Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery最新文献

{"title":"Ab initio elasticity workflow in the VLab science gateway","authors":"Pedro R. C. da Silveira, Lahiru Gunathilake, A. Holiday, Dave A. Yuen, M. Valdez, R. Wentzcovitch","doi":"10.1145/2484762.2484823","DOIUrl":"https://doi.org/10.1145/2484762.2484823","url":null,"abstract":"This paper describes a scientific workflow for ab initio calculations of elastic coefficients (Cij) of crystalline materials implemented in the VLab Cyberinfrastructure [da Silveira et al., 2008]. This workflow has recently been upgraded to treat crystals of all symmetries and integrated in the XSEDE. First we review the underlying Cij calculations and list explicitly different requirements for each Bravais lattice. We also describe the workflow management and its general method for handling actions. We illustrate the Cij application with a calculation of diamond's elastic coefficients at high pressures. We conclude with an outlook of future implementation plans.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131552876","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":"Large-scale land use optimization by enhancing a scalable parallel genetic algorithm library","authors":"Yan Y. Liu, M. Guo, Shaowen Wang","doi":"10.1145/2484762.2484824","DOIUrl":"https://doi.org/10.1145/2484762.2484824","url":null,"abstract":"Optimization algorithms are often employed in spatial analysis and modeling to provide adaptive mechanisms at both individual and collective levels to enable decision-makers for the search of optimal solutions with respect to single/multiple objectives and constraints imposed by spatial configurations. This research aims to solve large-scale agricultural land use optimization problems by exploiting massive parallel computing resources provided by supercomputers such as those in XSEDE. The optimization of agricultural land use patterns finds an optimal assignment of crops (e.g., food and biofuel crops) on land parcels of a specified study area that maximizes the total yield and satisfies various competing constraints. These constraints often consider spatial factors such as contiguity and ownership, climate and land management factors (e.g., soil, precipitation, light, temperature, and ozone) and their effects on the productivity, suitability, and cost of assigning a crop on a land parcel. We have formulated the land use optimization problem as a classic combinatorial optimization problem - Generalized Assignment Problem (GAP) [2]. GAP is a well-known NP-hard problem [3]. When a landscape includes tens of thousands of land parcels (e.g., Figure 1), finding an exact optimal solution is computationally intractable. In our research, we develop a parallel heuristic algorithm by combining an attention to the idiosyncrasies of agricultural land use optimization problem with a scalable parallel genetic algorithm (PGA) [4] to produce near-optimal solutions through scalable and efficient PGA computation on a large number of processors. Our PGA parallelizes the GA computation by running a large number of PGA processes simultaneously, each process conducting independent GA computation with a migration strategy that exchanges solutions between any two directly connected PGA processes at regular intervals. On each PGA process, a set of solutions form a local population. Standard GA operators such as population initialization, selection, crossover, mutation, and replacement are tailored to facilitate the search for better land use patterns based on aforementioned spatial and social economic factors. The parallelism in PGA is straightforward and easily permits a large number of PGA processes to evolve independently by following different randomized search paths and exploring the solution space collectively through migration strategies [1]. Nonetheless, a significant challenge remains regarding how to devise PGAs that are able to scale to massively parallel computer architectures. Issues persist because 1) a common PGA design adopts synchronized migration, which becomes increasingly costly as a larger number of processors are involved in global synchronization in each iteration; and 2) asynchronous PGA design and associated performance evaluation are intricate since the stochastic nature of PGA results in computations that are not simply dependent on the problem ","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128404732","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":"Biomedical CyberInfrastructure challenges","authors":"Claudiu Farcas, N. Balac, L. Ohno-Machado","doi":"10.1145/2484762.2484767","DOIUrl":"https://doi.org/10.1145/2484762.2484767","url":null,"abstract":"Biomedical research traverses a new era of advancements through the adoption of massive computing and big-data solutions to major scientific problems. However, the road ahead is far from \"a walk in a park\" -- many obstacles exist in the standardization, adoption, and evolution of methods, practices, algorithms, tools, and ultimately knowledge, that would mature along this road. In this article, we discuss such challenges that we encountered in this field and possible solutions from the iDASH program that closely engages this community.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131080867","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":"INSTANCES: incorporating computational scientific thinking advances into education & science courses","authors":"R. Landau, Greg Mulder, Raquell Holmes, Sofya Borinskaya, Nam-Hwa Kang, C. Bordeianu","doi":"10.1145/2484762.2484769","DOIUrl":"https://doi.org/10.1145/2484762.2484769","url":null,"abstract":"The INSTANCES project strives to create science educational materials that incorporate computation as an essential element [1]. Figure 1 illustrates how the authors incorporate this modern approach of scientific problem solving. Although a decade ago the combination of computing, science and applied mathematics known as computational science was rarely known beyond a few research universities, today K-12 organizations such as the Computer Science Teachers Association [2] and the National Science Teachers Association [3] recommend that secondary school classrooms teach simulation as a cornerstone of scientific inquiry.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132559417","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":"Getting started with high performance computing for humanities, arts, and social science","authors":"Alan B. Craig","doi":"10.1145/2484762.2484788","DOIUrl":"https://doi.org/10.1145/2484762.2484788","url":null,"abstract":"This abstract and presentation addresses the question of \"Why would someone in humanities, arts, or social science be interested in high performance computing?\", and discusses the resources and assistance that are available to humanists, artists, and social scientists who are interested in high performance computing. The Extreme Science And Engineering Discovery Environment (XSEDE) provides a network of high performance computing resources that are available to researchers. In this talk I will discuss the resources that are available, who is eligible for these resources, and assistance that is available to help you use those resources. My role within XSEDE is to help you get started on XSEDE as well as to help you after you get resources allocated. In this talk I will walk you through the process of applying for an XSEDE startup account and let you know what to expect as you begin using the resources. I will also discuss some of the different types of projects that have been done by humanities, arts, and social science researchers which range from large scale analysis of texts, images and videos, network analysis (including social media), map based problems, simulations, and others. Finally, I will address some of the lessons I have learned from working with humanities, arts, and social science researchers who are using XSEDE resources. Whether you need computational power, storage, assistance with analysis of large datasets, or are just curious of what these types of resources can do for you, this talk will provide answers that you are looking for.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125914225","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":"Large memory high performance computing enables comparison across human gut microbiome of patients with autoimmune diseases and healthy subjects","authors":"Sitao Wu, Weizhong Li, L. Smarr, K. Nelson, Shibu Yooseph, M. Torralba","doi":"10.1145/2484762.2484828","DOIUrl":"https://doi.org/10.1145/2484762.2484828","url":null,"abstract":"Microbial communities that live on the outside and inside of the human body dramatically influence human health and diseases. In recent years, major progress has been made in understanding the human microbiome communities through projects such as the Human Microbiome Project (http://commonfund.nih.gov/hmp/), using next generation sequencing technologies and metagenomic approaches. In this paper, we describe a comparative computational analysis of 183 human gut microbiome sequence datasets, drawn from healthy individuals as well as those with autoimmune diseases. About 2.4 TB of Illumina deep sequencing metagenomic data were analyzed using computational workflows we developed, which run multiple steps of data- and computing-intensive analyses such as mapping, sequence assembly, gene identification, clustering and functional annotations. The analyses were carried out on the Gordon supercomputer at the San Diego Supercomputer Center (SDSC), using ~180,000 core hours and tens of TB storage space. Our analysis reveals the detailed microbial composition, dynamics, and functional profiles of the samples and provides new insight into how to correlate microbial profiles with human health and disease states.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130310516","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":"Scalasca support for MPI+OpenMP parallel applications on large-scale HPC systems based on Intel Xeon Phi","authors":"B. Wylie, W. Frings","doi":"10.1145/2484762.2484777","DOIUrl":"https://doi.org/10.1145/2484762.2484777","url":null,"abstract":"Intel Xeon Phi coprocessors based on the Many Integrated Core (MIC) architecture are starting to appear in HPC systems, with Stampede being a prominent example available within the XSEDE cyber-infrastructure. Porting MPI and OpenMP applications to such systems is often no more than simple recompilation, however, execution performance needs to be carefully analyzed and tuned to effectively exploit their unique capabilities. For performance measurement and analysis tools, the variety of execution modes need to be supported in a consistent and convenient manner, and especially execution configurations involving large numbers of compute nodes each with several multicore host processors and many-core coprocessors. Early experience using the open-source Scalasca toolset for runtime summarization and automatic trace analysis with the NPB BT-MZ MPI+OpenMP parallel application on Stampede is reported, along with discussion of on-going and future work.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"31 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130439419","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":"In-situ visualization for global hybrid simulations","authors":"H. Karimabadi, B. Loring, P. O’leary, A. Majumdar, M. Tatineni, Berk Geveci","doi":"10.1145/2484762.2484822","DOIUrl":"https://doi.org/10.1145/2484762.2484822","url":null,"abstract":"Petascale simulations have become mission critical in diverse areas of science and engineering. Knowledge discovery from such simulations remains a major challenge and is becoming more urgent as the march towards ultra-scale computing with millions of cores continues. One major issue with the current paradigm of running the simulations and saving the data to disk for post-processing is that it is only feasible to save the data at a small number of time slices. This low temporal resolution of the saved data is a serious handicap in many studies where the time evolution of the system is of principle interest. One way to address this I/O issue is through in-situ visualization strategies. The idea is to minimize data storage by extracting important features of the data and saving them, rather than raw data, at high temporal resolution. Parallel file systems of current petascale and future exascale systems are expensive shared resources and need to be utilized effectively, and similarly archival storage can be limited and both of these will benefit from in-situ visualization as it will lead to intelligent way of utilizing storage. In this paper, we present preliminary results from our in-situ visualization for global hybrid (electron fluid, kinetic ions) simulations which are used to study the interaction of the solar wind with planetary magnetospheres such as the Earth and Mercury. In particular, we examine the overhead and effect on code performance associated with the inline computations associated with in-situ visualization.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117153109","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":"The Oklahoma cyberinfrastructure initiative","authors":"Henry Neeman, Zane Gray, D. Brunson, Eddie Huebsch, David Horton, James Deaton, Debi Gentis","doi":"10.1145/2484762.2484793","DOIUrl":"https://doi.org/10.1145/2484762.2484793","url":null,"abstract":"The Oklahoma Cyberinfrastructure Initiative (OCII) is a mechanism by which institutions in the state can share resources, both physical and human, to enable research and education statewide to utilize advanced computing technologies. OCII provides eight kinds of service: access to cyberinfrastructure; dissemination via an annual conference that has reached over 2500 participants in 11 years; education via a workshop series in person and via videoconferencing; faculty/staff development via summer weeklong workshops; outreach via a supercomputing talk suitable for non-technical audiences; proposal support in the form of both letters of commitment and direct collaboration; technology acquired for institutions or assisting those institutions in acquiring it; workforce development in the form of a mentorship program for Information Technology and Computer Science students statewide. To date, OCII has reached 50 academic and 47 non-academic institutions and organizations.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121297736","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":"Comprehensive job level resource usage measurement and analysis for XSEDE HPC systems","authors":"Charng-Da Lu, J. Browne, R. L. Deleon, John L. Hammond, W. Barth, T. Furlani, S. Gallo, Matthew D. Jones, A. Patra","doi":"10.1145/2484762.2484781","DOIUrl":"https://doi.org/10.1145/2484762.2484781","url":null,"abstract":"This paper presents a methodology for comprehensive job level resource use measurement and analysis and applications of the analyses to planning for HPC systems and a case study application of the methodology to the XSEDE Ranger and Lonestar4 systems at the University of Texas. The steps in the methodology are: System-wide collection of resource use and performance statistics at the job and node levels, mapping and storage of the resultant job-wise data to a relational database which eases further implementation and transformation of data to the formats required by specific statistical and analytical algorithms. Analyses can be carried out at different levels of granularity: job, user, or system-wide basis. Measurements are based on a novel lightweight job-centric measurement tool \"TACC_Stats\" [1], which gathers a comprehensive set of metrics on all compute nodes. The data mapping and analysis tools will be an extension to the XDMoD project [2] for the XSEDE community. This paper also reports the preliminary results from the analysis of measured data for Texas Advanced Computing Center's Lonestar4 and Ranger supercomputers. The case studies presented indicate the level of detailed information that will be available for all resources when TACC_Stats is deployed throughout the XSEDE system. The methodology can be applied to any system that runs the TACC_Stats measurement tool.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115237899","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}