Greed Is Good: Parallel Algorithms for Bipartite-Graph Partial Coloring on Multicore Architectures

Taş, Mustafa Kemal; Kaya, Kamer; Saule, Érik

doi:10.1109/icpp.2017.59

Cited by 11 publications

(9 citation statements)

References 26 publications

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“…As with distance-1 coloring, we use algorithms from Deveci et al in KokkosKernels for local distance-2 coloring. Specifically, we use NB BIT, which is a "net-based" distance-2 coloring algorithm that uses the approach described by Tas ¸et al [22]. Instead of checking for distance-2 conflicts only between a single vertex and its two-hop neighborhood, the net-based approach detects distance-2 conflicts among the immediate neighbors of a vertex.…”

Section: Distance-2 Coloring (D2)mentioning

confidence: 99%

Parallel graph coloring algorithms for distributed GPU environments

et al. 2022

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Section: Distance-2 Coloring (D2)mentioning

confidence: 99%

Parallel graph coloring algorithms for distributed GPU environments

et al. 2022

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“…We compare BalColorTM with their proposed algorithms, i.e., CLU, VFF, Recoloring, in Section 2.3, and demonstrate that our proposed BalColorTM scheme on average performs best across all large real-world graphs. Tas et al [50] propose balanced graph coloring algorithms for bitpartie graphs, i.e., graphs whose vertices can be divided into two disjoint and independent sets U and V , and every edge (u, v ) either connects a vertex from U to V or a vertex from V to U . In contrast, ColorTM and BalColorTM are designed to be general, and efficiently color any arbitrary real-world graph using a large number of parallel threads.…”

Section: Related Workmentioning

confidence: 99%

High-Performance and Balanced Parallel Graph Coloring on Multicore Platforms

Giannoula

Peppas

Goumas

et al. 2022

Preprint

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Graph coloring is widely used to parallelize scientific applications by identifying subsets of independent tasks that can be executed simultaneously. Graph coloring assigns colors the vertices of a graph, such that no adjacent vertices have the same color. The number of colors used corresponds to the number of parallel steps in a real-world end-application. Therefore, the total runtime of the graph coloring kernel adds to the overall parallel overhead of the real-world end-application, whereas the number of the vertices of each color class determines the number of the independent concurrent tasks of each parallel step, thus affecting the amount of parallelism and hardware resource utilization in the execution of the real-world end-application.In this work, we propose a high-performance graph coloring algorithm, named ColorTM, that leverages Hardware Transactional Memory (HTM) to detect coloring inconsistencies between adjacent vertices. ColorTM detects and resolves coloring inconsistencies between adjacent vertices with an eager approach to minimize data access costs, and implements a speculative synchronization scheme to minimize synchronization costs and increase parallelism. We extend our proposed algorithmic design to propose a balanced graph coloring algorithm, named BalColorTM, with which all color classes include almost the same number of vertices to achieve high parallelism and resource utilization in the execution of the real-world end-applications.We evaluate ColorTM and BalColorTM using a wide variety of large real-world graphs with diverse characteristics. ColorTM and BalColorTM improve performance by 12.98x and 1.91x on average using 56 parallel threads compared to prior state-of-the-art approaches. Moreover, we study the impact of our proposed graph coloring algorithmic designs on a popular end-application, i.e., Community Detection, and demonstrate the ColorTM and BalColorTM can provide high performance improvements in real-world end-applications across various input data given.

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“…For these problems, Kokkos Kernels implements a parallel algorithm called net-based (NB) coloring by Tas ¸et al [28]. The simplest approach to greedy distance-2 coloring is to extend the distance-1 VB algorithm to loop over both neighbors and neighbors-of-neighbors.…”

Section: Distance-2 Graph Coloringmentioning

confidence: 99%

Kokkos Kernels: Performance Portable Sparse/Dense Linear Algebra and Graph Kernels

Rajamanickam¹,

Acer²,

Berger-Vergiat³

et al. 2021

Preprint

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As hardware architectures are evolving in the push towards exascale, developing Computational Science and Engineering (CSE) applications depend on performance portable approaches for sustainable software development. This paper describes one aspect of performance portability with respect to developing a portable library of kernels that serve the needs of several CSE applications and software frameworks. We describe Kokkos Kernels, a library of kernels for sparse linear algebra, dense linear algebra and graph kernels. We describe the design principles of such a library and demonstrate portable performance of the library using some selected kernels. Specifically, we demonstrate the performance of four sparse kernels, three dense batched kernels, two graph kernels and one team level algorithm.

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Greed Is Good: Parallel Algorithms for Bipartite-Graph Partial Coloring on Multicore Architectures

Cited by 11 publications

References 26 publications

Parallel graph coloring algorithms for distributed GPU environments

Parallel graph coloring algorithms for distributed GPU environments

High-Performance and Balanced Parallel Graph Coloring on Multicore Platforms

Kokkos Kernels: Performance Portable Sparse/Dense Linear Algebra and Graph Kernels

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