Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)最新文献

{"title":"iCER Interns: Engaging Undergraduates in High Performance Computing","authors":"D. Colbry","doi":"10.1145/2616498.2616573","DOIUrl":"https://doi.org/10.1145/2616498.2616573","url":null,"abstract":"The Institute for Cyber-Enabled Research (iCER) at Michigan State University (MSU) has an internship program to provide undergraduate students with hands-on experience in advanced computational research. The goals of the iCER Intern program are: (1) to give students in-depth exposure to Advanced Computing; (2) to leverage students' knowledge and skills to support iCER administrators, staff and researchers; and (3) to minimize the investment of mentors' time and resources, while maximizing student productivity. This paper details the evolution and structure of the iCER Intern program, which provides a rich educational experience for undergraduates while efficiently managing the efforts of iCER mentors. The program structure helps iCER staff quickly assess the interests and skills of new student interns and encourages productivity among the undergraduates. The result is a net positive return on the investment of mentors' time and effort, and a valuable professional learning experience for students. The methodology described here is based on lessons learned in mentoring more than 50 undergraduate students, and other institutions or high performance computing centers could readily adopt this program structure and approach.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"25 1","pages":"71:1-71:5"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75335135","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":"Efficient 3D Movement-Based Kernel Density Estimator and Application to Wildlife Ecology","authors":"J. Tracey, James K. Sheppard, Glenn K. Lockwood, A. Chourasia, M. Tatineni, R. Fisher, R. Sinkovits","doi":"10.1145/2616498.2616522","DOIUrl":"https://doi.org/10.1145/2616498.2616522","url":null,"abstract":"We describe an efficient implementation of a 3D movement-based kernel density estimator for determining animal space use from discrete GPS measurements. This new method provides more accurate results, particularly for species that make large excursions in the vertical dimension. The downside of this approach is that it is much more computationally expensive than simpler, lower-dimensional models. Through a combination of code restructuring, parallelization and performance optimization, we were able to reduce the time to solution by up to a factor of 1000x, thereby greatly improving the applicability of the method.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"42 1","pages":"14:1-14:8"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80922326","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 SAGA and the Open Science Grid to Search for Aptamers","authors":"Kevin R. Shieh, Pilib Ó Broin, David Rhee, M. Levy, A. Golden","doi":"10.1145/2616498.2616517","DOIUrl":"https://doi.org/10.1145/2616498.2616517","url":null,"abstract":"RNA aptamers are small oligonucleotide molecules whose composition and resulting folded structure enable them to bind with high affinity and high selectivity to target ligands and therefore hold great promise as potential therapeutic drugs. Functional aptamers are selected from a large, randomized initial library in a process known as SELEX (systematic evolution of ligands by exponential enrichment). This is an iterative process involving numerous rounds of binding, elution, and amplification against a specific target substrate. During each iteration -- or round of selection -- we enrich for the species with the highest binding affinity to the target. After multiple rounds, we ideally have an enriched aptamer library suitable for subsequent investigation. Modern techniques employ massively parallel sequencing, enabling the generation of large libraries (~106 sequences) in a matter of hours for each round of selection. As RNA is single-stranded, covariance models (CMs) are ideal for representing motifs in their secondary structures, allowing us to discover patterns within functional aptamer populations following each round. CMs have been implemented in Infernal, a program that infers RNA alignments based on RNA sequence and structure. Calibrating a single CM in Infernal can take several hours and is a significant performance bottleneck for our work. However, as each CM calculation is itself independently determined and requires defined processing and memory resources, their computation in parallel offers a potential solution to this problem. In this paper, we describe using the Open Science Grid (OSG) to facilitate the identification of aptamer motifs by running CM calibrations and refinements in parallel across up to ten OSG clients. We use the Simple API for Grid Applications (SAGA) to interface with OSG and manage job submissions and file transfers. When run in parallel, our results show a significant speed up, constrained by typical latencies and QoS associated with nominal OSG usage. Our work demonstrates the ability of SAGA and the OSG to assist in parallelizing solutions to complex sequencing-based biomedical challenges.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"86 1","pages":"27:1-27:4"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83934136","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":"Highly Energetic Collisions of Xe with Fullerene Clusters","authors":"D. Nugawela, S. Stuart, J. Jakowski","doi":"10.1145/2616498.2616514","DOIUrl":"https://doi.org/10.1145/2616498.2616514","url":null,"abstract":"One subset of the fullerene activities is collision experiments using high energetic inert gases with fullerene clusters. We report on the formation of fullerene oligomers upto (C60)m, m=2-12 following the collisions of 200 keV and 400 keV Xe with (C60)55 fullerene clusters using classical reactive dynamics. A preference for C60+n (n=1-4) fragments was observed after the collision. The sequence of peaks detected in the range C105 - C122 after 1 ns from the collision is comparable with the experimental results. According to the post collisional dynamics, a dimer formed with one cross-link between two fullerenes led to a peanut shaped molecule after 25 ns and a linear trimer has turned into a carbon nanotube like structure after 43 ns. At the ns time scale, more organized carbon molecules as well as big amorphous carbon chunks also remained as collisional products.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"25 1","pages":"18:1-18:8"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85818094","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":"Accelerating Sparse Canonical Correlation Analysis for Large Brain Imaging Genetics Data","authors":"Jingwen Yan, Hui Zhang, Lei Du, E. Wernert, A. Saykin, Li Shen","doi":"10.1145/2616498.2616515","DOIUrl":"https://doi.org/10.1145/2616498.2616515","url":null,"abstract":"Recent advances in acquiring high throughput neuroimaging and genomics data provide exciting new opportunities to study the influence of genetic variation on brain structure and function. Research in this emergent field, known as imaging genetics, aims to identify the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs). Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. However, the scale and complexity of the imaging genetic data have presented critical computational bottlenecks requiring new concepts and enabling tools. In this paper, we present our initial efforts on developing a set of massively parallel strategies to accelerate a widely used SCCA implementation provided by the Penalized Multivariate Analysis (PMA) software package. In particular, we exploit parallel packages of R, optimized mathematical libraries, and the automatic offload model for Intel Many Integrated Core (MIC) architecture to accelerate SCCA. We create several simulated imaging genetics data sets of different sizes and use these synthetic data to perform comparative study. Our performance evaluation demonstrates that a 2-fold speedup can be achieved by the proposed acceleration. The preliminary results show that by combining data parallel strategy and the offload model for MIC we can significantly reduce the knowledge discovery timelines involving applying SCCA on large brain imaging genetics data.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"1 1","pages":"4:1-4:7"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90699603","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":"Performance of Applications using Dual-Rail InfiniBand 3D Torus network on the Gordon Supercomputer","authors":"D. J. Choi, Glenn K. Lockwood, R. Sinkovits, M. Tatineni","doi":"10.1145/2616498.2616541","DOIUrl":"https://doi.org/10.1145/2616498.2616541","url":null,"abstract":"Multi-rail InfiniBand networks provide options to improve bandwidth, increase reliability, and lower latency for multi-core nodes. The Gordon supercomputer at SDSC, with its dual-rail InfiniBand 3-D torus network, is used to evaluate the performance impact of using multiple rails. The study was performed using the OSU micro-benchmarks, the P3FFT application kernel, and scientific applications LAMMPS and AMBER. The micro-benchmarks confirmed the bandwidth and latency performance benefits. At the application level, performance improvements depended on the communication level and profile.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"63 1","pages":"43:1-43:6"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91136951","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":"Integrating Science Gateways with XSEDE Security: A Survey of Credential Management Approaches","authors":"J. Basney, Jeff Gaynor, S. Marru, M. Pierce, Thejaka Amila Kanewala, R. Dooley, Joe Stubbs","doi":"10.1145/2616498.2616559","DOIUrl":"https://doi.org/10.1145/2616498.2616559","url":null,"abstract":"We present a survey of credential management approaches for science gateways to integrate with the X.509 security infrastructure used by XSEDE.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"23 1","pages":"58:1-58:2"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88567744","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":"Instrumenting Genomic Sequence Analysis Pipeline Mothur on Shared Memory Architecture","authors":"Junqi Yin, Bhanu Rekepalli, Pragneshkumar B. Patel, Chanda Drennen, A. Engel","doi":"10.1145/2616498.2616505","DOIUrl":"https://doi.org/10.1145/2616498.2616505","url":null,"abstract":"Mothur is an open source bioinformatics pipeline used for biological sequence analysis that has gained increasing attention in the microbial ecology community. Because a large set of functionalities in Mothur are memory bound, it is well suited for shared memory architectures, such as the Nautilus supercomputer. In this paper, we present performance results for several commands in Mothur that are popular in the operational taxonomic unit analysis, and show that Nautilus can accelerate pipeline processes many orders of magnitude faster.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"8 1","pages":"19:1-19:4"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84788696","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":"Integrating Performance Measurement and Program Evaluation to Promote Understanding","authors":"L. Rivera, L. DeStefano","doi":"10.1145/2616498.2616577","DOIUrl":"https://doi.org/10.1145/2616498.2616577","url":null,"abstract":"In this extended abstract, we describe the trend in reporting performance assessments of publicly funded social programs as a result of public demands for accountability and its effect on evaluation. XSEDE's Training, Education, and Outreach Services (TEOS) external evaluation team outlines the differences between performance measurement and program evaluation, makes a case for their integration, and describes their effort to design and implement such an evaluation within XSEDE. Plans for integrating the developing performance measurement system and program evaluation within TEOS are discussed as well as initial steps toward integration to date.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"37 1","pages":"72:1-72:2"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85354639","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 SIMD Solution for the Quadratic Assignment Problem with GPU Acceleration","authors":"Abhilash Chaparala, C. Novoa, Apan Qasem","doi":"10.1145/2616498.2616521","DOIUrl":"https://doi.org/10.1145/2616498.2616521","url":null,"abstract":"In the Quadratic Assignment Problem (QAP), n units (usually departments, machines, or electronic components) must be assigned to n locations given the distance between the locations and the flow between the units. The goal is to find the assignment that minimizes the sum of the products of distance traveled and flow between units. The QAP is a combinatorial problem difficult to solve to optimality even for problems where n is relatively small (e.g., n = 30). In this paper, we solve the QAP problem using a parallel algorithm that employs a 2-opt heuristic and leverages the compute capabilities of current GPUs. The algorithm is implemented on the Stampede cluster hosted by the Texas Advanced Computing Center (TACC) at the University of Texas at Austin and on a GPU-equipped server housed at Texas State University. We enhance our implementation by fine tuning the occupancy levels and by exploiting inter-thread data locality through improved shared memory allocation. On a series of experiments on the well-known QAPLIB data sets, our algorithm, on average, outperforms an OpenMP implementation by a factor of 16.31 and a Tabu search based GPU implementation by a factor of 58.61. Given the wide applicability of QAP, the algorithm we propose has very good potential to accelerate the discovery in scholarly research in Engineering, particularly in the fields of Operations Research and design of electronic devices.","PeriodicalId":93364,"journal":{"name":"Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)","volume":"1 1","pages":"1:1-1:8"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84142118","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}