2012 41st International Conference on Parallel Processing Workshops 2012
DOI: 10.1109/icppw.2012.82
|View full text |Cite
|
Sign up to set email alerts
|

A CUDA-MPI Hybrid Bitonic Sorting Algorithm for GPU Clusters

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2016
2016
2020
2020

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 12 publications
(3 citation statements)
references
References 4 publications
0
3
0
Order By: Relevance
“…The array is changed by taking the elements from Q0 to Q9. Repeat the same procedure until most significant digit and we get the sorted elements [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”
Section: Radix Sortmentioning
confidence: 99%
“…The array is changed by taking the elements from Q0 to Q9. Repeat the same procedure until most significant digit and we get the sorted elements [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”
Section: Radix Sortmentioning
confidence: 99%
“…An important direction for research in this subject is the elegant use of heterogeneous grids/clusters/clouds of computers with banks of GPUs while sorting. In their research White et al [39] take a first step in this direction by implementing a 2-phase protocol to leverage the well known parallel bitonic sort algorithm in a cluster environment with heterogeneous compute nodes. In their work data was communicated through the MPI message passing standard.…”
Section: Cluster and Grid Gpu Sortingmentioning
confidence: 99%
“…Bandyopadhyay et al [1] Capannini et al [2] Batcher [3] Dowd et al [4] Blelloch et al [5] Dusseau et al [6] Cederman et al [7] Cederman et al [8] Bandyopadhyay et al [9] Davidson et al [10] Ye et al [11] Dehne et al [12] Satish et al [13] Govindaraju et al [14] Baraglia et al [15] Manca et al [16] Le Grand et al [17] Sundar et al [18] Beliakov et al [20] Leischner et al [21] Ye et al [22] Chen et al [23] Satish et al [24] Merrill et al [25] Sun et al [26] Jan et al [27] Ajdari et al [29] Tansic et al [30] Nvidia [31] Brodtkorb et al [32] Peters et al [34] Bandyopadhyay et al [35] Sengupta et al [36] Sengupta et al [37] Keckler et al [38] White et al [39] Zhong et al [40] Greb et al [41] Bilardi et al [42] Zachman et al [43] Peters et al [44] Kipfer et al [45] Govindaraju et al [46] Sintorn et al [47] Polok et al [48] Li et al…”
Section: Miscmentioning
confidence: 99%