2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)最新文献

Supporting Multi-messenger Astrophysics with Fast Gamma-ray Burst Localization 支持多信使天体物理学与快速伽马射线暴定位

2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC) Pub Date : 2021-11-01 DOI: 10.1109/UrgentHPC54802.2021.00008

Jacob Wheelock, William Kanu, M. Sudvarg, Zhili Xiao, J. Buhler, R. Chamberlain, J. Buckley

引用次数: 3

Utilising urgent computing to tackle the spread of mosquito-borne diseases 利用紧急计算解决蚊媒疾病的传播

2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC) Pub Date : 2021-11-01 DOI: 10.1109/UrgentHPC54802.2021.00010

Nick Brown, R. Nash, P. Poletti, G. Guzzetta, M. Manica, A. Zardini, M. Flatken, Jules Vidal, Charles Gueunet, E. Belikov, Julien Tierny, Artur Podobas, W. Chien, S. Markidis, A. Gerndt

{"title":"Utilising urgent computing to tackle the spread of mosquito-borne diseases","authors":"Nick Brown, R. Nash, P. Poletti, G. Guzzetta, M. Manica, A. Zardini, M. Flatken, Jules Vidal, Charles Gueunet, E. Belikov, Julien Tierny, Artur Podobas, W. Chien, S. Markidis, A. Gerndt","doi":"10.1109/UrgentHPC54802.2021.00010","DOIUrl":"https://doi.org/10.1109/UrgentHPC54802.2021.00010","url":null,"abstract":"It is estimated that around 80% of the world’s population live in areas susceptible to at-least one major vector borne disease, and approximately 20% of global communicable diseases are spread by mosquitoes. Furthermore, the outbreaks of such diseases are becoming more common and widespread, with much of this driven in recent years by socio-demographic and climatic factors. These trends are causing significant worry to global health organisations, including the CDC and WHO, and-so an important question is the role that technology can play in addressing them. In this work we describe the integration of an epidemiology model, which simulates the spread of mosquito-borne diseases, with the VESTEC urgent computing ecosystem. The intention of this work is to empower human health professionals to exploit this model and more easily explore the progression of mosquito-borne diseases. Traditionally in the domain of the few research scientists, by leveraging state of the art visualisation and analytics techniques, all supported by running the computational workloads on HPC machines in a seamless fashion, we demonstrate the significant advantages that such an integration can provide. Furthermore we demonstrate the benefits of using an ecosystem such as VESTEC, which provides a framework for urgent computing, in supporting the easy adoption of these technologies by the epidemiologists and disaster response professionals more widely.","PeriodicalId":360682,"journal":{"name":"2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)","volume":"2495 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127479908","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: 7

Real-Time COVID-19 Infection Risk Assessment and Mitigation based on Public-Domain Data 基于公共领域数据的实时COVID-19感染风险评估与缓解

2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC) Pub Date : 2021-11-01 DOI: 10.1109/UrgentHPC54802.2021.00009

A. Cheng

{"title":"Real-Time COVID-19 Infection Risk Assessment and Mitigation based on Public-Domain Data","authors":"A. Cheng","doi":"10.1109/UrgentHPC54802.2021.00009","DOIUrl":"https://doi.org/10.1109/UrgentHPC54802.2021.00009","url":null,"abstract":"A number of models have been developed to predict the spreads of the COVID-19 pandemic and how non-pharmaceutical interventions (NPIs) such as social distancing, facial coverings, and business and school closures can contain this pandemic. Evolutionary artificial intelligence (AI) approaches have recently been proposed to automatically determine the most effective interventions by generating a large number of candidate strategies customized for different countries and locales and evaluating them with predictive models. These epidemiological models and advanced AI techniques assist policy makers by providing them with strategies in balancing the need to contain the pandemic and the need to minimize their economic impact as well as educating the general public about ways to reduce the chance of infection. However, they do not advise an individual citizen at a specific moment and location on taking the best course of actions to accomplish a task such as grocery shopping while minimizing infection.Therefore, this paper describes a new project aiming to develop a mobile-phone-deployable, real-time COVID-19 infection risk assessment and mitigation (RT-CIRAM) system which analyzes up-to-date data from multiple open sources leveraging urgent HPC/cloud computing, coupled with time-critical scheduling and routing techniques. Implementation of a RT-CIRAM prototype is underway, and it will be made available to the public. Facing the increasing spread of the more contagious Delta (B.1.617.2) and Delta Plus (AY.4.2) variants, this personal system will be especially useful for individual citizen to reduce her/his infection risk despite increasing vaccination rates while contributing to containing the spread of the current and future pandemics.","PeriodicalId":360682,"journal":{"name":"2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133576867","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

Evaluating policy-driven adaptation on the Edge-to-Cloud Continuum 评估边缘到云连续体上的策略驱动适应

2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC) Pub Date : 2021-11-01 DOI: 10.1109/UrgentHPC54802.2021.00007

Daniel Balouek-Thomert, I. Rodero, M. Parashar

{"title":"Evaluating policy-driven adaptation on the Edge-to-Cloud Continuum","authors":"Daniel Balouek-Thomert, I. Rodero, M. Parashar","doi":"10.1109/UrgentHPC54802.2021.00007","DOIUrl":"https://doi.org/10.1109/UrgentHPC54802.2021.00007","url":null,"abstract":"Developing data-driven applications requires developers and service providers to orchestrate data-to-discovery pipelines across distributed data sources and computing units. Realizing such pipelines poses two major challenges: programming analytics that reacts at runtime to unforeseen events, and adaptation of the resources and computing paths between the edge and the cloud. While these concerns are interdependent, they must be separated during the design process of the application and the deployment operations of the infrastructure. This work proposes a system stack for the adaptation of distributed analytics across the computing continuum. We implemented this software stack to evaluate its ability to continually balance the computation or data movement’s cost with the value of operations to the application objectives. Using a disaster response application, we observe that the system can select appropriate configurations while managing trade-offs between user-defined constraints, quality of results, and resource utilization. The evaluation shows that our model is able to adapt to variations in the data input size, bandwidth, and CPU capacities with minimal deadline violations (close to 10%). This constitutes encouraging results to benefit and facilitate the creation of ad-hoc computing paths for urgent science and time-critical decision-making.","PeriodicalId":360682,"journal":{"name":"2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130661437","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: 3

Experiences with Cross-Facility Real-Time Light Source Data Analysis Workflows 具有跨设施实时光源数据分析工作流程的经验

2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC) Pub Date : 2021-11-01 DOI: 10.1109/UrgentHPC54802.2021.00011

Anna Giannakou, Johannes P. Blaschke, Deborah Bard, L. Ramakrishnan

引用次数: 9

[Copyright notice] (版权)

2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC) Pub Date : 2021-11-01 DOI: 10.1109/urgenthpc54802.2021.00002

引用次数: 0