[Storage-research-list] GrAPL 2020 - Virtual Event - Call for
Participation
by Tumeo,
[Please accept our apologies for multiple postings.]
CALL FOR PARTICIPATION
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GrAPL 2020: Workshop on Graphs, Architectures, Programming, and Learning
https://hpc.pnl.gov/grapl/
May 18, 2020
8AM – 10AM PDT
IMPORTANT: This year, GrAPL will hold two LIVE 45 minute Q&A sessions with the authors of the accepted papers and invited talks according to the schedule below. Papers and static presentations for the entire conference including the GrAPL Workshop will be made available to all conference registrants by Friday May 15th. Register for free at the IPDPS website (http://www.ipdps.org) to get instructions on how to access to this content. In addition, links to 3-5 minute lightning talks by the workshop speakers will be found at the GrAPL website (https://hpc.pnl.gov/grapl/) by May 15th.
To attend the Zoom Sessions, we ask participants to register in advance at the following link: https://tinyurl.com/grapl2020
The organizing committee will then provide the link to the session.
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Program for May 18th:
0800 – 0845 (PDT): Session 1
Welcome message.
Algorithms and Applications
Kronecker Graph Generation with Ground Truth for 4-Cycles and Dense Structure in Bipartite Graphs
Trevor Steil (University of Minnesota), Scott McMillan (SEI, Carnegie Mellon University), Geoffrey Sanders (LLNL), Roger Pearce (LLNL), Benjamin Priest (LLNL)
A scalable graph generation algorithm to sample over a given shell distribution
M. Yusuf Özkaya (Georgia Institute of Technology), Muhammed Fatih Balin (Georgia Institute of Technology), Ali Pinar (SNL), Ümit V. Çatalyürek (Georgia Institute of Technology)
An incremental GraphBLAS solution for the 2018 TTC Social Media case study
Márton Elekes (Budapest University of Technology and Economics), Gábor Szárnyas (Budapest University of Technology and Economics)
Linear Algebraic Louvain Method in Python
Tze Meng Low (Carnegie Mellon University), Daniele Spampinato (Carnegie Mellon University), Scott McMillan (SEI, Carnegie Mellon University), Michel Pelletier (FPX, LLC)
0900 – 0945 (PDT): Session 2
Keynote - The GraphIt Universal Graph Framework: Achieving High-Performance across Algorithms, Graph Types and Architectures
Saman Amarasinghe (Massachusetts Institute of Technology)
API's and Implementations
Parallelizing Maximal Clique Enumeration on Modern Manycore Processors
Jovan Blanuša (IBM Research - Zürich, EPFL), Radu Stoica (IBM Research - Zürich), Paolo Ienne (EPFL), Kubilay Atasu (IBM Research - Zürich)
A Roadmap for the GraphBLAS C++ API
Benjamin A. Brock (UC Berkeley), Aydın Buluç (LBNL), Timothy G. Mattson (Intel), Scott McMillan (SEI, Carnegie Mellon University), José E. Moreira (IBM)
Considerations for a Distributed GraphBLAS API
Benjamin A. Brock (UC Berkeley), Aydın Buluç (LBNL), Timothy G. Mattson (Intel), Scott McMillan (SEI, Carnegie Mellon University), José E. Moreira (IBM), Roger Pearce (LLNL), Oguz Selvitopi (LBNL), Trevor Steil (University of Minnesota)
75,000,000,000 Streaming Inserts/Second Using Hierarchical Hypersparse GraphBLAS Matrices
Jeremy Kepner (MIT Lincoln Laboratory)
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GrAPL is the result of the combination of two IPDPS workshops:
GABB: Graph Algorithms Building Blocks
GraML: Workshop on The Intersection of Graph Algorithms and Machine Learning
SUMMARY
-------
Data analytics is one of the fastest growing segments of computer science. Many real-world analytic workloads are a mix of graph and machine learning methods. Graphs play an important role in the synthesis and analysis of relationships and organizational structures, furthering the ability of machine-learning methods to identify signature features. Given the difference in the parallel execution models of graph algorithms and machine learning methods, current tools, runtime systems, and architectures do not deliver consistently good performance across data analysis workflows. In this workshop we are interested in graphs, how their synthesis (representation) and analysis is supported in hardware and software, and the ways graph algorithms interact with machine learning. The workshop’s scope is broad and encompasses the wide range of methods used in large-scale data analytics workflows.
This workshop seeks papers on the theory, model-based analysis, simulation, and analysis of operational data for graph analytics and related machine learning applications. In particular, we are interested, but not limited to the following topics:
• Provide tractability and performance analysis in terms of complexity, time-to-solution, problem size, and quality of solution for systems that deal with mixed data analytics workflows;
• Discuss the problem domains and problems addressable with graph methods, machine learning methods, or both;
• Discuss programming models and associated frameworks such as Pregel, Galois, Boost, GraphBLAS, GraphChi, etc., for building large multi-attributed graphs;
• Discuss how frameworks for building graph algorithms interact with those for building machine learning algorithms;
• Discuss hardware platforms specialized for addressing large, dynamic, multi-attributed graphs and associated machine learning;
Besides regular papers, short papers (up to four pages) describing work-in-progress or incomplete but sound, innovative ideas related to the workshop theme are also encouraged.
ORGANIZATION
------------
General co-Chairs:
Scott McMillan (CMU SEI), smcmillan(a)sei.cmu.edu
Manoj Kumar (IBM), manoj1(a)us.ibm.com
Program Chairs:
Danai Koutra (University of Michigan, Ann Arbor), dkoutra(a)umich.edu
Mahantesh Halappanavar (PNNL), hala(a)pnnl.gov
GrAPL's Little Helpers:
Tim Mattson (Intel)
Antonino Tumeo (PNNL)
Program Committee:
Nesreen K Ahmed, Intel Research and Intel AI, USA
Sasikanth Avancha, Intel Labs - Parallel Computing Lab, India
Aydin Buluç, Lawrence Berkeley National Lab, USA
Timothy A. Davis, University of Florida, USA
Jana Doppa, Washington State University, USA
John Gilbert, University of California at Santa Barbara, USA
Sergio Gómez, Universitat Rovira i Virgili, Catalonia
Will Hamilton, McGill University, Mila, Canada
Stratis Ioannidis, Northeastern University, Boston, USA
Bharat Kaul, Intel Labs - Parallel Computing Labs, India
Kamesh Madduri, The Pennsylvania State University, USA
Henning Meyerhenke, Humboldt University of Berlin, Germany
Indranil Roy, Natural Intelligence, USA
Robert Rallo, Pacific Northwest National Lab, USA
P. Sadayappan, University of Utah, USA
Yizhou Sun, University of California, Los Angeles, USA
Flavio Vella, Free University of Bozen, Italy
Steering Committee:
David A. Bader (New Jersey Institute of Technology)
Aydın Buluç (LBNL)
John Feo (PNNL)
John Gilbert (UC Santa Barbara)
Tim Mattson (Intel)
Ananth Kalyanaraman (Washington State University)
Jeremy Kepner (MIT Lincoln Laboratory)
Antonino Tumeo (PNNL)
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[Storage-research-list] [New Deadline: May 30] Workshop on
Challenges and Opportunities of HPC Storage Systems (CHAOSS)
by Michael Kuhn
*UPDATE* Euro-Par 2020 has been converted to an all-virtual event. Our
workshop will follow this format and thus take place virtually. Please
note that the submission deadline has been updated accordingly.
# First International Workshop on
Challenges and Opportunities of HPC Storage Systems (CHAOSS)
The workshop is aimed at researchers, developers of scientific
applications, engineers and everyone interested in the evolution of HPC
storage systems. As the developments of computing power, storage and
network technologies continue to diverge, the performance gap between
them widens. This trend, combined with the growing data volumes,
results in I/O and storage bottlenecks that become increasingly serious
especially for large-scale HPC storage systems. The hierarchy of
different storage technologies to ease this situation leads to a
complex environment which will become even more challenging for future
exascale systems.
This workshop is a venue for papers exploring topics related to data
organization and management along with the impacts of multi-tier memory
and storage for optimizing application throughput. It will take place
at the Euro-Par 2020 conference in Warsaw, Poland on either August 24
or 25, 2020. More information is available at:
https://wr.informatik.uni-hamburg.de/events/2020/chaoss
## Important Dates
Paper Submission: May 30, 2020 (*UPDATED*)
Notification to Authors: July 3, 2020 (*UPDATED*)
Registration: July 10, 2020
Workshop Dates: August 24 or 25, 2020
Camera-Ready Deadline: September 11, 2020
## Submission Guidelines
Submissions will be submitted and managed via EasyChair at:
https://easychair.org/conferences/?conf=europar2020workshop
Papers should not exceed 12 pages (including title, text, figures,
appendices and references). Papers of less than 10 pages will be
considered as short papers that can be presented at the conference but
will not be published in the proceedings. Papers must be formatted
according to Springer's LNCS guidelines available at
https://www.springer.com/gp/computer-science/lncs/conference-proceedings-...
. Accepted papers will be published in a separate LNCS workshop volume
after the conference.
One author of each accepted paper is required to register for the
workshop and present the paper. Due to the virtual format,
presentations have to be given either via pre-recorded video or live
stream. In both cases, the organizers will collect questions during the
workshop and perform a live Q&A session with the presenter.
## Topics of Interest
Submissions may be more hands-on than research papers and we therefore
explicitly encourage submissions in the early stages of research.
Topics of interest include, but are not limited to:
- Kernel and user space file/storage systems
- Parallel and distributed file/storage systems
- Data management approaches for heterogeneous storage systems
- Management of self-describing data formats
- Metadata management
- Approaches using query and database interfaces
- Hybrid solutions using file systems and databases
- Optimized indexing techniques
- Data organizations to support online workflows
- Domain-specific data management solutions
- Related experiences from users: what worked, what didn't?
## Program Committee
- Gabriel Antoniu (INRIA)
- Konstantinos Chasapis (DDN)
- Andreas Dilger (DDN)
- Kira Duwe (UHH)
- Wolfgang Frings (JSC)
- Elsa Gonsiororowski (LLNL)
- Anthony Kougkas (IIT)
- Michael Kuhn (UHH)
- Margaret Lawson (UIUC, SNL)
- Jay Lofstead (SNL)
- Johann Lombardi (Intel)
- Jakob Lüttgau (DKRZ)
- Anna Queralt (BSC)
- Yue Zhu (FSU)
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