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ORCIDVignesh T. Ravi
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2010 – 2019
- 2017
[c19]Vignesh T. Ravi, James Erwin, Pradeep Sivakumar, C. Q. Tang, Jianxin Xiong, Ravindra Babu Ganapathi, Mark Debbage:
Host Software Stack Optimizations to Maximize Aggregate Fabric Throughput. Hot Interconnects 2017: 95-102- 2014
- [j4]Indrani Paul, Vignesh T. Ravi, Srilatha Manne, Manish Arora, Sudhakar Yalamanchili:
Coordinated energy management in heterogeneous processors. Sci. Program. 22(2): 93-108 (2014) - [j3]Mai Zheng, Vignesh T. Ravi, Feng Qin, Gagan Agrawal:
GMRace: Detecting Data Races in GPU Programs via a Low-Overhead Scheme. IEEE Trans. Parallel Distributed Syst. 25(1): 104-115 (2014) - 2013
- [j2]Vignesh T. Ravi, Michela Becchi, Wei Jiang, Gagan Agrawal, Srimat T. Chakradhar:
Scheduling concurrent applications on a cluster of CPU-GPU nodes. Future Gener. Comput. Syst. 29(8): 2262-2271 (2013) - [c18]Indrani Paul
, Vignesh T. Ravi, Srilatha Manne, Manish Arora, Sudhakar Yalamanchili:
Coordinated energy management in heterogeneous processors. SC 2013: 59:1-59:12 - 2012
- [j1]Vignesh T. Ravi, Wenjing Ma, David Chiu, Gagan Agrawal:
Compiler and runtime support for enabling reduction computations on heterogeneous systems. Concurr. Comput. Pract. Exp. 24(5): 463-480 (2012) - [c17]Vignesh T. Ravi, Michela Becchi, Wei Jiang, Gagan Agrawal, Srimat T. Chakradhar:
Scheduling Concurrent Applications on a Cluster of CPU-GPU Nodes. CCGRID 2012: 140-147 - [c16]Mai Zheng, Vignesh T. Ravi, Wenjing Ma, Feng Qin, Gagan Agrawal:
GMProf: A low-overhead, fine-grained profiling approach for GPU programs. HiPC 2012: 1-10 - [c15]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 - [c14]Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, Srimat T. Chakradhar:
ValuePack: value-based scheduling framework for CPU-GPU clusters. SC 2012: 53 - 2011
- [c13]Xin Huo, Vignesh T. Ravi, Gagan Agrawal:
Porting irregular reductions on heterogeneous CPU-GPU configurations. HiPC 2011: 1-10 - [c12]Vignesh T. Ravi, Gagan Agrawal:
A dynamic scheduling framework for emerging heterogeneous systems. HiPC 2011: 1-10 - [c11]Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, Srimat T. Chakradhar:
Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework. HPDC 2011: 217-228 - [c10]Xin Huo, Vignesh T. Ravi, Wenjing Ma, Gagan Agrawal:
An execution strategy and optimized runtime support for parallelizing irregular reductions on modern GPUs. ICS 2011: 2-11 - [c9]Mai Zheng, Vignesh T. Ravi, Feng Qin, Gagan Agrawal:
GRace: a low-overhead mechanism for detecting data races in GPU programs. PPoPP 2011: 135-146 - 2010
- [c8]Wei Jiang, Vignesh T. Ravi, Gagan Agrawal:
A Map-Reduce System with an Alternate API for Multi-core Environments. CCGRID 2010: 84-93 - [c7]Xin Huo, Vignesh T. Ravi, Wenjing Ma, Gagan Agrawal:
Approaches for parallelizing reductions on modern GPUs. HiPC 2010: 1-10 - [c6]Venkatram Ramanathan, Wenjing Ma, Vignesh T. Ravi, Tantan Liu, Gagan Agrawal:
Parallelizing an Information Theoretic Co-clustering Algorithm Using a Cloud Middleware. ICDM Workshops 2010: 186-193 - [c5]Vignesh T. Ravi, Wenjing Ma, David Chiu, Gagan Agrawal:
Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations. ICS 2010: 137-146
2000 – 2009
- 2009
- [c4]Vignesh T. Ravi, Gagan Agrawal:
Performance Issues in Parallelizing Data-Intensive Applications on a Multi-core Cluster. CCGRID 2009: 308-315 - [c3]Wei Jiang, Vignesh T. Ravi, Gagan Agrawal:
Comparing map-reduce and FREERIDE for data-intensive applications. CLUSTER 2009: 1-10 - [c2]Vignesh T. Ravi, Gagan Agrawal:
Integrating and optimizing transactional memory in a data mining middleware. HiPC 2009: 215-224 - [c1]Leonid Glimcher, Vignesh T. Ravi, Gagan Agrawal:
Supporting load balancing for distributed data-intensive applications. HiPC 2009: 235-244
Coauthor Index
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