, and timescales, ranging from real-time data to archival data. DDDAS entails the dynamic integration of the traditional high-end/mid-range parallel and distributed computing with the real-time data-acquisition and control. Thus, in tandem with the important new dimension of dynamic data, DDDAS implies an extended view of Big Computing, which includes a new dimension of computing - the collective computing by networked assemblies of multitudes of sensors and controllers. The DDDAS paradigm, driving and exploiting these notions of Large-Scale Dynamic Data and Large-Scale Big Computing, is shaping research directions and engendering transformative impact in a range of natural and engineered systems application areas. Spanning application areas from the nanoscale to the terra-scale and the extra-terra-scale environments, examples of advances and new capabilities that will be presented include: materials analysis and decision support for structural systems; manufacturing systems; cellular, neural, and biorobotic systems; environmental systems; critical infrastructure systems, such as urban and air transportation, energy powergrids, and smart agriculture. In addition the challenges, opportunities, and advances that have been made in the systems software for these Large-Scale-Big-Computing and Large-Scale-Big-Data environments will also be addressed in the talk.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"231 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113996671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}