Fast minimum spanning tree for large graphs on the GPU

Vineet, Vibhav; Harish, Pawan; Patidar, Suryakant; Narayanan, P. J.

doi:10.1145/1572769.1572796

Cited by 93 publications

(51 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…lg n MST-Boruvaka [21] m lg n lg n m lg n Suffix Tree [12] nk k nk Suffix Array [12] nk lg m k lg m nk lg m/C FFT [13] n lg n lg n n lg n/C List Ranking [17] n lg n lg n n lg n APSP [8] n 3 lg n n lg n n 3 lg n/( √ ZC)…”

Section: Algorithmsmentioning

confidence: 99%

Analysis of classic algorithms on GPUs

Chamberlain

Agrawal

2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

Abstract-The recently developed Threaded Many-core Memory (TMM) model provides a framework for analyzing algorithms for highly-threaded many-core machines such as GPUs. In particular, it tries to capture the fact that these machines hide memory latencies via the use of a large number of threads and large memory bandwidth. The TMM model analysis contains two components: computational complexity and memory complexity.A model is only useful if it can explain and predict empirical data. In this work, we investigate the effectiveness of the TMM model. We analyze algorithms for 5 classic problems -suffix tree/array for string matching, fast Fourier transform, merge sort, list ranking, and all-pairs shortest paths -under this model, and compare the results of the analysis with the experimental findings of ours and other researchers who have implemented and measured the performance of these algorithms on an spectrum of diverse GPUs. We find that the TMM model is able to predict important and sometimes previously unexplained trends and artifacts in the experimental data.

show abstract

Section: Algorithmsmentioning

confidence: 99%

Analysis of classic algorithms on GPUs

Chamberlain

Agrawal

2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

show abstract

“…Harish and Narayanan [10] describe implemen- tations of important graph algorithms such as BFS, SingleSource Shortest Paths, Minimum Spanning Tree, etc. Vineet et al [23] and Nobari et al [20] propose computing the minimum spanning tree and forest, respectively, on GPUs. All of these approaches rely on atomic instructions for updating shared data, and none of them exploit algebraic properties.…”

Section: Related Workmentioning

confidence: 99%

Atomic-free irregular computations on GPUs

Nasre

Burtscher

Pingali

2013

Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units

View full text Add to dashboard Cite

Atomic instructions are a key ingredient of codes that operate on irregular data structures like trees and graphs. It is well known that atomics can be expensive, especially on massively parallel GPUs, and are often on the critical path of a program. In this paper, we present two high-level methods to eliminate atomics in irregular programs. The first method advocates synchronous processing using barriers. We illustrate how to exploit synchronous processing to avoid atomics even when the threads' memory accesses conflict with each other. The second method is based on exploiting algebraic properties of algorithms to elide atomics. Specifically, we focus on three key properties: monotonicity, idempotency and associativity, and show how each of them enables an atomic-free implementation. We illustrate the generality of the two methods by applying them to five irregular graph applications: breadth-first search, single-source shortest paths computation, Delaunay mesh refinement, pointer analysis and survey propagation, and show that both methods provide substantial speedup in each case on different GPUs.

show abstract

“…Vineet et al develop a GPU implementation of Boruvka's minimum spanning tree algorithm using data parallel primitives such as sort, scan and reduce [24]. Merrill et al discuss parallelization strategies and performance characterization of GPU graph traversals using various algorithms based on data parallel primitives [18].…”

Section: Related Workmentioning

confidence: 99%

General transformations for GPU execution of tree traversals

Goldfarb

Kulkarni

2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

View full text Add to dashboard Cite

With the advent of programmer-friendly GPU computing environments, there has been much interest in offloading workloads that can exploit the high degree of parallelism available on modern GPUs. Exploiting this parallelism and optimizing for the GPU memory hierarchy is well-understood for regular applications that operate on dense data structures such as arrays and matrices. However, there has been significantly less work in the area of irregular algorithms and even less so when pointer-based dynamic data structures are involved. Recently, irregular algorithms such as Barnes-Hut and kd-tree traversals have been implemented on GPUs, yielding significant performance gains over CPU implementations. However, the implementations often rely on exploiting application-specific semantics to get acceptable performance. We argue that there are general-purpose techniques for implementing irregular algorithms on GPUs that exploit similarities in algorithmic structure rather than application-specific knowledge. We demonstrate these techniques on several tree traversal algorithms, achieving speedups of up to 38× over 32-thread CPU versions.

show abstract

Fast minimum spanning tree for large graphs on the GPU

Cited by 93 publications

References 7 publications

Analysis of classic algorithms on GPUs

Analysis of classic algorithms on GPUs

Atomic-free irregular computations on GPUs

General transformations for GPU execution of tree traversals

Contact Info

Product

Resources

About