Aravind Sukumaran-Rajam

ORCID

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 46

Top Venues

IPDPS

CoRR

PACT

University of Strasbourg, France (PHD)

Education History

University of Strasbourg, France

PHD

Publications

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)
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) (2023)
M. Emin Ozturk, Omid Asudeh, Gerald Sabin, P. Sadayappan, Aravind Sukumaran-Rajam
A Performance Portability Study Using Tensor Contraction Benchmarks. IPDPS Workshops (2023)
Süreyya Emre Kurt, Jinghua Yan, Aravind Sukumaran-Rajam, Prashant Pandey, P. Sadayappan
Communication Optimization for Distributed Execution of Graph Neural Networks. IPDPS (2023)
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 (2022)
Lizhi Xiang, P. Sadayappan, Aravind Sukumaran-Rajam
High-Performance Architecture Aware Sparse Convolutional Neural Networks for GPUs. PACT (2022)
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)
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) (2022)
Miheer Vaidya, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan
Comprehensive Accelerator-Dataflow Co-design Optimization for Convolutional Neural Networks. CGO (2022)
Süreyya Emre Kurt, Saurabh Raje, Aravind Sukumaran-Rajam, P. Sadayappan
Sparsity-Aware Tensor Decomposition. IPDPS (2022)
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)
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) (2022)
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)
Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan
Efficient Distributed Algorithms for Convolutional Neural Networks. SPAA (2021)
Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan
Efficient distributed algorithms for Convolutional Neural Networks. CoRR (2021)
Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan
Analytical Characterization and Design Space Exploration for Optimization of CNNs. CoRR (2021)
Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan
Analytical characterization and design space exploration for optimization of CNNs. ASPLOS (2021)
Gordon Euhyun Moon, J. Austin Ellis, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, P. Sadayappan
ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization. KDD (2020)
Süreyya Emre Kurt, Aravind Sukumaran-Rajam, Fabrice Rastello, P. Sadayappan
Efficient tiled sparse matrix multiplication through matrix signatures. SC (2020)
Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan
Load-Balanced Sparse MTTKRP on GPUs. CoRR (2019)
Prashant Singh Rawat, Miheer Vaidya, Aravind Sukumaran-Rajam, Atanas Rountev, Louis-Noël Pouchet, P. Sadayappan
On Optimizing Complex Stencils on GPUs. IPDPS (2019)
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)
Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan
Load-Balanced Sparse MTTKRP on GPUs. IPDPS (2019)
Gordon Euhyun Moon, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, P. Sadayappan
PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization. CoRR (2019)
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)
Changwan Hong, Aravind Sukumaran-Rajam, Israt Nisa, Kunal Singh, P. Sadayappan
Adaptive sparse tiling for sparse matrix multiplication. PPoPP (2019)
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)
Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Prashant Singh Rawat, Sriram Krishnamoorthy, P. Sadayappan
An efficient mixed-mode representation of sparse tensors. SC (2019)
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)
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)
Prashant Singh Rawat, Aravind Sukumaran-Rajam, Atanas Rountev, Fabrice Rastello, Louis-Noël Pouchet, P. Sadayappan
Associative instruction reordering to alleviate register pressure. SC (2018)
Gordon Euhyun Moon, Israt Nisa, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Srinivasan Parthasarathy, P. Sadayappan
Parallel Latent Dirichlet Allocation on GPUs. ICCS (2) (2018)
Israt Nisa, Aravind Sukumaran-Rajam, Süreyya Emre Kurt, Changwan Hong, P. Sadayappan
Sampled Dense Matrix Multiplication for High-Performance Machine Learning. HiPC (2018)
Prashant Singh Rawat, Fabrice Rastello, Aravind Sukumaran-Rajam, Louis-Noël Pouchet, Atanas Rountev, P. Sadayappan
Register optimizations for stencils on GPUs. PPOPP (2018)
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)
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)
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)
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)
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) (2018)
Israt Nisa, Aravind Sukumaran-Rajam, Rakshith Kunchum, P. Sadayappan
Parallel CCD++ on GPU for Matrix Factorization. GPGPU@PPoPP (2017)
Changwan Hong, Aravind Sukumaran-Rajam, Jinsung Kim, P. Sadayappan
MultiGraph: Efficient Graph Processing on GPUs. PACT (2017)
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)
Gordon Euhyun Moon, Aravind Sukumaran-Rajam, P. Sadayappan
Parallel LDA with Over-Decomposition. HiPC Workshops (2017)
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)
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)
Aravind Sukumaran-Rajam, Philippe Clauss
The Polyhedral Model of Nonlinear Loops. ACM Trans. Archit. Code Optim. 12 (4) (2016)
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)
Aravind Sukumaran-Rajam, Juan Manuel Martinez Caamaño, Willy Wolff, Alexandra Jimborean, Philippe Clauss
Speculative Program Parallelization with Scalable and Decentralized Runtime Verification. RV (2014)
Alexandra Jimborean, Philippe Clauss, Juan Manuel Martinez Caamaño, Aravind Sukumaran-Rajam
Online Dynamic Dependence Analysis for Speculative Polyhedral Parallelization. Euro-Par (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aravind Sukumaran-Rajam