2016 Proceeding- General Chairs:
- Toyotaro Suzumura,
- Dario Garcia-Gasulla,
- Program Chair:
- Miyuru Dayarathna
It is our great pleasure to welcome you to the 2016 High Performance Graph Processing Workshop -- HPGP'16. This inaugural workshop of the HPGP workshop series is focused on multiple aspects of graph processing on high performance computing systems. The mission of the workshop is to serve as a platform for dissemination of cutting edge research conducted on high performance graph processing and identify new directions for future research and development. HPGP'16 provides researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of high performance graph processing.
The call for papers attracted submissions mainly from Asia and the United States.
We also encourage attendees to attend the keynote talk. This valuable and insightful talk can and will guide us to a better understanding of the future of high performance graph processing:
Towards next-generation graph processing and management platform, Toyotaro Suzumura (who is currently at IBM T.J. Watson Research Center).
Proceeding Downloads
A Comparative Study on Exact Triangle Counting Algorithms on the GPU
We implement exact triangle counting in graphs on the GPU using three different methodologies: subgraph matching to a triangle pattern; programmable graph analytics, with a set-intersection approach; and a matrix formulation based on sparse matrix-...
Parallel Shortest-Path Queries in Planar Graphs
We develop several parallel algorithms for shortest distance queries in planar graphs that use graph partitioning in the preprocessing phase to precompute and store distances between selected pairs of vertices. In the query phase, given a pair of ...
Towards Next-Generation Graph Processing and Management Platform
Applications which need to process and manage large graph data sets have imposed significant challenges for data science community in recent times. This talk discusses the key challenges which need to be handled when implementing a next-generation graph ...
NUMA-aware Scalable Graph Traversal on SGI UV Systems
Breadth-first search (BFS) is one of the most fundamental processing algorithms in graph theory. We previously presented a scalable BFS algorithm based on Beamer's direction-optimizing algorithm for non-uniform memory access (NUMA)-based systems, in ...
Graph Topology Abstraction for Distributed Path Queries
Querying graph data often involves identifying matching paths, either as an end product, or as an intermediate step for further graph analysis. Distributed graph querying, suffers from high communication to computation costs, due to challenges in ...
Betweenness Centrality in an HSA-enabled System
This paper studies different approaches to implementing betweenness centrality in a heterogeneous system. Betweenness centrality is an important algorithm in graph processing. It presents multiple levels of parallelism when processing a graph, and is an ...
BLADYG: A Novel Block-Centric Framework for the Analysis of Large Dynamic Graphs
Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing ...
Distributed Incremental Pattern Matching on Streaming Graphs
Big data has shifted the computing paradigm of data analysis. While some of the data can be treated as simple texts or independent data records, many other applications have data with structural patterns which are modeled as a graph, such as social ...
Index Terms
- Proceedings of the ACM Workshop on High Performance Graph Processing
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| HPGP '16 | 6 | 5 | 83% |
| Overall | 6 | 5 | 83% |