Online Dynamic Dependence Analysis for Speculative Polyhedral Parallelization. - Alexandra Jimborean; Philippe Clauss; Juan Manuel Martinez Caamaño; Aravind Sukumaran-Rajam (Euro-Par, 2013)

Speculative Program Parallelization with Scalable and Decentralized Runtime Verification. - Aravind Sukumaran-Rajam; Juan Manuel Martinez Caamaño; Willy Wolff; Alexandra Jimborean; Philippe Clauss (RV, 2014)

Speculative Runtime Parallelization of Loop Nests: Towards Greater Scope and Efficiency. - Aravind Sukumaran-Rajam; Luis Esteban Campostrini; Juan Manuel Martinez Caamaño; Philippe Clauss (IPDPS Workshops, 2015)

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). - Aravind Sukumaran-Rajam (2015)

The Polyhedral Model of Nonlinear Loops. - Aravind Sukumaran-Rajam; Philippe Clauss (ACM Trans. Archit. Code Optim., 2016)

MultiGraph: Efficient Graph Processing on GPUs. - Changwan Hong; Aravind Sukumaran-Rajam; Jinsung Kim; P. Sadayappan (PACT, 2017)

POSTER: Statement Reordering to Alleviate Register Pressure for Stencils on GPUs. - Prashant Singh Rawat; Aravind Sukumaran-Rajam; Atanas Rountev; Fabrice Rastello; Louis-Noël Pouchet; P. Sadayappan (PACT, 2017)

Characterization of Data Movement Requirements for Sparse Matrix Computations on GPUs. - Süreyya Emre Kurt; Vineeth Thumma; Changwan Hong; Aravind Sukumaran-Rajam; P. Sadayappan (HiPC, 2017)

Parallel LDA with Over-Decomposition. - Gordon Euhyun Moon; Aravind Sukumaran-Rajam; P. Sadayappan (HiPC Workshops, 2017)

On improving performance of sparse matrix-matrix multiplication on GPUs. - Rakshith Kunchum; Ankur Chaudhry; Aravind Sukumaran-Rajam; Qingpeng Niu; Israt Nisa; P. Sadayappan (ICS, 2017)

Parallel CCD++ on GPU for Matrix Factorization. - Israt Nisa; Aravind Sukumaran-Rajam; Rakshith Kunchum; P. Sadayappan (GPGPU@PPoPP, 2017)

Sampled Dense Matrix Multiplication for High-Performance Machine Learning. - Israt Nisa; Aravind Sukumaran-Rajam; Süreyya Emre Kurt; Changwan Hong; P. Sadayappan (HiPC, 2018)

Efficient sparse-matrix multi-vector product on GPUs. - Changwan Hong; Aravind Sukumaran-Rajam; Bortik Bandyopadhyay; Jinsung Kim; Süreyya Emre Kurt; Israt Nisa; Shivani Sabhlok; Ümit V. Çatalyürek; Srinivasan Parthasarathy 001; P. Sadayappan (HPDC, 2018)

Parallel Latent Dirichlet Allocation on GPUs. - Gordon Euhyun Moon; Israt Nisa; Aravind Sukumaran-Rajam; Bortik Bandyopadhyay; Srinivasan Parthasarathy 001; P. Sadayappan (ICCS (2), 2018)

Optimizing Tensor Contractions in CCSD(T) for Efficient Execution on GPUs. - Jinsung Kim; Aravind Sukumaran-Rajam; Changwan Hong; Ajay Panyala; Rohit Kumar Srivastava; Sriram Krishnamoorthy; P. Sadayappan (ICS, 2018)

Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs. - Israt Nisa; Charles Siegel; Aravind Sukumaran-Rajam; Abhinav Vishnu; P. Sadayappan (IPDPS Workshops, 2018)

TTLG - An Efficient Tensor Transposition Library for GPUs. - Jyothi Vedurada; Arjun Suresh; Aravind Sukumaran-Rajam; Jinsung Kim; Changwan Hong; Ajay Panyala; Sriram Krishnamoorthy; V. Krishna Nandivada; Rohit Kumar Srivastava; P. Sadayappan (IPDPS, 2018)

GPU code optimization using abstract kernel emulation and sensitivity analysis. - Changwan Hong; Aravind Sukumaran-Rajam; Jinsung Kim; Prashant Singh Rawat; Sriram Krishnamoorthy; Louis-Noël Pouchet; Fabrice Rastello; P. Sadayappan (PLDI, 2018)

Performance modeling for GPUs using abstract kernel emulation. - Changwan Hong; Aravind Sukumaran-Rajam; Jinsung Kim; Prashant Singh Rawat; Sriram Krishnamoorthy; Louis-Noël Pouchet; Fabrice Rastello; P. Sadayappan (PPoPP, 2018)

Register optimizations for stencils on GPUs. - Prashant Singh Rawat; Fabrice Rastello; Aravind Sukumaran-Rajam; Louis-Noël Pouchet; Atanas Rountev; P. Sadayappan (PPoPP, 2018)

Associative instruction reordering to alleviate register pressure. - Prashant Singh Rawat; Aravind Sukumaran-Rajam; Atanas Rountev; Fabrice Rastello; Louis-Noël Pouchet; P. Sadayappan (SC, 2018)

Domain-Specific Optimization and Generation of High-Performance GPU Code for Stencil Computations. - Prashant Singh Rawat; Miheer Vaidya; Aravind Sukumaran-Rajam; Mahesh Ravishankar; Vinod Grover; Atanas Rountev; Louis-Noël Pouchet; P. Sadayappan (Proc. IEEE, 2018)

A Code Generator for High-Performance Tensor Contractions on GPUs. - Jinsung Kim; Aravind Sukumaran-Rajam; Vineeth Thumma; Sriram Krishnamoorthy; Ajay Panyala; Louis-Noël Pouchet; Atanas Rountev; P. Sadayappan (CGO, 2019)

Load-Balanced Sparse MTTKRP on GPUs. - Israt Nisa; Jiajia Li 001; Aravind Sukumaran-Rajam; Richard W. Vuduc; P. Sadayappan (IPDPS, 2019)

On Optimizing Complex Stencils on GPUs. - Prashant Singh Rawat; Miheer Vaidya; Aravind Sukumaran-Rajam; Atanas Rountev; Louis-Noël Pouchet; P. Sadayappan (IPDPS, 2019)

Adaptive sparse tiling for sparse matrix multiplication. - Changwan Hong; Aravind Sukumaran-Rajam; Israt Nisa; Kunal Singh; P. Sadayappan (PPoPP, 2019)

Analytical cache modeling and tilesize optimization for tensor contractions. - Rui Li 033; Aravind Sukumaran-Rajam; Richard Veras; Tze Meng Low; Fabrice Rastello; Atanas Rountev; P. Sadayappan (SC, 2019)

Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and Graph Embeddings. - Gordon Euhyun Moon; Denis Newman-Griffis; Jinsung Kim; Aravind Sukumaran-Rajam; Eric Fosler-Lussier; P. Sadayappan (MLHPC@SC, 2019)

An efficient mixed-mode representation of sparse tensors. - Israt Nisa; Jiajia Li 001; Aravind Sukumaran-Rajam; Prashant Singh Rawat; Sriram Krishnamoorthy; P. Sadayappan (SC, 2019)

PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization. - Gordon Euhyun Moon; Aravind Sukumaran-Rajam; Srinivasan Parthasarathy 001; P. Sadayappan (CoRR, 2019)

ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization. - Gordon Euhyun Moon; J. Austin Ellis; Aravind Sukumaran-Rajam; Srinivasan Parthasarathy 001; P. Sadayappan (KDD, 2020)

Efficient tiled sparse matrix multiplication through matrix signatures. - Süreyya Emre Kurt; Aravind Sukumaran-Rajam; Fabrice Rastello; P. Sadayappan (SC, 2020)

Analytical characterization and design space exploration for optimization of CNNs. - Rui Li 033; Yufan Xu; Aravind Sukumaran-Rajam; Atanas Rountev; P. Sadayappan (ASPLOS, 2021)

cuTS: scaling subgraph isomorphism on distributed multi-GPU systems using trie based data structure. - Lizhi Xiang; Arif Khan 005; Edoardo Serra; Mahantesh Halappanavar; Aravind Sukumaran-Rajam (SC, 2021)

Efficient Distributed Algorithms for Convolutional Neural Networks. - Rui Li 033; Yufan Xu; Aravind Sukumaran-Rajam; Atanas Rountev; P. Sadayappan (SPAA, 2021)

High-Performance Architecture Aware Sparse Convolutional Neural Networks for GPUs. - Lizhi Xiang; P. Sadayappan; Aravind Sukumaran-Rajam (PACT, 2022)

Effective Performance Modeling and Domain-Specific Compiler Optimization of CNNs for GPUs. - Yufan Xu; Qiwei Yuan; Erik Curtis Barton; Rui Li 033; P. Sadayappan; Aravind Sukumaran-Rajam (PACT, 2022)

Training of deep learning pipelines on memory-constrained GPUs via segmented fused-tiled execution. - Yufan Xu; Saurabh Raje; Atanas Rountev; Gerald Sabin; Aravind Sukumaran-Rajam; P. Sadayappan (CC, 2022)

Comprehensive Accelerator-Dataflow Co-design Optimization for Convolutional Neural Networks. - Miheer Vaidya; Aravind Sukumaran-Rajam; Atanas Rountev; P. Sadayappan (CGO, 2022)

Sparsity-Aware Tensor Decomposition. - Süreyya Emre Kurt; Saurabh Raje; Aravind Sukumaran-Rajam; P. Sadayappan (IPDPS, 2022)

High-Performance and Energy-Efficient 3D Manycore GPU Architecture for Accelerating Graph Analytics. - Dwaipayan Choudhury; Aravind Sukumaran-Rajam; Ananth Kalyanaraman; Partha Pratim Pande (ACM J. Emerg. Technol. Comput. Syst., 2022)

Software/Hardware Co-design of 3D NoC-based GPU Architectures for Accelerated Graph Computations. - Dwaipayan Choudhury; Reet Barik; Aravind Sukumaran-Rajam; Ananth Kalyanaraman; Partha Pratim Pande (ACM Trans. Design Autom. Electr. Syst., 2022)

Communication Optimization for Distributed Execution of Graph Neural Networks. - Süreyya Emre Kurt; Jinghua Yan; Aravind Sukumaran-Rajam; Prashant Pandey; P. Sadayappan (IPDPS, 2023)

A Performance Portability Study Using Tensor Contraction Benchmarks. - M. Emin Ozturk; Omid Asudeh; Gerald Sabin; P. Sadayappan; Aravind Sukumaran-Rajam (IPDPS Workshops, 2023)

TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition. - Lizhi Xiang; Miao Yin; Chengming Zhang 006; Aravind Sukumaran-Rajam; P. Sadayappan; Bo Yuan 001; Dingwen Tao (PPoPP, 2023)

cuAlign: Scalable Network Alignment on GPU Accelerators. - Lizhi Xiang; Arif M. Khan; S. M. Ferdous; Aravind Sukumaran-Rajam; Mahantesh Halappanavar (SC Workshops, 2023)

Accelerating Graph Computations on 3D NoC-Enabled PIM Architectures. - Dwaipayan Choudhury; Lizhi Xiang; Aravind Sukumaran-Rajam; Anantharaman Kalyanaraman; Partha Pratim Pande (ACM Trans. Design Autom. Electr. Syst., 2023)

Meta's Second Generation AI Chip: Model-Chip Co-Design and Productionization Experiences. - Joel Coburn; Chunqiang Tang; Sameer Abu Asal; Neeraj Agrawal; Raviteja Chinta; Harish Dattatraya Dixit; Brian Dodds; Saritha Dwarakapuram; Amin Firoozshahian; Cao Gao; Kaustubh Gondkar; Tyler Graf; et al. (ISCA, 2025)

ScaWL: Scaling k-WL (Weisfeiler-Lehman) Algorithms in Memory and Performance on Shared and Distributed-Memory Systems. - Coby Soss; Aravind Sukumaran-Rajam; Janet Layne; Edoardo Serra; Mahantesh Halappanavar; Assefaw H. Gebremedhin (ACM Trans. Archit. Code Optim., 2025)

cuTeSpMM: Accelerating Sparse-Dense Matrix Multiplication using GPU Tensor Cores. - Lizhi Xiang; Omid Asudeh; Gerald Sabin; Aravind Sukumaran-Rajam; P. Sadayappan (CoRR, 2025)