Untitled

Guo, Minyi; Pan, Yi; Liu, Zhen

doi:10.1023/a:1024262610201

Cited by 26 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, with respect to irregular array redistribution, previous work has usually focused on indexing, message generation, or communication efficiency. Guo et al first mentioned the problem of communication set generation for irregular applications [31]. They also devised optimization techniques for irregular array in nested loops.…”

Section: Related Workmentioning

confidence: 99%

New Bipartite Graph Techniques for Irregular Data Redistribution Scheduling

2019

Algorithms

View full text Add to dashboard Cite

For many parallel and distributed systems, automatic data redistribution improves its locality and increases system performance for various computer problems and applications. In general, an array can be distributed to multiple processing systems by using regular or irregular distributions. Some data distribution adopts BLOCK, CYCLIC, or BLOCK-CYCLIC to specify data array decomposition and distribution. On the other hand, irregular distributions specify a different-size data array distribution according to user-defined commands or procedures. In this work, we propose three bipartite graph problems, including the “maximum edge coloring problem”, the “maximum degree edge coloring problem”, and the “cost-sharing maximum edge coloring problem” to formulate these kinds of distribution problems. Next, we propose an approximation algorithm with a ratio bound of two for the maximum edge coloring problem when the input graph is biplanar. Moreover, we also prove that the “cost-sharing maximum edge coloring problem” is an NP-complete problem even when the input graph is biplanar.

show abstract

Section: Related Workmentioning

confidence: 99%

New Bipartite Graph Techniques for Irregular Data Redistribution Scheduling

2019

Algorithms

View full text Add to dashboard Cite

show abstract

“…Although most of the practical applications satisfy this condition, still some do not, so as to be prohibited from parallelism by affine decomposition. Most previous research only can parallel irregular loop and array at run time or use feedback [14][15][16]. In fact, if only the irregular access is caused by indirect array, loop and array reference can be partitioned at compile time on DMPCs, and the parallel does not cause data competition.…”

Section: Introductionmentioning

confidence: 99%

An Improvement to Affine Decomposition on Distributed Memory Architecture

Ding¹,

Zhao²,

Liu³

2012

2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering &Amp; Science

View full text Add to dashboard Cite

Automatic decomposition is an optimization technique that distributes computation and data onto different processors. The consequence of decomposition directly affects the performance of parallel program. Since every computing node has its own memory in distributed memory parallel computers (DMPCs), false dependence does not hinder the parallelism. Affine decomposition is an effective method to represent and derive computation partition and data distribution, and its principle of adding dependence constraint is too strict to gain more parallelism. Some loop nests do not satisfy the affine condition, and are prohibited from parallelism by affine decomposition. However, if only the irregular access is caused by indirect array, loop and array reference can be partitioned at compile time. To tackle above problems of affine decomposition, an improved static decomposition algorithm of DMPCs proposed in this paper. The experimental results show that this algorithm can improve the performance of parallel programs.

show abstract