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Percipient Storage for Exascale Data Centric Computing2


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


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

Author(s): Artur Podobas, Martin Svedin, Steven W. D. Chien, Ivy B. Peng, Naresh Balaji Ravichandran, Pawel Herman, Anders Lansner, and Stefano Markidis
Published in: 2021
Publisher: ACM
DOI: 10.1145/3468044.3468052

Learning representations in Bayesian Confidence Propagation neural networks

Author(s): Ravichandran, Naresh & Lansner, Anders & Herman, Pawel
Published 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
Publisher: IEEE
DOI: 10.1109/ijcnn48605.2020.9207061

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

Author(s): Pleiter, Dirk
Published in: 24th International Conference on Computing in High Energy and Nuclear Physics, CHEP 2019, Adelaide, Australia, 2019-11-04 - 2019-11-08, Issue 1, 2019
Publisher: CHEP
DOI: 10.5281/zenodo.3599755

Characterizing Deep-Learning I/O Workloads in TensorFlow

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

Sage2 - Percipient StorAGe for Exascale Data Centric Computing

Author(s): Witt, Shaun de; Samaddar, Debby; Narasimhamurthy, Sai; Umanesan, Ganesan; Pleiter, Dirk; El Sayed Mohamed, Salem; Davis, Andrew
Published in: 24th International Conference on Computing in High Energy and Nuclear Physics, CHEP 2019, Adelaide, Australia, 2019-11-04 - 2019-11-08, Issue 1, 2019
Publisher: CHEP

Persistent coarrays - integrating MPI storage windows in coarray fortran

Author(s): Sergio Rivas-Gomez, Alessandro Fanfarillo, Sai Narasimhamurthy, Stefano Markidis
Published in: Proceedings of the 26th European MPI Users' Group Meeting on - EuroMPI '19, 2019, Page(s) 1-8, ISBN 9781-450371759
Publisher: ACM Press
DOI: 10.1145/3343211.3343214

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

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

Software Wear Management for Persistent Memories

Author(s): 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
Published in: 17th USENIX Conference on File and Storage Technologies (FAST 19), 2019
Publisher: USENIX Association

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

Author(s): William Wang, Stephan Diestelhorst
Published in: The 31st ACM on Symposium on Parallelism in Algorithms and Architectures - SPAA '19, 2019, Page(s) 309-311, ISBN 9781-450361842
Publisher: ACM Press
DOI: 10.1145/3323165.3323166

Relaxed Persist Ordering Using Strand Persistency

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

Predicting File Lifetimes with Machine Learning

Author(s): Thomas Leibovici, Florent Monjalet
Published in: ISC High Performance 2019 International Workshops, 2019
Publisher: Springer

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

Author(s): Atzori, M., Köpp, W., Chien, S.W.D. et al
Published in: The Journal of Supercomputing, 2021
Publisher: Springer
DOI: 10.1007/s11227-021-03990-3

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

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

Language Support for Memory Persistency

Author(s): Aasheesh Kolli, Vaibhav Gogte, Ali Saidi, Stephan Diestelhorst, William Wang, Peter M. Chen, Satish Narayanasamy, Thomas F. Wenisch
Published in: IEEE Micro, Issue 39/3, 2019, Page(s) 94-102, ISSN 0272-1732
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mm.2019.2910821

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

Author(s): Chien, Steven & Podobas, Artur & Peng, Ivy & Markidis, Stefano
Published in: 2020
Publisher: IEEE
DOI: 10.1109/cluster49012.2020.00046

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