Percipient Storage for Exascale Data Centric Computing2

Risultati finali

Report on porting applications and application tools to Sage2 system

This deliverable reports the first experiences of porting use casestools to the Sage2 system It also reports on potential optimizations by leveraging Sage2 feature sets

Sage2 ecosystem Design Document

This report (from multiple tasks) will describe the design for all the Sage2 ecosystem components. This will get its inputs from Tasks 3.1 to 3.4

Best practices in porting and optimizing applications and application tools for Sage2 system

In this report we will document experience and best practices obtained while porting and optimising applications to and for the Sage2 system including performance assessments It aims to provide practical guidance for future users of the technologies produced within this project

Report on application and use case requirements

This deliverable provides an analysis of IO requirements of Sage2 use cases and tools together with a data retention plan for each workflow This deliverable also identifies changes in applications and tools for fully using the Sage2 system and also limitations that might provide obstacles to their implementation

System extrapolations to Exascale

This deliverable will discuss the system extrapolations of the features and benefits of Sage2 for the use cases at scale and quantify the performance improvements in IO time to solution and scalability if there were larger scale deployments representative of the Sage2 prototype Analysis of how the system might actually perform in relation to the originally anticipated performance goals will be provided This deliverable will gather inputs from the use cases in WP1

Pubblicazioni

StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs

Autori: Artur Podobas, Martin Svedin, Steven W. D. Chien, Ivy B. Peng, Naresh Balaji Ravichandran, Pawel Herman, Anders Lansner, and Stefano Markidis
Pubblicato in: 2021
Editore: ACM
DOI: 10.1145/3468044.3468052

Learning representations in Bayesian Confidence Propagation neural networks

Autori: Ravichandran, Naresh & Lansner, Anders & Herman, Pawel
Pubblicato in: Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART '21). Association for Computing Machinery, New York, NY, USA, Article 8, 1–6, 2020
Editore: IEEE
DOI: 10.1109/ijcnn48605.2020.9207061

HPC systems in the next decade - what to expect, when, where

Autori: Pleiter, Dirk
Pubblicato in: 24th International Conference on Computing in High Energy and Nuclear Physics, CHEP 2019, Adelaide, Australia, 2019-11-04 - 2019-11-08, Numero 1, 2019
Editore: CHEP
DOI: 10.5281/zenodo.3599755

Characterizing Deep-Learning I/O Workloads in TensorFlow

Autori: Steven W. D. Chien, Stefano Markidis, Chaitanya Prasad Sishtla, Luis Santos, Pawel Herman, Sai Narasimhamurthy, Erwin Laure
Pubblicato in: 2018 IEEE/ACM 3rd International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems (PDSW-DISCS), 2018, Pagina/e 54-63, ISBN 978-1-7281-0192-7
Editore: IEEE
DOI: 10.1109/PDSW-DISCS.2018.00011

Sage2 - Percipient StorAGe for Exascale Data Centric Computing

Autori: Witt, Shaun de; Samaddar, Debby; Narasimhamurthy, Sai; Umanesan, Ganesan; Pleiter, Dirk; El Sayed Mohamed, Salem; Davis, Andrew
Pubblicato in: 24th International Conference on Computing in High Energy and Nuclear Physics, CHEP 2019, Adelaide, Australia, 2019-11-04 - 2019-11-08, Numero 1, 2019
Editore: CHEP

Persistent coarrays - integrating MPI storage windows in coarray fortran

Autori: Sergio Rivas-Gomez, Alessandro Fanfarillo, Sai Narasimhamurthy, Stefano Markidis
Pubblicato in: Proceedings of the 26th European MPI Users' Group Meeting on - EuroMPI '19, 2019, Pagina/e 1-8, ISBN 9781-450371759
Editore: ACM Press
DOI: 10.1145/3343211.3343214

uMMAP-IO: User-Level Memory-Mapped I/O for HPC

Autori: Sergio Rivas-Gomez, Alessandro Fanfarillo, Sebastien Valat, Christophe Laferriere, Philippe Couvee, Sai Narasimhamurthy, Stefano Markidis
Pubblicato in: 2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC), 2019, Pagina/e 363-372, ISBN 978-1-7281-4535-8
Editore: IEEE
DOI: 10.1109/hipc.2019.00051

Software Wear Management for Persistent Memories

Autori: Vaibhav Gogte, University of Michigan; William Wang and Stephan Diestelhorst, ARM; Aasheesh Kolli, Pennsylvania State University and VMware Research; Peter M. Chen, Satish Narayanasamy, and Thomas F. Wenisch, University of Michigan
Pubblicato in: 17th USENIX Conference on File and Storage Technologies (FAST 19), 2019
Editore: USENIX Association

Brief Announcement - Persistent Atomics for Implementing Durable Lock-Free Data Structures for Non-Volatile Memory

Autori: William Wang, Stephan Diestelhorst
Pubblicato in: The 31st ACM on Symposium on Parallelism in Algorithms and Architectures - SPAA '19, 2019, Pagina/e 309-311, ISBN 9781-450361842
Editore: ACM Press
DOI: 10.1145/3323165.3323166

Relaxed Persist Ordering Using Strand Persistency

Autori: Vaibhav Gogte, William Wang, Stephan Diestelhorst, Peter M. Chen, Satish Narayanasamy, Thomas F. Wenisch
Pubblicato in: ISCA '20: Proceedings of the 47th International Symposium on Computer Architecture, 2020
Editore: Association for Computing Machinery

Predicting File Lifetimes with Machine Learning

Autori: Thomas Leibovici, Florent Monjalet
Pubblicato in: ISC High Performance 2019 International Workshops, 2019
Editore: Springer

In situ visualization of large-scale turbulence simulations in Nek5000 with ParaView Catalyst

Autori: Atzori, M., Köpp, W., Chien, S.W.D. et al
Pubblicato in: The Journal of Supercomputing, 2021
Editore: Springer
DOI: 10.1007/s11227-021-03990-3

NoaSci: A Numerical Object Array Library for I/O of Scientific Applications on Object Storage

Autori: Wei Der Chien, Artur Podobas, Martin Svedin, Andriy Tkachuk, Salem El Sayed, Pawel Herman, Ganesan Umanesan, Sai Narasimhamur thy and Stefano Markidis
Pubblicato in: PDP 2022: International Conference on Parallel, Distributed and Network- Based Processing, 2022
Editore: IEEE

Language Support for Memory Persistency

Autori: Aasheesh Kolli, Vaibhav Gogte, Ali Saidi, Stephan Diestelhorst, William Wang, Peter M. Chen, Satish Narayanasamy, Thomas F. Wenisch
Pubblicato in: IEEE Micro, Numero 39/3, 2019, Pagina/e 94-102, ISSN 0272-1732
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mm.2019.2910821

tf-Darshan: UnderstandingFine-grainedI/O Performance in MachineLearning Workloads

Autori: Chien, Steven & Podobas, Artur & Peng, Ivy & Markidis, Stefano
Pubblicato in: 2020
Editore: IEEE
DOI: 10.1109/cluster49012.2020.00046

Risultati finali

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