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GrAPL 2022: Workshop on Graphs, Architectures, Programming, and Learning

Virtual

30 May 2022

Scope and Goals:

GrAPL is the result of the combination of two IPDPS workshops

• GABB: Graph Algorithms Building Block
• GraML: Workshop on The Intersection of Graph Algorithms and Machine Learning

Data analytics is one of the fastest growing segments of computer science. Much of the recent focus in Data Analytics has emphasized machine learning. This is understandable given the success of deep learning over the last decade. However, 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 which is a natural outgrowth of 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. We are particularly interested in papers that:

• Provide tractability 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.

Location:

This workshop is co-located with IPDPS 2022, held 30 May - 3 June 2022, virtual. Registration information for IPDPS2022 can be found at here.

Program:

Details and Dates

Due to perduring travel limitations, IPDPS 2022 will still be held as a virtual conference.

The GrAPL organizing committee has planned an exciting online program, consisting in three LIVE sessions on May 30 (starting at 7:30 AM PDT, 2:30 PM UTC, 4:30 PM CET) with presentations Q/A for each accepted paper and the keynote. We provide below the tentative schedule.

Please note that IPDPS will be provide the platform for virtual conferencing, and attendees will need to be registered to IPDPS to attend also the workshops, access the papers, and (optional) presentation materials that speakers would like to share before the live event.

To attend GrAPL, please register at: http://www.ipdps.org

Workshop Organizers:

General Co-Chairs:

• Scott McMillan (CMU SEI)
• Manoj Kumar (IBM)

Local Chair:

• Gabor Szarnyas (Centrum Wiskunde & Informatica)

Program Chair:

• Nesreen Ahmed (Intel)

GrAPL's Little Helpers:

• Tim Mattson (Intel)
• Antonino Tumeo (PNNL)

Technical Program committee members (in addition to the chair):

• Paul Bogdan, University of Southern California , US
• Anu Bourgeois, Georgia State University , US
• Aydin Buluç, Lawrence Berkeley National Laboratory; University of California, Berkeley, US
• Timothy Davis, Texas A&M University, US
• John Gilbert, University of California, Santa Barbara, US
• Sergio Gomez, Universitat Rovira i Virgili , ES
• Kamesh Madduri, Pennsylvania State University , US
• Hesham Mostafa, Intel Labs, US
• Roger Pearce, Lawrence Livermore National Laboratory, US
• Indranil Roy, Natural Intelligence Systems, Inc. , US
• Ponnuswamy Sadayappan, University of Utah, US
• Shaden Smith, Microsoft Corporation, US
• Trevor Steil, Lawrence Livermore National Laboratory, US
• Yizhou Sun, University of California, Los Angeles, US
• Ramachandran Vaidyanathan, Louisiana State University , US
• Alexander van der Grinten, Humboldt-University of Berlin , DE
• Ana Lucia Varbanescu, University of Amsterdam, NL
• Flavio Vella, Free University of Bozen, IT

Steering committee:

• David A. Bader (New Jersey Institute of Technology)
• Aydın Buluç (LBNL)
• John Feo (PNNL)
• John Gilbert (UC Santa Barbara)
• Mahantesh Halappanavar (PNNL)
• Tim Mattson (Intel)
• Ananth Kalyanaraman (Washington State University)
• Jeremy Kepner (MIT Lincoln Labs)
• Danai Koutra (University of Michigan)
• Antonino Tumeo (PNNL)