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UID:submissions.supercomputing.org_SC21_sess342@linklings.com
SUMMARY:WORKS21: 16th Workshop on Workflows in Support of Large-Scale Scie
 nce
DESCRIPTION:Workshop\n\nVisDict: Enhancing the Communication between Workf
 low Providers and User Communities via a Visual Dictionary\n\nGesing, Ferr
 eira da Silva, Deelman, Hildreth, McDowell...\n\nWorkflows have proved to 
 be an excellent medium for representing scientific methods and for enhanci
 ng the efficiency and reproducibility of computational tasks. There is a s
 trong need for close collaboration and intensive communication with domain
  scientists to successfully translate high-impact sci...\n\n--------------
 -------\nNot All Tasks Are Created Equal: Adaptive Resource Allocation for
  Heterogeneous Tasks in Dynamic Workflows\n\nPhung, Thain, Chard, Ward\n\n
 Users running dynamic workflows in distributed systems usually have inadeq
 uate expertise to correctly size the allocation of resources (cores, memor
 y, disk) to each task due to the difficulty in uncovering the obscure yet 
 important correlation between tasks and their resource consumption. Thus, 
 user...\n\n---------------------\nWORKS21:  Q&A Session 3\n\n\n\n---------
 ------------\nA Recommender System for Scientific Datasets and Analysis Pi
 pelines\n\nMazaheri, Kiar, Glatard\n\nScientific datasets and analysis pip
 elines are increasingly being shared publicly in the interest of open scie
 nce. However, mechanisms are lacking to reliably identify which pipelines 
 and datasets can appropriately be used together. Given the increasing numb
 er of high-quality public datasets and pip...\n\n---------------------\nWO
 RKS21:  Q&A Session 2\n\n\n\n---------------------\nWORKS21: 16th Workshop
  on Workflows in Support of Large-Scale Science\n\nFerreira da Silva, Filg
 ueira\n\nScientific workflows have been used across scientific domains and
  have underpinned some of the most significant discoveries of the past sev
 eral decades. Workflow systems provide abstraction and automation which en
 able a broad range of researchers to easily define sophisticated computati
 onal processe...\n\n---------------------\nLearning Fundamental Workflow C
 oncepts with EduWRENCH\n\nCasanova, Tanaka, Koch, Ferreira da Silva\n\n---
 ------------------\nWORKS21:  Morning Break (10-10:30)\n\n\n\n------------
 ---------\nIntelligent Resource Provisioning for Scientific Workflows and 
 HPC\n\nShealy, Feltus, Smith\n\nScientific workflows and high-performance 
 computing (HPC) systems are critically important to modern scientific rese
 arch. In order to perform scientific experiments at scale, domain scientis
 ts must have knowledge and expertise in software and hardware systems that
  are highly complex and rapidly evol...\n\n---------------------\nDynamic 
 Heterogeneous Task Specification and Execution for In Situ Workflows\n\nYi
 ldiz, Morozov, Nicolae, Peterka\n\nToday’s science campaigns consist of mu
 ltiple tasks with wide-ranging data and computing requirements, and rarely
  are all the required capabilities found in current in situ workflow syste
 ms. In this work, we explore providing increased capabilities for scientif
 ic computing by bringing new capabiliti...\n\n---------------------\nWORKS
 21:  Afternoon Break (3-3:30)\n\n\n\n---------------------\nScience Capsul
 e: Toward Sharing and Reproducibility of Scientific Workflows\n\nGhoshal, 
 Bianchi, Essiari, Paine, Poon...\n\nWorkflows are increasingly processing 
 large volumes of data from scientific instruments, experiments and sensors
 . These workflows often consist of complex data processing and analysis st
 eps that might involve human in the loop, and use a diverse set of analysi
 s tools. Sharing and reproducing these w...\n\n---------------------\nWORK
 S21:  Lunch Break (12:30-2)\n\n\n\n---------------------\nA Lightweight GP
 U Monitoring Extension for Pegasus Kickstart\n\nPapadimitriou, Deelman\n\n
 This presentation presents a lightweight tool to capture monitoring inform
 ation from Nvidia GPUs. The tool is an extension of the Pegasus Kickstart 
 wrapper designed for monitoring CPU-based workflow jobs.\n\n--------------
 -------\nWORKS21:  Q&A Session 4\n\n\n\n---------------------\nA Performan
 ce Characterization of Scientific Machine Learning Workflows\n\nKrawczuk, 
 Papadimitriou, Tanaka, Do, Subramanya...\n\nScientific workflows are one o
 f the well-established pillars of modern large-scale computational science
 . More recently, scientists have started to leverage machine learning (ML)
  capabilities in their workflows, leading to a new category of scientific 
 workflows, denoted as scientific ML workflows. M...\n\n-------------------
 --\nWORKS21:  Invited Talk – FAIR Computational Workflows\n\nGoble\n\nThe 
 FAIR principles (Findable, Accessible, Interoperable, Reusable) have laid 
 a foundation for sharing and publishing digital assets, starting with data
  and now extending to all digital objects including software. The use of c
 omputational workflows has accelerated in the past few years driven by the
 ...\n\n---------------------\nExaWorks: Workflows for Exascale\n\nAl-Saadi
 , Ahn, Babuji, Chard, Corbett...\n\nExascale computers will offer transfor
 mative capabilities to combine data-driven and learning-based approaches w
 ith traditional simulation applications to accelerate scientific discovery
  and insight. These software combinations and integrations, however, are d
 ifficult to achieve due to challenges of...\n\n---------------------\nAn A
 daptive Elasticity Policy For Staging Based In-Situ Processing\n\nWang, Do
 rier, Subedi, E. Davis, Parashar\n\nIn-situ processing alleviates the gap 
 between computation and I/O capabilities by performing data analysis close
  to the data source. With simulation data varying in size and content duri
 ng workflow execution, it becomes necessary for in-situ processing to supp
 ort resource elasticity, i.e., the abili...\n\n---------------------\nWORK
 S21:  Q&A Session 1\n\n\n\n---------------------\nWORKS21:  Q&A Session 5\
 n\n\n\n---------------------\nThe Benefits of Prefetching for Large-Scale 
 Cloud-Based Neuroimaging Analysis Workflows\n\nHayot-Sasson, Glatard, Roke
 m\n\nTo support the growing demands of neuroscience applications, research
 ers are transitioning to cloud computing for its scalable, robust and elas
 tic infrastructure. Nevertheless, large datasets residing in object stores
  may result in significant data transfer overheads during workflow executi
 on. Prefe...\n\n---------------------\nCoordinating Dynamic Ensemble Calcu
 lations with libEnsemble\n\nHudson, Navarro, Larson, Wild\n\nlibEnsemble i
 s a Python library to coordinate the concurrent evaluation of dynamic ense
 mbles of calculations. The library is developed to use massively parallel 
 resources to accelerate the solution of design, decision, and inference pr
 oblems and to expand the class of problems that can benefit from ...\n\n--
 -------------------\nWORKS21:  Welcome\n\nFilgueira, Ferreira da Silva\n\n
 \nTag: Online Only, Cloud and Distributed Computing, Scientific Computing,
  Workflows\n\nRegistration Category: Workshop Reg Pass
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