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

{"title":"iData: a community geospatial data sharing environment to support data-driven science","authors":"Rajesh Kalyanam, Lan Zhao, Carol X. Song, Yuet Ling Wong, Jaewoo Lee, Nelson B. Villoria","doi":"10.1145/2484762.2484813","DOIUrl":"https://doi.org/10.1145/2484762.2484813","url":null,"abstract":"With the advent of XSEDE, the national cyberinfrastructure has evolved from a set of traditional HPC resources to a broader range of digital services. Science gateways, which serve as portals to scientific applications, have also evolved as researchers are dealing with rapidly expanding scientific datasets and the increasingly complex workflows. More and more gateways are being developed to support integrated services for running data-driven applications on HPC resources such as those on XSEDE. To facilitate this type of workflow, there is a pressing need for web-based data management systems that are easy to use, support data upload, sharing, access and management, and can be integrated with advanced computation and storage resources. More importantly such systems need to be accessible by users from the broad research and education communities. In this paper, we describe the design and implementation of iData, a web-based community data publishing and sharing system. iData supports both generic file-based data collections and several commonly used environmental data collection formats including time series, GIS vector and raster data. Integrated data processing, visualization and filtering capabilities are provided for these data formats. Currently iData can be downloaded and deployed in a HUBzero-based gateway, and we plan to make it available for non-HUBzero platforms in the future. We present two examples in which iData has been successfully used to support research collaboration in driNET and GEOSHARE projects.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"10 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":"134068024","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":"1000 words: advanced visualization for the humanities","authors":"Rob Turknett, Brandt M. Westing, Samuel Moore","doi":"10.1145/2484762.2484835","DOIUrl":"https://doi.org/10.1145/2484762.2484835","url":null,"abstract":"1000 Words is a project to enable discoveries at extreme scale in the Humanities. Funded by the National Endowment for the Humanities (NEH), this project aims to make advanced visualization systems attached to high performance computing resources both useful and usable for scholars in the arts and humanities. This paper describes Massive Pixel Environment (MPE), our initial effort toward this goal. Massive Pixel Environment is a software library developed at the Texas Advanced Computing Center (TACC) for extending Processing sketches to multi-node tiled displays. Processing is an open source programming language and environment for creating images, animations and interactions. MPE significantly lowers the learning curve and time needed to develop software and interactive visualizations for multi-node tiled displays. We will discuss the applications and implications of MPE for the sciences, humanities, and media arts.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"48 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":"122092077","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":"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":"Optimizing utilization across XSEDE platforms","authors":"Haihang You, Charng-Da Lu, Ziliang Zhao, Fei Xing","doi":"10.1145/2484762.2484778","DOIUrl":"https://doi.org/10.1145/2484762.2484778","url":null,"abstract":"HPC resources provided by XSEDE give researchers unique opportunities to carry out scientific studies. As of 2013 XSEDE consists of 16 systems with varied architectural designs and capabilities. The hardware heterogeneity and software diversity make efficient utilization of such a federation of computing resources very challenging. For example, users are constantly faced with a myriad of possibilities to build and run an application: compilers, numerical libraries, and runtime parameters. In this paper we report performance data of several popular scientific applications built with different compilers and numerical libraries available on two XSEDE systems: Kraken and Gordon, and suggest the best way to compile applications for optimal performance. By comparison, we validate SU conversion factors between the aforementioned XSEDE systems from application's viewpoint.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"10 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":"114238103","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 tale of three outreach programs: strategic collaboration across XSEDE outreach services","authors":"L. Akli, R. Kravetz, R. Moye","doi":"10.1145/2484762.2484799","DOIUrl":"https://doi.org/10.1145/2484762.2484799","url":null,"abstract":"This is a tale of how three outreach programs with very different missions have enhanced the impact of the XSEDE Scholars Program through strong collaboration. The XSEDE Scholars Program (XSP) is a program for U.S. students from underrepresented groups in the area of computational sciences that provides opportunities to learn more about high performance computing and XSEDE resources and to network with cutting-edge researchers and professional leaders. The mission of the XSEDE MSI Outreach program is to expand the number of faculty from Minority Serving Institutions (MSIs) and underrepresented groups engaged in the use of XSEDE resources, HPC, and computational science and engineering. The XSEDE Campus Champions program strengthens the connection between campuses and XSEDE by supporting campus representatives as a local source of knowledge about high-performance and high-throughput computing and other digital services, opportunities and resources.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"56 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":"115927740","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":"FluMapper: an interactive CyberGIS environment for massive location-based social media data analysis","authors":"Anand Padmanabhan, Shaowen Wang, G. Cao, Myunghwa Hwang, Yanli Zhao, Zhenhua Zhang, Yizhao Gao","doi":"10.1145/2484762.2484821","DOIUrl":"https://doi.org/10.1145/2484762.2484821","url":null,"abstract":"Social media, such as social network (e.g., Facebook), microblogs (e.g. Twitter) have experienced a spectacular rise in popularity, and attracting hundreds of millions of users generating unprecedented amount of information. Twitter, for example, has rapidly gained approximately 500 million registered users as of 2012, generating 340 million tweets daily. Although each tweet is limited to only 140 characters, the aggregate of millions of tweets may provide a realistic representation of landscapes for a certain topic of interest. Furthermore, with widespread use of location aware mobile devices, users are sharing their whereabouts through social media services. This has resulted in a dramatic increase in volume of spatial data and they are becoming a crucial attribute of social media. These location-based social media thus could provide valuable insights to understanding many geographic phenomena. Recent studies capitalizing on social networking and media data show significant societal impacts, in many areas including infectious disease tracking [1].","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"61 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":"124115309","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":"Using XDMoD to facilitate XSEDE operations, planning and analysis","authors":"T. Furlani, Barry L. Schneider, Matthew D. Jones, John Towns, David L. Hart, S. Gallo, R. L. Deleon, Charng-Da Lu, Amin Ghadersohi, Ryan J. Gentner, A. Patra, G. Laszewski, Fugang Wang, Jeffrey T. Palmer, N. Simakov","doi":"10.1145/2484762.2484763","DOIUrl":"https://doi.org/10.1145/2484762.2484763","url":null,"abstract":"The XDMoD auditing tool provides, for the first time, a comprehensive tool to measure both utilization and performance of high-end cyberinfrastructure (CI), with initial focus on XSEDE. Here, we demonstrate, through several case studies, its utility for providing important metrics regarding resource utilization and performance of TeraGrid/XSEDE that can be used for detailed analysis and planning as well as improving operational efficiency and performance. Measuring the utilization of high-end cyberinfrastructure such as XSEDE helps provide a detailed understanding of how a given CI resource is being utilized and can lead to improved performance of the resource in terms of job throughput or any number of desired job characteristics. In the case studies considered here, a detailed historical analysis of XSEDE usage data using XDMoD clearly demonstrates the tremendous growth in the number of users, overall usage, and scale of the simulations routinely carried out. Not surprisingly, physics, chemistry, and the engineering disciplines are shown to be heavy users of the resources. However, as the data clearly show, molecular biosciences are now a significant and growing user of XSEDE resources, accounting for more than 20 percent of all SUs consumed in 2012. XDMoD shows that the resources required by the various scientific disciplines are very different. Physics, Astronomical sciences, and Atmospheric sciences tend to solve large problems requiring many cores. Molecular biosciences applications on the other hand, require many cycles but do not employ core counts that are as large. Such distinctions are important in guiding future cyberinfrastructure design decisions. XDMoD's implementation of a novel application kernel-based auditing system to measure overall CI system performance and quality of service is shown, through several examples, to provide a useful means to automatically detect under performing hardware and software. This capability is especially critical given the complex composition of today's advanced CI. Examples include an application kernel based on a widely used quantum chemistry program that uncovered a software bug in the I/O stack of a commercial parallel file system, which was subsequently fixed by the vendor in the form of a software patch that is now part of their standard release. This error, which resulted in dramatically increased execution times as well as outright job failure, would likely have gone unnoticed for sometime and was only uncovered as a result of implementation of XDMoD's suite of application kernels.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"36 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":"121365732","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":"Preliminary experiences with the uintah framework on Intel Xeon Phi and stampede","authors":"Qingyu Meng, A. Humphrey, John A. Schmidt, M. Berzins","doi":"10.1145/2484762.2484779","DOIUrl":"https://doi.org/10.1145/2484762.2484779","url":null,"abstract":"In this work, we describe our preliminary experiences on the Stampede system in the context of the Uintah Computational Framework. Uintah was developed to provide an environment for solving a broad class of fluid-structure interaction problems on structured adaptive grids. Uintah uses a combination of fluid-flow solvers and particle-based methods, together with a novel asynchronous task-based approach and fully automated load balancing. While we have designed scalable Uintah runtime systems for large CPU core counts, the emergence of heterogeneous systems presents considerable challenges in terms of effectively utilizing additional on-node accelerators and co-processors, deep memory hierarchies, as well as managing multiple levels of parallelism. Our recent work has addressed the emergence of heterogeneous CPU/GPU systems with the design of a Unified heterogeneous runtime system, enabling Uintah to fully exploit these architectures with support for asynchronous, out-of-order scheduling of both CPU and GPU computational tasks. Using this design, Uintah has run at full scale on the Keeneland System and TitanDev. With the release of the Intel Xeon Phi co-processor and the recent availability of the Stampede system, we show that Uintah may be modified to utilize such a coprocessor based system. We also explore the different usage models provided by the Xeon Phi with the aim of understanding portability of a general purpose framework like Uintah to this architecture. These usage models range from the pragma based offload model to the more complex symmetric model, utilizing all co-processor and host CPU cores simultaneously. We provide preliminary results of the various usage models for a challenging adaptive mesh refinement problem, as well as a detailed account of our experience adapting Uintah to run on the Stampede system. Our conclusion is that while the Stampede system is easy to use, obtaining high performance from the Xeon Phi co-processors requires a substantial but different investment to that needed for GPU-based systems.","PeriodicalId":426819,"journal":{"name":"Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery","volume":"72 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":"114697889","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":"Providing a supported online course on parallel computing","authors":"S. Gordon, Jay Alameda, J. Demmel, R. Carbunescu, Susan Mehringer","doi":"10.1145/2484762.2484765","DOIUrl":"https://doi.org/10.1145/2484762.2484765","url":null,"abstract":"Learning the principles of computational modeling and parallel computing requires more than a short workshop. Workshops generally run from a few hours to a few days and are therefore limited in the amount of material that can be covered. In addition, it is more difficult for participants to retain large amounts of new material under the time pressures of a workshop. Deeper understanding of such complex materials can come from more traditional academic courses. Yet, many institutions either lack the expertise or the curriculum flexibility to offer such courses. In the spring of 2013 we offered the equivalent of a full semester course entitled Applications of Parallel Computing as an open, online course in an effort to address these issues. The course was offered over a period of thirteen weeks using materials captured from the University of California Berkeley course CS267. Enrollment was initially limited to 345 students. Creating and implementing the course involved decisions in several areas: design of the instructional materials, creating an environment to run programming assignments, support mechanisms for the large number of students taking the course, and automatic grading of assignments. In this session, we will present a summary of the experience in addressing these questions along with an evaluation of the course outcomes.","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":"122686578","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}