Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)最新文献

{"title":"Flexible Partitioning of Scientific Workflows Using the JX Workflow Language","authors":"Tim Shaffer, Nathaniel Kremer-Herman, D. Thain","doi":"10.1145/3332186.3338100","DOIUrl":"https://doi.org/10.1145/3332186.3338100","url":null,"abstract":"Scientific workflows are typically expressed as a graph of logical tasks, each one representing a single program along with its input and output files. A conventional workflow manager transforms each logical task into a discrete batch job and submits it to an underlying execution system. However, converting every logical task into one batch job is not necessarily the most efficient partitioning of a workflow. By grouping multiple logical tasks into a single batch job, we may decrease data transfer, increase system utilization, and reduce the execution time of a workflow. This paper presents JX (JSON eXtended), a declarative language that can express complex workloads as an assembly of sub-graphs that can be partitioned in flexible ways. We present a case study of using JX to represent complex workflows for the Lifemapper biodiversity project. We evaluate partitioning approaches across several computing environments, including HTCondor at the University of Notre Dame, TACC Stampede2, and SDSC Comet, and show that a coarse partitioning results in faster turnaround times, reduced data transfer, and lower master utilization across all three systems.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134395205","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":"Practical Cyber-Security Solutions for the Science DMZ","authors":"P. Hawrylak, George Louthan, John Hale, M. Papa","doi":"10.1145/3332186.3332213","DOIUrl":"https://doi.org/10.1145/3332186.3332213","url":null,"abstract":"The Science DMZ provides high-bandwidth and high-throughput private connectivity to or among computing resources. However, the performance comes at a cost: there is minimal security on the Science DMZ. This is by design, as conventional enterprise network security controls introduce cost, latency and bottlenecks. Data intensive applications, particularly those using geographically distributed resources, rely on the performance of the Science DMZ. With a particular lens toward multi-institution DMZs such as the OneOklahoma Friction Free Network, this paper analyzes performance and security requirements, introduces a threat model specific to the Science DMZ, and delivers recommendations for controls to mitigate these threats in the context of the identified requirements. In particular, we apply a risk-based view to the trade-offs between performance and security, considering impacts both to the science goals of the DMZ's multiple research stakeholders and the more conventional enterprise security posture of the facilities it connects.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"449 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131579759","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":"Expressing and Managing Network Policies for Emerging HPC Systems","authors":"Sergio Rivera, J. Griffioen, Zongming Fei, J. Hayes","doi":"10.1145/3332186.3333045","DOIUrl":"https://doi.org/10.1145/3332186.3333045","url":null,"abstract":"Traditional high performance computing (HPC) centers that operate a single large supercomputer cluster have not required sophisticated mechanisms to manage and enforce network policies. Recently, HPC centers have expanded to support a wide range of computational infrastructure, such as OpenStack-based private clouds and Ceph object stores, each with its own unique characteristics and network security requirements. Network security policies are becoming more complex and harder to manage. To address the challenge, this paper explores ways to define and manage the new network policies required by emerging HPC systems. As the first step, we identify the new types of policies that are required and the technical capabilities needed to support them. We present example policies and discuss ways to implement those policies using emerging programmable networks and intent-based networks. We describe our initial work toward automatically converting human readable network policies into network configurations and programmable network controllers that implement those policies using business rule management systems.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131816613","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":"Cyber Infrastructure: Training and Mentoring: A way to engage students in their technology future","authors":"Diana V. Dugas, Brian D. Ormand","doi":"10.1145/3332186.3332243","DOIUrl":"https://doi.org/10.1145/3332186.3332243","url":null,"abstract":"The Cyber Infrastructure: Training and Mentoring (CI:TraM) program is designed to engage with local-area high school students and undergraduates in Las Cruces, New Mexico to teach them technology skills, as well as provide career mentoring. We have seen a variety of results, mostly aligned with the amount of effort and involvement the student demonstrates. As part of the program, students leave behind the \"do for me\" mindset of high school and early college and enter the \"how do I\" mindset of successful adults. CI:TraM is an individualized program designed to give students the freedom to explore career options in-depth, as well as expose students to the technology needed in their desired career choice. Students engage with professionals in possible disciplines to decide their career paths and determine how to reach those goals both academically and financially. Students learn core technology skills, such as Excel, and discipline-specific ones, such as Matlab and Auto-CAD. The CI:TraM program has helped engaged students to solidify their career paths and feel more confident both professionally and as persons on college campuses. Although longitudinal data is forthcoming, students have identified their believed benefits of the program and its impact on their lives.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116334725","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":"Breaking Resource Discovery Barriers","authors":"J. Navarro, Amy Hovious","doi":"10.1145/3332186.3333153","DOIUrl":"https://doi.org/10.1145/3332186.3333153","url":null,"abstract":"By aggregating research IT resource information across the University of Illinois system and the XSEDE program we can enhance and simplify how researchers, educators, and students discover and use technology across departments, campuses, and institutional boundaries in support of research and educational activities.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121949187","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":"Developing Edge Services for Federated Infrastructure Using MiniSLATE","authors":"Joe Breen, L. Bryant, Jiahui Chen, Emerson Ford, R. Gardner, Gage Glupker, Skyler Griffith, Ben Kulbertis, S. McKee, R. Pierce, B. Riedel, Mitchell Steinman, Jason Stidd, Luan Truong, Jeremy Van, I. Vukotic, Christopher N. Weaver","doi":"10.1145/3332186.3332236","DOIUrl":"https://doi.org/10.1145/3332186.3332236","url":null,"abstract":"Modern software development workflow patterns often involve the use of a developer's local machine as the first platform for testing code. SLATE mimics this paradigm with an implementation of a light-weight version, called MiniSLATE[? ], that runs completely contained on the developer's local machine or scales to larger machines (laptop, virtual machine, or another physical server). MiniSLATE resolves many development environment issues by providing an isolated and local configuration for the developer. Application developers are able to download MiniSLATE which provides a fully orchestrated set of containers on top of a production SLATE platform, complete with central information service, API server, and a local Kubernetes cluster. This approach mitigates the overhead of a hypervisor but still provides the requisite isolated environment. They are able to create the environment, iterate, destroy it, and repeat at will. A local MiniSLATE environment also allows the developer to explore the packaging of the edge service within a constrained security context in order to validate its full functionality within limited permissions. As a result, developers are able to test the functionality of their application with the complete complement of SLATE components local to their development environment without the overhead of building a cluster or virtual machine, registering a cluster, interacting with the production SLATE platform, etc.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117106017","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":"Facial Expression Recognition: Utilizing Digital Image Processing, Deep Learning, and High-Performance Computing","authors":"Francisco Reveriano, Ünal Sakoglu, Jiang Lu","doi":"10.1145/3332186.3333039","DOIUrl":"https://doi.org/10.1145/3332186.3333039","url":null,"abstract":"The purpose of this project was to analyze which image pre-processing technique was most beneficial in improving the performance of Facial Expression Recognition through Deep Learning and High-Performance Computing. Contrary to our expectations, the results obtained in this work showed that deep learning does not significantly benefit from various commonly used image pre-processing techniques such as resizing, smoothing, or edge detection. The results confirm previous findings that an increase in accuracy is obtained by increasing the size of the training dataset. This study proceeds to show that the increase in training data size can easily be handled by the High-Performance Computing (HPC) cluster provided by the Pittsburg Supercomputing Center (PSC) through XSEDE.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128207082","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":"Extracting Domain Information using Deep Learning","authors":"Amit Gupta, Weijia Xu, P. Jaiswal, Crispin Taylor, Jennifer Regala","doi":"10.1145/3332186.3332255","DOIUrl":"https://doi.org/10.1145/3332186.3332255","url":null,"abstract":"Across various scientific domains, digital publication of technical documents, often in the form of conference/journal article submissions, are the first accessible instance of new human knowledge in these respective fields. Synthesizing and curating this information is a slow and difficult process and often requires non-trivial human expertise. Given the ever increasing rate of these publications and the natural limitations of manual approaches, a computational solution to this problem is the paramount need of the hour. One of the central tasks is to extract important phrases and terminologies from scientific article. Although many tools are available to extracting keywords and named entities from a document, a key challenge is to determine how important they are with regard the context of the entire document. In some cases, important entities to an article might be a new vocabulary that haven't, or rarely, appeared from previous data. In other cases, there are also many other existing entities that are less important for the particular article but weighted more significantly from models based on prior knowledge. In this paper, we investigate how deep learning methods may be used to address this issue. We have developed a computational tool to provide entity extraction and expert curation functionality. The tool has been integrated with the publication pipeline used in American Society of Plant Biologists. Using the author feedback mechanism in our deployed tool we were able to create a expert user annotated dataset based on articles submitted over an entire year. Using this new gold standard dataset for supervised training, we are now able to contrast several methods for the entity extraction task. We use the NeuroNER tool to investigate the effectiveness of deep neural network in this task and also contrast it with other tools using a variety of different methods such as ABNER (using CRF) and DIVE (using an ensemble of regular expression rules, keyword dictionaries and ontology files). Our results show that DIVE ensemble of methods have higher precision scores than pre-trained CRF models included in ABNER. However, early results from NeuroNER training with on author annotations shows very promising improvement on predicting the important words from the documents.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127374277","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":"LSU Computational System Biology Gateway for Education","authors":"Eroma Abeysinghe, M. Brylinski, Marcus A. Christie, S. Marru, M. Pierce","doi":"10.1145/3332186.3333259","DOIUrl":"https://doi.org/10.1145/3332186.3333259","url":null,"abstract":"Science gateways are a mechanism for delivering scientific software as a service, especially when the software requires high performance computing (HPC) resources to run effectively. The existence of a science gateway eliminates the user's need to learn to work with HPC systems and to manage software installations and updates. With well-designed user interfaces, users can more quickly become effective users of scientific applications and can manage information needed for replicating, modifying, and sharing results. All of these efficiency gains enable users to focus more on their research. In addition, science gateways are being identified as an effective educational tool, a tool to be used in classroom environments as a method to get students quickly into research on domain specific questions. In the absence of a science gateway, students are likely to need a considerable time to learn to work with HPC systems, and any time spent on such will reduce their time on the actual science. This poster presents how the Louisiana State University (LSU) gateway for the Computational System Biology Group (CSBG) - (www.brylinski.org) was updated and improved to be a classroom teaching tool. This work makes extensive use of Apache Airavata's group management capabilities.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131072640","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":"Publishing and Serving Machine Learning Models with DLHub","authors":"Ryan Chard, Logan T. Ward, Zhuozhao Li, Y. Babuji, A. Woodard, S. Tuecke, K. Chard, B. Blaiszik, Ian T Foster","doi":"10.1145/3332186.3332246","DOIUrl":"https://doi.org/10.1145/3332186.3332246","url":null,"abstract":"In this paper we introduce the Data and Learning Hub for Science (DLHub). DLHub serves as a nexus for publishing, sharing, discovering, and reusing machine learning models. It provides a flexible publication platform that enables researchers to describe and deposit models by associating publication and model-specific metadata and assigning a persistent identifier for subsequent citation. DLHub also supports scalable model inference, allowing researchers to execute inference tasks using a distributed execution engine, containerized models, and Kubernetes. Here we describe DLHub and present four scientific use cases that illustrate how DLHub can be used to reliably, efficiently, and scalably integrate ML into scientific processes.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130529575","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}