A Shared-Memory Parallel Algorithm for Updating Single-Source Shortest Paths in Large Dynamic Networks
- Computer Science2018 IEEE 25th International Conference on High Performance Computing (HiPC)
This work presents a novel two-step shared-memory algorithm for updating SSSP on large dynamic graphs, i.e. graphs whose structure evolves with time, and is one of the first practical parallel algorithms for updating networks on shared- memory systems that is also scalable to large networks.
The Need for Speed of AI Applications: Performance Comparison of Native vs. Browser-based Algorithm Implementations
- Computer ScienceArXiv
A Parallel Fully Dynamic Iterative Bio-Inspired Shortest Path Algorithm
- Computer Science
A fully dynamic bio-inspired parallel algorithm for solving the shortest path problem on dynamically changing graphs based on Physarum Solver, which is an amoeba shortest path algorithm that computes effectively dynamic shortest path even if percentage of changing edges is large.
Single-Source Shortest Path Tree for Big Dynamic Graphs
- Computer Science2018 IEEE International Conference on Big Data (Big Data)
This paper proposes a novel distributed computing approach, SSSPIncJoint, to update SSSP on big dynamic graphs using GraphX, and considerably speeds up the recomputation of the SSSP tree by reducing the number of map-reduce operations required for implementing SSSP in the gather-apply- scatter programming model used by GraphX.
SHOWING 1-10 OF 12 REFERENCES
- Computer ScienceGPGPU@ASPLOS
Accelerating Large Graph Algorithms on the GPU Using CUDA
- Computer ScienceHiPC
This work presents a few fundamental algorithms - including breadth first search, single source shortest path, and all-pairs shortest path - using CUDA on large graphs using the G80 line of Nvidia GPUs.
- Computer ScienceOOPSLA
Designing Multithreaded Algorithms for Breadth-First Search and st-connectivity on the Cray MTA-2
- Computer Science2006 International Conference on Parallel Processing (ICPP'06)
This paper presents fast parallel implementations of three fundamental graph theory problems, breadth-first search, st-connectivity and shortest paths for unweighted graphs, on multithreaded architectures such as the Cray MTA-2, and reports impressive results, both for algorithm execution time and parallel performance.
A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L
- Computer ScienceACM/IEEE SC 2005 Conference (SC'05)
This paper presents a distributed breadth- first search (BFS) scheme that scales for random graphs with up to three billion vertices and 30 billion edges, and develops efficient collective communication functions for the 3D torus architecture of BlueGene/L that take advantage of the structure in the problem.
Chronos: a graph engine for temporal graph analysis
- Computer ScienceEuroSys '14
The result is a high-performance temporal-graph system that offers up to an order of magnitude speedup for temporal iterative graph mining compared to a straightforward application of existing graph engines on a series of snapshots.
On querying historical evolving graph sequences
- Computer ScienceProc. VLDB Endow.
This work proposes that historical graph-structured data be maintained for analytical processing and puts forward a solution framework called FVF, which is highly efficient in EGS query processing.
A Query Based Approach for Mining Evolving Graphs
- Computer ScienceAusDM
This paper addresses the novel problem of querying evolving graphs using spatio-temporal patterns by answering selection queries, which can discover evolving subgraphs that satisfy both a temporal and a spatial predicate.
Mining Temporally Evolving Graphs
- Computer Science
This paper provides a framework to approach problems of this kind and identifies interesting problems at each level and can be applied to other domains, where data can be mode led as graph, such as network intrusion detection o r social networks.
GraphScope: parameter-free mining of large time-evolving graphs
- Computer ScienceKDD '07
The efficiency and effectiveness of the GraphScope is demonstrated, which is designed to operate on large graphs, in a streaming fashion, on real datasets from several diverse domains, and produces meaningful time-evolving patterns that agree with human intuition.