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Matthieu Dorier, Gabriel Antoniu, Franck Cappello, Marc Snir, Robert Sisneros, Orcun Yildiz, Shadi Ibrahim, Tom Peterka, Leigh Orf
2016 ACM Transactions on Parallel Computing  
With exascale computing on the horizon, reducing performance variability in data management tasks (storage, visualization, analysis, etc.) is becoming a key challenge in sustaining high performance.  ...  In this paper, we present Damaris, a system that leverages dedicated cores in multicore nodes to offload data management tasks, including I/O, data compression, scheduling of data movements, in situ analysis  ...  Applications (Urbana-Champaign, USA) and Argonne National Laboratory, within the Joint Inria-UIUC-ANL-BSC-JSC Laboratory for Extreme-Scale Computing (JLESC), formerly Joint Laboratory for Petascale Computing  ... 
doi:10.1145/2987371 fatcat:rc4h6ec6hrehzo7mc5bwkg7uli

Damaris: How to Efficiently Leverage Multicore Parallelism to Achieve Scalable, Jitter-free I/O

Matthieu Dorier, Gabriel Antoniu, Franck Cappello, Marc Snir, Leigh Orf
2012 2012 IEEE International Conference on Cluster Computing  
data processing and I/O in order to hide this variability.  ...  With exascale computing on the horizon, the performance variability of I/O systems represents a key challenge in sustaining high performance.  ...  Laboratory for Petascale Computing.  ... 
doi:10.1109/cluster.2012.26 dblp:conf/cluster/DorierACSO12 fatcat:m5n3qnb4irai5l24ldjzvxtdgi

Efficient I/O using Dedicated Cores in Large-Scale HPC Simulations

Matthieu Dorier
2013 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum  
DEVELOPMENT DIRECTIONS As mentioned in the introduction, improving I/O at the simulation level partially addresses the "Big Data" challenge raised by post-petascale machines.  ...  We also present current work and future directions leveraging Damaris for in-situ data analysis, thus addressing several aspects of this "Big Data" challenge. II.  ... 
doi:10.1109/ipdpsw.2013.101 dblp:conf/ipps/Dorier13 fatcat:zuyqjkg425duvdfypoyndsn3ku

On the energy footprint of I/O management in Exascale HPC systems

Matthieu Dorier, Orcun Yildiz, Shadi Ibrahim, Anne-Cécile Orgerie, Gabriel Antoniu
2016 Future generations computer systems  
With I/O management acquiring a crucial role in supporting scientific simulations, various I/O management approaches have been proposed to achieve high performance and scalability.  ...  To the best of our knowledge, our work provides the first in-depth look into the energy-performance tradeoffs of I/O management approaches .  ...  The Damaris Middleware Damaris [16] is a middleware for efficient data management in large-scale HPC simulations.  ... 
doi:10.1016/j.future.2016.03.002 fatcat:qgzcoxpja5hkffbuxwzadr3sxa

The Landscape of Exascale Research

Stijn Heldens, Pieter Hijma, Ben Van Werkhoven, Jason Maassen, Adam S. Z. Belloum, Rob V. Van Nieuwpoort
2020 ACM Computing Surveys  
However, as we look forward, we still foresee two major concerns: the lack of suitable programming tools and the growing gap between processor performance and data bandwidth (i.e., memory, storage, networks  ...  We use a three-stage approach in which we (1) discuss various exascale landmark studies, (2) use data-driven techniques to analyze the large collection of related literature, and (3) discuss eight research  ...  situ, analysis, post, processing, data, simulation, interactions, image, disk, extract, savings, rendering, explore, tools, damaris, scientists, raw, parallel, traditional, P model, prediction, climate  ... 
doi:10.1145/3372390 fatcat:jhtwt7pxd5c5darhz75hiqgsnq

Dagstuhl Reports, Volume 8, Issue 7, July 2018, Complete Issue [article]

A performance model that provides an a priori answer for the cost of using an in situ approach for a given task would assist in managing the trade-offs between simulation and visualization resources.  ...  Performance analysis tools such as Vampir and Scalasca have handled execution traces (performance data) in an in situ manner for large scale simulation runs.  ...  Experiments show the methods perform better than others for the problem. We study the problem of learning similarity functions over very large corpora using neural network embedding models.  ... 
doi:10.4230/dagrep.8.7 fatcat:3ijkweopore5demqz5y4ja6a3e