Data structures and network algorithms
Data structures and network algorithms
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Performance optimization of VLSI interconnect layout
Integration, the VLSI Journal
Fundamentals of Computer Alori
Fundamentals of Computer Alori
Fast matrix multiplies using graphics hardware
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Parallel Implementation of Borvka's Minimum Spanning Tree Algorithm
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Practical Parallel Algorithms for Minimum Spanning Trees
SRDS '98 Proceedings of the The 17th IEEE Symposium on Reliable Distributed Systems
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Applications of k-Local MST for Topology Control and Broadcasting in Wireless Ad Hoc Networks
IEEE Transactions on Parallel and Distributed Systems
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Fast shared-memory algorithms for computing the minimum spanning forest of sparse graphs
Journal of Parallel and Distributed Computing
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
An efficient transactional memory algorithm for computing minimum spanning forest of sparse graphs
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
Fast minimum spanning tree for large graphs on the GPU
Proceedings of the Conference on High Performance Graphics 2009
Designing efficient sorting algorithms for manycore GPUs
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
A Divide-and-Conquer Approach for Minimum Spanning Tree-Based Clustering
IEEE Transactions on Knowledge and Data Engineering
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Fast euclidean minimum spanning tree: algorithm, analysis, and applications
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 14th International Conference on Extending Database Technology
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
Atomic-free irregular computations on GPUs
Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units
TOUCH: in-memory spatial join by hierarchical data-oriented partitioning
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Scalable parallel OPTICS data clustering using graph algorithmic techniques
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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The proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph's minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper we propose a novel, scalable, parallel MSF algorithm for undirected weighted graphs. Our algorithm leverages Prim's algorithm in a parallel fashion, concurrently expanding several subsets of the computed MSF. Our effort focuses on minimizing the communication among different processors without constraining the local growth of a processor's computed subtree. In effect, we achieve a scalability that previous approaches lacked. We implement our algorithm in CUDA, running on a GPU and study its performance using real and synthetic, sparse as well as dense, structured and unstructured graph data. Our experimental study demonstrates that our algorithm outperforms the previous state-of-the-art GPU-based MSF algorithm, while being several orders of magnitude faster than sequential CPU-based algorithms.