Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)最新文献_第3页

Employing Augmented Reality for Cybersecurity Operations in High Performance Computing Environments 在高性能计算环境中使用增强现实技术进行网络安全操作

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3333160

Nitin Sukhija, C. Haser, Elizabeth Bautista

{"title":"Employing Augmented Reality for Cybersecurity Operations in High Performance Computing Environments","authors":"Nitin Sukhija, C. Haser, Elizabeth Bautista","doi":"10.1145/3332186.3333160","DOIUrl":"https://doi.org/10.1145/3332186.3333160","url":null,"abstract":"With the advent of myriad different technologies in the modern computing era, the future of high performance computing (HPC) data centers comprising heterogeneous compute servers and networks involves a significantly greater degree of cyber-threats than we are observing currently. As a result, conducting cybersecurity operations at the HPC data centers encompasses a highly cognitive workload environment, where information is rapidly generated, is exceedingly dynamic and is vastly unpredictable. An important challenge faced by cybersecurity specialists is managing the extreme levels of stress and utilizing emerging technologies for monitoring, assessing and defending multiple cyber-attacks in real-time. In this paper, we present an augmented reality based framework for storing, analyzing, visualizing and augmenting data at the HPC centers. This framework is proposed with the goal of improving the data presentation, forecasting, analysis and decision making, thus reducing the time to insight and learning the HPC security operations to combat increasing cyber physical threats.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"39 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":"130733442","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}

引用次数: 0

Expanding the Computational Anatomy Gateway from clinical imaging to basic neuroscience research 将计算解剖学门户从临床成像扩展到基础神经科学研究

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3332217

D. Tward, Anthony Kolasny, Fatima Khan, J. Troncoso, M. Miller

{"title":"Expanding the Computational Anatomy Gateway from clinical imaging to basic neuroscience research","authors":"D. Tward, Anthony Kolasny, Fatima Khan, J. Troncoso, M. Miller","doi":"10.1145/3332186.3332217","DOIUrl":"https://doi.org/10.1145/3332186.3332217","url":null,"abstract":"The Computational Anatomy Gateway, powered largely by the Comet (San Diego Super-computer Center) and Stampede (Texas Advanced Computing Center) clusters through XSEDE, provides software as a service tools for atlas based analysis of human brain magnetic resonance images. This includes deformable registration, automatic labeling of tissue types, and morphometric analysis. Our goal is to extend these services to the broader neuroscience community, accommodating multiple model organisms and imaging modalities, as well as low quality or missing data. We developed a new approach to multimodality registration: by predicting one modality from another, we can replace ad hoc image similarity metrics (such as mutual information or normalized cross correlation) with a log likelihood under a noise model. This statistical approach enables us to account for missing data using the Expectation Maximization algorithm. For portability and scalability we have implemented this algorithm in tensorflow. For accessibility we have compiled and many working examples for multiple model organisms, imaging systems, and missing tissue or image anomaly situations. These examples are made easily usable in the form of Jupyter notebooks, and made publicly available through github. This framework will significantly reduce the barrier to entry for basic neuroscientists, enabling the community to benefit from atlas based computational image analysis techniques.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"6 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":"127954528","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}

引用次数: 0

HiperVR

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3337958

Ngan V. T. Nguyen, Lino Virgen, Tommy Dang

引用次数: 2

A Multi-Environment HPC-Scale Puppet Infrastructure for Compliance and Systems Automation 用于遵从性和系统自动化的多环境高性能计算规模傀儡基础设施

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3332240

Raychel M. Benson, Edward A. Munsell, N. Bertrand, Michael Baynton, Evan F. Bollig, Jeffrey McDonald

{"title":"A Multi-Environment HPC-Scale Puppet Infrastructure for Compliance and Systems Automation","authors":"Raychel M. Benson, Edward A. Munsell, N. Bertrand, Michael Baynton, Evan F. Bollig, Jeffrey McDonald","doi":"10.1145/3332186.3332240","DOIUrl":"https://doi.org/10.1145/3332186.3332240","url":null,"abstract":"Unifying configuration management across High Performance Compute (HPC) and supporting infrastructure systems is a challenge commonly faced by research computing centers. System operators no longer have the luxury to manage one-off cases manually; automation is essential. Centralized tooling and site-wide configuration promises efficiencies, but the substantial differences between, for example, compute nodes and networking gear can dissuade operators from attempting to manage their entire fleet with a single tool. To solve this, the Minnesota Supercomputing Institute (MSI) at the University of Minnesota assembled an innovative collection of utilities for Institute-wide management of systems with Puppet 5 as the centerpiece. The innovative solution emerged from the need for more than a dozen system operators/administrators to coordinate changes across a medium- to large-scale data-center with over 1500 nodes. A centralized base configuration ensures all systems are in-line with University security policies and other compliance needs, while the Puppet infrastructure additionally enables per-cluster or even per-node customizations as needed. This document presents the architecture of MSI's orchestration and management infrastructure, as well as the workflow followed by operators to provision disparate systems with Puppet and ensure that quality, accountability, and compliance requirements are met. The resulting system is actively used in the day-to-day management of the Institute, including persistent infrastructure services, cluster head- and compute-nodes, and even networking equipment. The workflow described herein is recommended for similar research computing institutions, including those with as few as five nodes and two operators.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"17 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":"123837529","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}

引用次数: 2

A Virtual Reality Approach to Pediatric Conflict De-escalation and Anger Management 儿童冲突降级和愤怒管理的虚拟现实方法

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3338098

Heyi Liu, Mary Loveless

{"title":"A Virtual Reality Approach to Pediatric Conflict De-escalation and Anger Management","authors":"Heyi Liu, Mary Loveless","doi":"10.1145/3332186.3338098","DOIUrl":"https://doi.org/10.1145/3332186.3338098","url":null,"abstract":"In recent years, anger management has received considerable attention in the school setting. Youth suffering from elevated anger often relates to health problems, typically cardiovascular diseases; physical aggression, negative verbal responses, and drug use have also been reported as a negative consequence of unmanaged anger. While many forms of treatment currently exist, they often require a counselor's assistance in providing suitable de-escalation methods. For public schools, most states have difficulties allocating funding to establish a large enough school counseling program; therefore, anger management services are not commonly accessible in schools. Virtual Reality (VR) creates an immersive experience through computer-generated environments. First popularized as a game enhancement, VR has contributed to academia by recreating a desired atmosphere, notably within psychological research. This study investigates the potential of a virtual reality de-escalation mechanism to increase its accessibility and affordability in a school setting. The program aims to achieve an immediate de-escalation for the youth through a calm room with minimal adult assistance and prepare them to re-enter the real world by creating a solution to a common anger trigger which can hopefully eventually reduce their dependence on the technology.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"40 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":"128083807","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}

引用次数: 2

Extensible Framework for Analysis of Farm Practices and Programs 农业实践和计划分析的可扩展框架

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3337063

Sandeep Puthanveetil Satheesan, Rabin Bhattarai, Shannon Bradley, Jonathan W. Coppess, Lisa Gatzke, Rishabh Gupta, Hanseok Jeong, Jong S. Lee, Vara Veera Gowtham Naraharisetty, M. Ondrejcek, G. Schnitkey, Yan Zhao, Christopher M. Navarro

{"title":"Extensible Framework for Analysis of Farm Practices and Programs","authors":"Sandeep Puthanveetil Satheesan, Rabin Bhattarai, Shannon Bradley, Jonathan W. Coppess, Lisa Gatzke, Rishabh Gupta, Hanseok Jeong, Jong S. Lee, Vara Veera Gowtham Naraharisetty, M. Ondrejcek, G. Schnitkey, Yan Zhao, Christopher M. Navarro","doi":"10.1145/3332186.3337063","DOIUrl":"https://doi.org/10.1145/3332186.3337063","url":null,"abstract":"We present an open source extensible web framework for the analysis of different farm practices and programs and easy dissemination of their results to the users. Currently, this framework is being applied to two use cases --- a web-based decision support system for cover crop management and a web-based farm program analysis tool to assist farmers, academics, and policymakers to understand programs and policies surrounding the Farm Bill. Through the first use case, we address the problem of bridging the gap between the scientific research that happens in labs and experimental plots and the day to day practices and decisions taken by the farmers in the fields. Specifically, this use case focuses on the practice of cover crops, their management, and the impact on reducing nutrient runoff into water bodies. Through the second use case, we address the problem of predicting the expected payment amounts and measured risk or probability of payment for different government insurance programs authorized by the 2018 Farm Bill, namely the Agriculture Risk Coverage (ARC) and Price Loss Coverage (PLC). This helps the farmers compare these programs based on forecasted crop yields and prices. In this paper, we describe the overall architecture of the framework and its major components, the use cases that are currently benefiting from using this framework and share screenshots of the web applications developed using this framework for those use cases. We also share our plans for future work and conclusions about applying this framework to the two use cases.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"54 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":"133932194","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}

引用次数: 0

Deploying a Top-100 Supercomputer for Large Parallel Workloads: the Niagara Supercomputer 为大型并行工作负载部署百强超级计算机:尼亚加拉超级计算机

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3332195

Marcelo Ponce, R. van Zon, Scott A. Northrup, D. Gruner, Joseph M. Chen, Fatih Ertinaz, A. Fedoseev, L. Groer, Fei Mao, B. Mundim, M. Nolta, Jaime Pinto, M. Saldarriaga, Vladimir Slavnic, Erik Spence, Ching-Hsing Yu, W. Peltier

{"title":"Deploying a Top-100 Supercomputer for Large Parallel Workloads: the Niagara Supercomputer","authors":"Marcelo Ponce, R. van Zon, Scott A. Northrup, D. Gruner, Joseph M. Chen, Fatih Ertinaz, A. Fedoseev, L. Groer, Fei Mao, B. Mundim, M. Nolta, Jaime Pinto, M. Saldarriaga, Vladimir Slavnic, Erik Spence, Ching-Hsing Yu, W. Peltier","doi":"10.1145/3332186.3332195","DOIUrl":"https://doi.org/10.1145/3332186.3332195","url":null,"abstract":"Niagara is currently the fastest supercomputer accessible to academics in Canada. It was deployed at the beginning of 2018 and has been serving the research community ever since. This homogeneous 60,000-core cluster, owned by the University of Toronto and operated by SciNet, was intended to enable large parallel jobs and has a measured performance of 3.02 petaflops, debuting at #53 in the June 2018 TOP500 list. It was designed to optimize throughput of a range of scientific codes running at scale, energy efficiency, and network and storage performance and capacity. It replaced two systems that SciNet operated for over 8 years, the Tightly Coupled System (TCS) and the General Purpose Cluster (GPC) [13]. In this paper we describe the transition process from these two systems, the procurement and deployment processes, as well as the unique features that make Niagara a one-of-a-kind machine in Canada.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"34 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":"134585605","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}

引用次数: 117

MagmaDNN

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3333047

Daniel Nichols, Kwai Wong, Stanimire Tomov, Lucien Ng, Sihan Chen, Alexander Gessinger

引用次数: 8

Deep Learning Enabled Predicting Modeling of Mortality of Diabetes Mellitus Patients 基于深度学习的糖尿病患者死亡率预测模型

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3333262

Ian Wittler, Xinlian Liu, Aijuan Dong

{"title":"Deep Learning Enabled Predicting Modeling of Mortality of Diabetes Mellitus Patients","authors":"Ian Wittler, Xinlian Liu, Aijuan Dong","doi":"10.1145/3332186.3333262","DOIUrl":"https://doi.org/10.1145/3332186.3333262","url":null,"abstract":"Diabetes mellitus (DM) is a major public health concern that requires continuing medical care. It is also a leading cause of other serious health complications associated with longer hospital stays and increased mortality rates. Fluctuation of blood glucose levels are easy to monitor. Physicians manage patients' blood glucose to prevent or slow the progress of diabetes. In this paper, the MIMIC-III data set is used to develop and train multiple models that aim to predict the mortality of DM patients. Our deep learning model of convolutional neural network produced a 0.885 AUC score, above all baseline models we constructed, which include decision trees, random forests, and fully connected neural networks. The inputs for each model were comprised of admission type, age, Elixhauser comorbidity score, blood glucose measurements, and blood glucose range. The results obtained from these models are valuable for physicians, patients, and insurance companies. By analyzing the features that drive these models, care management for diabetic patients in an ICU setting can be improved resulting in lowered motality rate.","PeriodicalId":422540,"journal":{"name":"Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)","volume":"245 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":"133336573","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}

引用次数: 1

Virtual Reality Based Environment for Immersive, Hands-On Learning 虚拟现实为基础的沉浸式环境，动手学习

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) Pub Date : 2019-07-28 DOI: 10.1145/3332186.3338099

Atherton Mook

引用次数: 2