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Kittisak Sajjapongse
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2010 – 2019
- 2017
- [c7]Jungwon Kim, Kittisak Sajjapongse, Seyong Lee, Jeffrey S. Vetter:
Design and Implementation of Papyrus: Parallel Aggregate Persistent Storage. IPDPS 2017: 1151-1162 - 2016
- [c6]Kittisak Sajjapongse, Ruidong Gu, Michela Becchi:
IVM: a task-based shared memory programming model and runtime system to enable uniform access to CPU-GPU clusters. Conf. Computing Frontiers 2016: 154-163 - 2015
- [c5]Michael Butler, Kittisak Sajjapongse, Michela Becchi:
Improving Application Concurrency on GPUs by Managing Implicit and Explicit Synchronizations. ICPADS 2015: 535-544 - 2014
- [j1]Huan Truong, Da Li, Kittisak Sajjapongse, Gavin C. Conant, Michela Becchi:
Large-Scale Pairwise Alignments on GPU Clusters: Exploring the Implementation Space. J. Signal Process. Syst. 77(1-2): 131-149 (2014) - [c4]Kittisak Sajjapongse, Tejaswi Agarwal, Michela Becchi:
A flexible scheduling framework for heterogeneous CPU-GPU clusters. HiPC 2014: 1-11 - 2013
- [c3]Da Li, Kittisak Sajjapongse, Huan Truong, Gavin C. Conant, Michela Becchi:
A distributed CPU-GPU framework for pairwise alignments on large-scale sequence datasets. ASAP 2013: 329-338 - [c2]Kittisak Sajjapongse, Xiang Wang, Michela Becchi:
A preemption-based runtime to efficiently schedule multi-process applications on heterogeneous clusters with GPUs. HPDC 2013: 179-190 - 2012
- [c1]Michela Becchi, Kittisak Sajjapongse, Ian Graves, Adam M. Procter, Vignesh T. Ravi, Srimat T. Chakradhar:
A virtual memory based runtime to support multi-tenancy in clusters with GPUs. HPDC 2012: 97-108
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