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ORCIDAravind Sukumaran-Rajam
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2020 – today
- 2024
[e1]Gabriel Rodríguez, P. Sadayappan, Aravind Sukumaran-Rajam:
Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction, CC 2024, Edinburgh, United Kingdom, March 2-3, 2024. ACM 2024 [contents]- 2023
- [j5]Dwaipayan Choudhury
, Lizhi Xiang, Aravind Sukumaran-Rajam, Anantharaman Kalyanaraman, Partha Pratim Pande:
Accelerating Graph Computations on 3D NoC-Enabled PIM Architectures. ACM Trans. Design Autom. Electr. Syst. 28(3): 30:1-30:16 (2023) - [c40]Süreyya Emre Kurt, Jinghua Yan, Aravind Sukumaran-Rajam, Prashant Pandey, P. Sadayappan:
Communication Optimization for Distributed Execution of Graph Neural Networks. IPDPS 2023: 512-523 - [c39]M. Emin Ozturk, Omid Asudeh, Gerald Sabin, P. Sadayappan, Aravind Sukumaran-Rajam:
A Performance Portability Study Using Tensor Contraction Benchmarks. IPDPS Workshops 2023: 591-600 - [c38]Lizhi Xiang, Miao Yin, Chengming Zhang, Aravind Sukumaran-Rajam, P. Sadayappan, Bo Yuan, Dingwen Tao:
TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition. PPoPP 2023: 260-273 - [c37]Lizhi Xiang, Arif M. Khan, S. M. Ferdous, Aravind Sukumaran-Rajam, Mahantesh Halappanavar:
cuAlign: Scalable Network Alignment on GPU Accelerators. SC Workshops 2023: 747-755 - 2022
- [j4]Dwaipayan Choudhury, Aravind Sukumaran-Rajam, Ananth Kalyanaraman, Partha Pratim Pande:
High-Performance and Energy-Efficient 3D Manycore GPU Architecture for Accelerating Graph Analytics. ACM J. Emerg. Technol. Comput. Syst. 18(1): 18:1-18:19 (2022) - [j3]Dwaipayan Choudhury, Reet Barik, Aravind Sukumaran-Rajam, Ananth Kalyanaraman, Partha Pratim Pande:
Software/Hardware Co-design of 3D NoC-based GPU Architectures for Accelerated Graph Computations. ACM Trans. Design Autom. Electr. Syst. 27(6): 61:1-61:22 (2022) - [c36]Yufan Xu, Qiwei Yuan, Erik Curtis Barton, Rui Li, P. Sadayappan, Aravind Sukumaran-Rajam:
Effective Performance Modeling and Domain-Specific Compiler Optimization of CNNs for GPUs. PACT 2022: 252-264 - [c35]Lizhi Xiang, P. Sadayappan, Aravind Sukumaran-Rajam:
High-Performance Architecture Aware Sparse Convolutional Neural Networks for GPUs. PACT 2022: 265-278 - [c34]Yufan Xu, Saurabh Raje, Atanas Rountev, Gerald Sabin, Aravind Sukumaran-Rajam, P. Sadayappan:
Training of deep learning pipelines on memory-constrained GPUs via segmented fused-tiled execution. CC 2022: 104-116 - [c33]Miheer Vaidya, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan:
Comprehensive Accelerator-Dataflow Co-design Optimization for Convolutional Neural Networks. CGO 2022: 325-335 - [c32]Süreyya Emre Kurt, Saurabh Raje, Aravind Sukumaran-Rajam, P. Sadayappan:
Sparsity-Aware Tensor Decomposition. IPDPS 2022: 952-962 - [i5]Lizhi Xiang, Miao Yin, Chengming Zhang, Aravind Sukumaran-Rajam, P. Sadayappan, Bo Yuan, Dingwen Tao:
TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition. CoRR abs/2211.03715 (2022) - 2021
- [c31]Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan:
Analytical characterization and design space exploration for optimization of CNNs. ASPLOS 2021: 928-942 - [c30]Lizhi Xiang, Arif Khan, Edoardo Serra, Mahantesh Halappanavar, Aravind Sukumaran-Rajam:
cuTS: scaling subgraph isomorphism on distributed multi-GPU systems using trie based data structure. SC 2021: 69 - [c29]Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan:
Efficient Distributed Algorithms for Convolutional Neural Networks. SPAA 2021: 439-442 - [i4]Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan:
Analytical Characterization and Design Space Exploration for Optimization of CNNs. CoRR abs/2101.09808 (2021) - [i3]Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan:
Efficient distributed algorithms for Convolutional Neural Networks. CoRR abs/2105.13480 (2021) - 2020
- [c28]Gordon Euhyun Moon, J. Austin Ellis, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, P. Sadayappan:
ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization. KDD 2020: 1758-1767 - [c27]Süreyya Emre Kurt, Aravind Sukumaran-Rajam, Fabrice Rastello, P. Sadayappan:
Efficient tiled sparse matrix multiplication through matrix signatures. SC 2020: 87
2010 – 2019
- 2019
- [c26]Jinsung Kim, Aravind Sukumaran-Rajam, Vineeth Thumma, Sriram Krishnamoorthy, Ajay Panyala, Louis-Noël Pouchet, Atanas Rountev, P. Sadayappan:
A Code Generator for High-Performance Tensor Contractions on GPUs. CGO 2019: 85-95 - [c25]Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan:
Load-Balanced Sparse MTTKRP on GPUs. IPDPS 2019: 123-133 - [c24]Prashant Singh Rawat, Miheer Vaidya, Aravind Sukumaran-Rajam, Atanas Rountev, Louis-Noël Pouchet, P. Sadayappan:
On Optimizing Complex Stencils on GPUs. IPDPS 2019: 641-652 - [c23]Changwan Hong, Aravind Sukumaran-Rajam, Israt Nisa, Kunal Singh, P. Sadayappan:
Adaptive sparse tiling for sparse matrix multiplication. PPoPP 2019: 300-314 - [c22]Gordon Euhyun Moon, Denis Newman-Griffis, Jinsung Kim, Aravind Sukumaran-Rajam, Eric Fosler-Lussier, P. Sadayappan:
Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and Graph Embeddings. MLHPC@SC 2019: 44-55 - [c21]Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Prashant Singh Rawat, Sriram Krishnamoorthy, P. Sadayappan:
An efficient mixed-mode representation of sparse tensors. SC 2019: 49:1-49:25 - [c20]Rui Li, Aravind Sukumaran-Rajam, Richard Veras, Tze Meng Low, Fabrice Rastello, Atanas Rountev, P. Sadayappan:
Analytical cache modeling and tilesize optimization for tensor contractions. SC 2019: 74:1-74:13 - [i2]Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan:
Load-Balanced Sparse MTTKRP on GPUs. CoRR abs/1904.03329 (2019) - [i1]Gordon Euhyun Moon, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, P. Sadayappan:
PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization. CoRR abs/1904.07935 (2019) - 2018
- [j2]Prashant Singh Rawat, Miheer Vaidya, Aravind Sukumaran-Rajam, Mahesh Ravishankar, Vinod Grover, Atanas Rountev, Louis-Noël Pouchet, P. Sadayappan:
Domain-Specific Optimization and Generation of High-Performance GPU Code for Stencil Computations. Proc. IEEE 106(11): 1902-1920 (2018) - [c19]Israt Nisa, Aravind Sukumaran-Rajam, Süreyya Emre Kurt, Changwan Hong, P. Sadayappan:
Sampled Dense Matrix Multiplication for High-Performance Machine Learning. HiPC 2018: 32-41 - [c18]Changwan Hong, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Jinsung Kim, Süreyya Emre Kurt, Israt Nisa, Shivani Sabhlok, Ümit V. Çatalyürek, Srinivasan Parthasarathy, P. Sadayappan:
Efficient sparse-matrix multi-vector product on GPUs. HPDC 2018: 66-79 - [c17]Gordon Euhyun Moon, Israt Nisa, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Srinivasan Parthasarathy, P. Sadayappan:
Parallel Latent Dirichlet Allocation on GPUs. ICCS (2) 2018: 259-272 - [c16]Jinsung Kim, Aravind Sukumaran-Rajam, Changwan Hong, Ajay Panyala, Rohit Kumar Srivastava, Sriram Krishnamoorthy, P. Sadayappan:
Optimizing Tensor Contractions in CCSD(T) for Efficient Execution on GPUs. ICS 2018: 96-106 - [c15]Jyothi Vedurada, Arjun Suresh, Aravind Sukumaran-Rajam, Jinsung Kim, Changwan Hong, Ajay Panyala, Sriram Krishnamoorthy, V. Krishna Nandivada, Rohit Kumar Srivastava, P. Sadayappan:
TTLG - An Efficient Tensor Transposition Library for GPUs. IPDPS 2018: 578-588 - [c14]Israt Nisa, Charles Siegel, Aravind Sukumaran-Rajam, Abhinav Vishnu, P. Sadayappan:
Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs. IPDPS Workshops 2018: 1056-1065 - [c13]Changwan Hong, Aravind Sukumaran-Rajam, Jinsung Kim, Prashant Singh Rawat, Sriram Krishnamoorthy, Louis-Noël Pouchet, Fabrice Rastello, P. Sadayappan:
GPU code optimization using abstract kernel emulation and sensitivity analysis. PLDI 2018: 736-751 - [c12]Prashant Singh Rawat, Fabrice Rastello, Aravind Sukumaran-Rajam, Louis-Noël Pouchet, Atanas Rountev, P. Sadayappan:
Register optimizations for stencils on GPUs. PPoPP 2018: 168-182 - [c11]Changwan Hong, Aravind Sukumaran-Rajam, Jinsung Kim, Prashant Singh Rawat, Sriram Krishnamoorthy, Louis-Noël Pouchet, Fabrice Rastello, P. Sadayappan:
Performance modeling for GPUs using abstract kernel emulation. PPoPP 2018: 397-398 - [c10]Prashant Singh Rawat, Aravind Sukumaran-Rajam, Atanas Rountev, Fabrice Rastello, Louis-Noël Pouchet, P. Sadayappan:
Associative instruction reordering to alleviate register pressure. SC 2018: 46:1-46:13 - 2017
- [c9]Changwan Hong, Aravind Sukumaran-Rajam, Jinsung Kim, P. Sadayappan:
MultiGraph: Efficient Graph Processing on GPUs. PACT 2017: 27-40 - [c8]Prashant Singh Rawat, Aravind Sukumaran-Rajam, Atanas Rountev, Fabrice Rastello, Louis-Noël Pouchet, P. Sadayappan:
POSTER: Statement Reordering to Alleviate Register Pressure for Stencils on GPUs. PACT 2017: 158-159 - [c7]Gordon Euhyun Moon, Aravind Sukumaran-Rajam, P. Sadayappan:
Parallel LDA with Over-Decomposition. HiPC Workshops 2017: 25-31 - [c6]Süreyya Emre Kurt, Vineeth Thumma, Changwan Hong, Aravind Sukumaran-Rajam, P. Sadayappan:
Characterization of Data Movement Requirements for Sparse Matrix Computations on GPUs. HiPC 2017: 283-293 - [c5]Rakshith Kunchum, Ankur Chaudhry, Aravind Sukumaran-Rajam, Qingpeng Niu, Israt Nisa, P. Sadayappan:
On improving performance of sparse matrix-matrix multiplication on GPUs. ICS 2017: 14:1-14:11 - [c4]Israt Nisa, Aravind Sukumaran-Rajam, Rakshith Kunchum, P. Sadayappan:
Parallel CCD++ on GPU for Matrix Factorization. GPGPU@PPoPP 2017: 73-83 - 2016
- [j1]Aravind Sukumaran-Rajam, Philippe Clauss:
The Polyhedral Model of Nonlinear Loops. ACM Trans. Archit. Code Optim. 12(4): 48:1-48:27 (2016) - 2015
- [b1]Aravind Sukumaran-Rajam:
Beyond the Realm of the Polyhedral Model: Combining Speculative Program Parallelization with Polyhedral Compilation. (Au delà des limites du modèle polyédrique: en combinant la parallélisation spéculative de programmes et la compilation polyédrique). University of Strasbourg, France, 2015 - [c3]Aravind Sukumaran-Rajam, Luis Esteban Campostrini, Juan Manuel Martinez Caamaño, Philippe Clauss:
Speculative Runtime Parallelization of Loop Nests: Towards Greater Scope and Efficiency. IPDPS Workshops 2015: 245-254 - 2014
- [c2]Aravind Sukumaran-Rajam, Juan Manuel Martinez Caamaño, Willy Wolff, Alexandra Jimborean, Philippe Clauss:
Speculative Program Parallelization with Scalable and Decentralized Runtime Verification. RV 2014: 124-139 - 2013
- [c1]Alexandra Jimborean, Philippe Clauss, Juan Manuel Martinez Caamaño, Aravind Sukumaran-Rajam:
Online Dynamic Dependence Analysis for Speculative Polyhedral Parallelization. Euro-Par 2013: 191-202
Coauthor Index
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last updated on 2024-08-05 20:21 CEST by the dblp team
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