[hpc-announce] GrAPL Workshop at IPDPS 2024 - Call for Participation

[Jannesari, Ali [COM S]]

Call for Participation for the IPDPS Workshop on Graphs, Architectures, Programming, and Learning (GrAPL) on Monday, May 27th, 2024.

GrAPL is held in conjunction with IEEE IPDPS 2024, San Francisco, CA, USA. 

GrAPL website: https://urldefense.us/v3/__https://hpc.pnl.gov/grapl/__;!!G_uCfscf7eWS!cvW3omTT8lx9FL9x37ToRo5BRZcbH7KuoyJzSy5kkuTpKZFC_sHOhOtcQ60GkngzhsYuuSqYEgjqvBTxsPo0Xn8hX-4$ 
Register for the workshop at: https://urldefense.us/v3/__https://web.cvent.com/event/12a71674-f7c3-402e-917e-2d8b268feee6/summary?locale=en__;!!G_uCfscf7eWS!cvW3omTT8lx9FL9x37ToRo5BRZcbH7KuoyJzSy5kkuTpKZFC_sHOhOtcQ60GkngzhsYuuSqYEgjqvBTxsPo0txLZMYE$ 

PROGRAM

- Welcome and Introduction
- Keynote Tarek Abdelzaher (University of Chicago, Urbana Champaign)
- Papers:
	- Teaching Network Traffic Matrices in an Interactive Game Environment
	- Characterizing the Performance of Emerging Deep Learning, Graph, and High Performance Computing Workloads Under Interference (short paper)
	-The GraphBLAS 3.0 Project  (short paper)
	- Edge-Parallel Graph Encoder Embedding  (short paper)
	- Multi-Level GNN Preconditioner for Solving Large Scale Problems
	- STGraph: A Framework for Temporal Graph Neural Networks
	- GraphBinMatch: Graph-based Similarity Learning for Cross-Language Binary and Source Code Matching
	- GraphBLAS.jl v0.1: An Update on GraphBLAS in Julia (short paper)
	- ECG: Expressing Locality and Prefetching for Optimal Caching in Graph Structures (short paper)
	- Unlocking the Potential: Performance and Portability of Graph Algorithms on Kokkos Framework
	- Shared-Memory Parallel Edmonds Blossom Algorithm for Maximum Cardinality Matching in General Graphs
- Panel Discussion (TBD)
- Concluding Remarks

We hope that you will also encourage your colleagues, members of research groups, and other scientists to attend, contribute, and participate in this workshop. 

Giulia Guidi and Ali Jannesari 
GrAPL 2024 Program Co-Chairs 
gguidi at cornell.edu 
jannesar at iastate.edu 

*** 

IPDPS Workshop on Graphs, Architectures, Programming, and Learning (GrAPL) 2024, May 27, San Francisco, CA, USA. Data analytics is one of the fastest-growing segments of computer science. Many real-world analytic workloads combine 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 are supported in hardware and software, and the ways graph algorithms interact with machine learning. The workshop’s scope is broad and encompasses a wide range of methods used in large-scale data analytics workflows.