A framework for scalable greedy coloring on distributed-memory parallel computers

Bozdağ, Doruk; Gebremedhin, Assefaw H.; Manne, Fredrik; Boman, Erik G.; Çatalyürek, Ümit V.

doi:10.1016/j.jpdc.2007.08.002

Cited by 48 publications

(56 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The BSP style gives a high-level framework for algorithmic development, which eases parallelization of irregular algorithms such as graph algorithms. Examples where this style has been employed are graph coloring [4], edge-weighted graph matching [15], and single-source shortest paths [14].…”

Section: Introductionmentioning

confidence: 99%

Parallel greedy graph matching using an edge partitioning approach

Patwary

Bisseling

Manne

2010

Proceedings of the Fourth International Workshop on High-Level Parallel Programming and Applications

Self Cite

View full text Add to dashboard Cite

We present a parallel version of the Karp-Sipser graph matching heuristic for the maximum cardinality problem. It is bulksynchronous, separating computation and communication, and uses an edge-based partitioning of the graph, translated from a twodimensional partitioning of the corresponding adjacency matrix.It is shown that the communication volume of Karp-Sipser graph matching is proportional to that of parallel sparse matrix-vector multiplication (SpMV), so that efficient partitioners developed for SpMV can be used. The algorithm is presented using a small basic set of 7 message types, which are discussed in detail. Experimental results show that for most matrices, edge-based partitioning is superior to vertex-based partitioning, in terms of both parallel speedup and matching quality. Good speedups are obtained on up to 64 processors.

show abstract

Section: Introductionmentioning

confidence: 99%

Parallel greedy graph matching using an edge partitioning approach

Patwary

Bisseling

Manne

2010

Proceedings of the Fourth International Workshop on High-Level Parallel Programming and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…We additionally adapted C 2 to multithread/multicore computers. C 3 is an iterative parallel greedy coloring algorithm that was proposed by Bozdag et al [3]. Section 6 compares the impact of these graph coloring algorithms on the performance of our parallel optimization method.…”

Section: A Novel Parallel Algorithmmentioning

confidence: 99%

“…Many papers evaluate the performance of graph coloring algorithms on parallel computers [3,15,22], but their impacts on the performance of mesh optimization algorithms are rarely reported. First of all, we confirmed the performance results published in previous papers for C 1 , C 2 , and C 3 coloring algorithms.…”

Section: Influence Of Graph Coloring Algorithms On Parallel Performancementioning

confidence: 99%

Performance Evaluation of a Parallel Algorithm for Simultaneous Untangling and Smoothing of Tetrahedral Meshes

Benítez

Rodríguez

Escobar

et al. 2014

Proceedings of the 22nd International Meshing Roundtable

View full text Add to dashboard Cite

Abstract.A new parallel algorithm for simultaneous untangling and smoothing of tetrahedral meshes is proposed in this paper. We provide a detailed analysis of its performance on shared-memory many-core computer architectures. This performance analysis includes the evaluation of execution time, parallel scalability, load balancing, and parallelism bottlenecks. Additionally, we compare the impact of three previously published graph coloring procedures on the performance of our parallel algorithm. We use six benchmark meshes with a wide range of sizes. Using these experimental data sets, we describe the behavior of the parallel algorithm for different data sizes. We demonstrate that this algorithm is highly scalable when it runs on two different high-performance many-core computers with up to 128 processors. However, some parallel deterioration is observed. Here, we analyze the main causes of this parallel deterioration.

show abstract

“…Although this problem is NP-hard in general, there are good heuristics that provide colorings with ∆ + 1 colors, where ∆ is the maximum degree of the graph. They can be computed in parallel and in a distributed setting, see [6], and large scale tests (especially for matrix computations) show that these algorithms behave quite well in practise, see [3]. We don't think these computations will be a bottleneck for real applications of our techniques.…”

Section: Overlay Initialization and Deadlock Detectionmentioning

confidence: 99%

Iterative computations with ordered read–write locks

Clauss

Gustedt

2010

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

Abstract:We introduce the framework of ordered read-write locks, ORWL, that are characterized by two main features: a strict FIFO policy for access and the attribution of access to lock-handles instead of processes or threads. These two properties allow applications to have a controlled pro-active access to resources and thereby to achieve a high degree of asynchronicity between different tasks of the same application. For the case of iterative computations with many parallel tasks which access their resources in a cyclic pattern we provide a generic technique to implement them by means of ORWL. We show that the possible execution patterns for such a system correspond to a combinatorial lattice structure and that this lattice is finite iff the configuration contains a potential deadlock. In addition, we provide efficient algorithms: one that allows for a deadlock-free initialization of such a system and another one for the detection of deadlocks in an already initialized system.

show abstract

A framework for scalable greedy coloring on distributed-memory parallel computers

Cited by 48 publications

References 24 publications

Parallel greedy graph matching using an edge partitioning approach

Parallel greedy graph matching using an edge partitioning approach

Performance Evaluation of a Parallel Algorithm for Simultaneous Untangling and Smoothing of Tetrahedral Meshes

Iterative computations with ordered read–write locks

Contact Info

Product

Resources

About