P. (Saday) Sadayappan

P. (Saday) Sadayappan
Professor

School of Computing
University of Utah
Salt Lake City, UT 84112-9205
Email:saday_at_cs.utah.edu

Teaching

Research Interests

Current/Recent Projects

Enabling next generation machine learning for large scale image analysis NIH SBIR-Phase 2 (with RNET Technologies, Dayton, OH; Gerald Sabin (PI)), 2023-2025.
A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications NSF, 2022-2027.
AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE) (Utah PI for the multi-institutional project led by D.K. Panda at Ohio State University) NSF, 2021-2026.
Data Locality Optimization for Sparse Matrix/Tensor Computations NSF, 2020-2024.
A framework for solution of coupled partial differential equations on heterogeneous parallel systems (PI: Hari Sundar) NSF, 2020-2024.
Parallel Algorithm by Blocks - A Data-centric Compiler/runtime System for Productive Programming of Scalable Parallel Systems NSF, 2019-2023.
Tools for Productive High-Performance Computing with GPUs NSF, 2019-2023.
Performance Portable Framework for Developing Graph Applications DARPA SBIR-Phase 2 (with RNET Technologies, Dayton, OH), 2017-2022.
Towards Automated Characterization of the Data-Movement Complexity of Large Scale Analytics Applications, NSF, 2016-2019.
PARAGRAPH: Parallel, Scalable Graph Analytics, NSF, 2016-2019.
Whole-Program Adaptive Error Detection and Mitigation, DOE, 2015-2019 (Project PI: Sriram Krishnamoorthy, PNNL).
Improving Vectorization, NSF, 2014-2018.
Compiler/Runtime Support for Developing Scalable Parallel Multi-Scale Multi-Physics Applications, NSF, 2014-2018.

Selected Publications [More complete and up-to-date list from (DBLP) or (Google Scholar) ]

CGO '22 Comprehensive Accelerator-Dataflow Co-Design Optimization for Convolutional Neural Networks
Miheer Vaidya, Aravind Sukumaran-Rajam, Atanas Rountev, and P. Sadayappan
PLDI '21 IOOpt: Automatic Derivation of I/O Complexity Bounds for Affine Programs
Auguste Olivry, Guillaume Iooss, Nicolas Tollenaere, Atanas Rountev, P. Sadayappan, and Fabrice Rastello
ASPLOS '21 Analytical Characterization and Design Space Exploration for Optimization of CNNs
Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, and P. Sadayappan
SPAA '21 Brief Announcement: Efficient Distributed Algorithms for Convolutional Neural Networks
Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, and P. Sadayappan
SC '20 Efficient Tiled Sparse Matrix Multiplication Through Matrix Signatures
Sureyya Emre Kurt, Aravind Sukumaran-Rajam, Fabrice Rastello, and P. Sadayappan
SC '20 Scalable Heterogeneous Execution of a Coupled-Cluster Model with Perturbative Triples
Jinsung Kim, Ajay Panyala, Bo Peng, Karol Kowalski, P. Sadayappan, and Sriram Krishnamoorthy
SC '20 Compiling Generalized Histograms for GPU
Troels Henriksen, Sune Hellfritzsch, P. Sadayappan, and Cosmin Oancea
PLDI '20 Automated Derivation of Parametric Data Movement Lower Bounds for Affine Programs
Auguste Olivry, Julien Langou, Louis-Noel Pouchet, P. Sadayappan, and Fabrice Rastello
KDD '20 ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization
Gordon E. Moon, J. Austin Ellis, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, and P. Sadayappan
SC '19 Analytical Cache Modeling and Tilesize Optimization for Tensor Contractions
Rui Li, Aravind Sukumaran-Rajam, Richard Veras, Tze Meng Low, Fabrice Rastello, Atanas Rountev, and P. Sadayappan
SC '19 An Efficient Mixed-mode Representation of Sparse Tensors
Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Prashant Singh Rawat, Sriram Krishnamoorthy, and P. Sadayappan
CGO '19 A Code Generator for High-Performance Tensor Contractions on GPUs
Jinsung Kim, Aravind Sukumaran-Rajam, Vineeth Thumma, Sriram Krishnamoorthy, Ajay Panyala, Louis-Noel Pouchet, Atanas Rountev, and P. Sadayappan
PPOPP '19 Adaptive Sparse Tiling for Sparse Matrix Multiplication
Changwan Hong, Aravind Sukumaran-Rajam, Israt Nisa, Kunal Singh, and P. Sadayappan
PLDI '18 GPU Code Optimization using Abstract Kernel Emulation and Sensitivity Analysis
Changwan Hong, Aravind Sukumaran-Rajam, Jinsung Kim, Prashant Singh Rawat, Sriram Krishnamoorthy, Louis-Noel Pouchet, Fabrice Rastello, and P. Sadayappan
HPDC '18 Efficient Sparse-Matrix Multi-Vector Product on GPUs
Changwan Hong, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Jinsung Kim, Sureyya Emre Kurt, Israt Nisa, Shivani Sabhlok, Umit Catalyu¼rek, Srinivasan Parthasarathy, and P. Sadayappan
ICS '18 Optimizing Tensor Contractions in CCSD(T) for Efficient Execution on GPUs
Jinsung Kim, Aravind Sukumaran Rajam, Changwan Hong, Ajay Panyala, Rohit Srivastava, Sriram Krishnamoorthy, and P. Sadayappan
PPOPP '18 Register optimizations for stencils on GPUs
Prashant Rawat, Fabrice Rastello, Louis-Noel Pouchet, Atanas Rountev, and P. Sadayappan
POPL '18 Analytical Modeling of Cache Behavior for Affine Programs
Wenlei Bao, Sriram Krishnamoorthy, Louis-Noel Pouchet, Fabrice Rastello, and P. Sadayappan
PACT '17 MultiGraph: Efficient Graph Processing on GPUs
Changwan Hong, Aravind Sukumaran-Rajam, Jinsung Kim, and P. Sadayappan
ICS '17 On Improving Performance of Sparse Matrix-Matrix Multiplication on GPUs
Rakshith Kunchum, Ankur Chaudhry, Aravind Sukumaran-Rajam, Qingpeng Niu, Israt Nisa, and P. Sadayappan
PPOPP '17 Optimizing the Four-Index Integral Transform Using Data Movement Lower Bounds Analysis
Samyam Rajbhandari, Fabrice Rastello, Karol Kowalski, Sriram Krishnamoorthy, and P. Sadayappan
PACT '16 Resource Conscious Reuse-Driven Tiling for GPUs
Prashant Rawat, Changwan Hong, Mahesh Ravishankar, Vinod Grover, Louis-Noel Pouchet, Atanas Rountev, and P. Sadayappan
SC '16 A Domain-Specific Compiler for a Parallel Multiresolution Adaptive Numerical Simulation Environment
Samyam Rajbhandari, Jinsung Kim, Sriram Krishnamoorthy, Louis-Noel Pouchet, Fabrice Rastello, Robert J. Harrison, and P. Sadayappan
PLDI '16 Effective Padding of Multidimensional Arrays to Avoid Cache Conflict Misses
C. Hong, W. Bao, A. Cohen, S. Krishnamoorthy, L.-N. Pouchet, F. Rastello, J. Ramanujam, and P. Sadayappan
POPL '16 PolyCheck: Dynamic Verification of Iteration Space Transformations on Affine Programs
Wenlei Bao, Sriram Krishnamoorthy, Louis-Noel Pouchet, Fabrice Rastello, and P. Sadayappan
POPL '15 On Characterizing the Data Access Complexity of Programs
Venmugil Elango, Fabrice Rastello, Louis-Noel Pouchet, J. Ramanujam, and P. Sadayappan
PPOPP '15 Distributed Memory Code Generation for Mixed Irregular/Regular Computations
Mahesh Ravishankar, Roshan Dathathri, Venmugil Elango, Louis-Noel Pouchet, J. Ramanujam, Atanas Rountev, and P. Sadayappan
PPOPP '15 On Optimizing Machine Learning Workloads via Kernel Fusion
Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, and P. Sadayappan
SC '14 A Communication-Optimal Framework for Contracting Distributed Tensors
Samyam Rajbhandari, Akshay Nikam, Pai-Wei Lai, Kevin Stock, Sriram Krishnamoorthy, and P. Sadayappan
PLDI '14 A Framework for Enhancing Data Reuse via Associative Reordering
Kevin Stock, Martin Kong, Tobias Grosser, Louis-Noel Pouchet, Fabrice Rastello, J. Ramanujam, and P. Sadayappan
PLDI '14 Compiler-Assisted Detection of Transient Memory Errors
Sanket Tavarageri, Sriram Krishnamoorthy, and P. Sadayappan
SPAA '14 On Characterizing the Data Movement Complexity of Computational DAGs for Parallel Execution
Venmugil Elango, Fabrice Rastello, Louis-Noel Pouchet, J. Ramanujam, and P. Sadayappan
SC '13 A Framework for Load Balancing of Tensor Contraction Expressions via Dynamic Task Partitioning
Pai-Wei Lai, Kevin Stock, Samyam Rajbhandari, Sriram Krishnamoorthy, and P. Sadayappan
PLDI '13 When Polyhedral Transformations Meet SIMD Code Generation
Martin Kong, Richard Veras, Kevin Stock, Franz Franchetti, Louis-Noel Pouchet, and P. Sadayappan
PLDI '12 Dynamic Trace-Based Analysis of Vectorization Potential of Applications
Justin Holewinski, Ragavendar Ramamurthi, Mahesh Ravishankar, Naznin Fauzia, Louis-Noel Pouchet, Atanas Rountev, and P. Sadayappan
POPL '11 Loop Transformations: Convexity, Pruning and Optimization
Louis-Noel Pouchet, Uday Bondhugula, Cedric Bastoul, Albert Cohen, J. Ramanujam, P. Sadayappan, and Nicolas Vasilache
CC '10 Automatic C-to-CUDA Code Generation for Affine Programs
Muthu Baskaran, J. Ramanujam, and P. Sadayappan
SC '09 Scalable Work Stealing
James Dinan, Brian Larkins, Sriram Krishnamoorthy, Jarek Nieplocha, and P. Sadayappan
ICS '08 A Compiler Framework for Optimization of Affine Loop Nests for GPGPUs
Muthu Baskaran, Uday Bondhugula, Sriram Krishnamoorthy, J. Ramanujam, Atanas Rountev, and P. Sadayappan
PLDI '08 A Practical Automatic Polyhedral Parallelizer and Locality Optimizer (recipient of ACM SIGPLAN Most Influential PLDI Paper Award in 2018)
Uday Bondhugula, Albert Hartono, J. Ramanujam, and P. Sadayappan
PLDI '07 Effective Automatic Parallelization of Stencil Computations
Sriram Krishnamoorthy, Muthu Baskaran, Uday Bondhugula, J. Ramanujam, Atanas Rountev, and P. Sadayappan
Proceedings IEEE '05 Synthesis of High-Performance Parallel Programs for a Class of Ab Initio Quantum Chemistry Models
Gerald Baumgartner, Alexander Auer, David E Bernholdt, Alina Bibireata, Venkatesh Choppella, Daniel Cociorva, Xiaoyang Gao, Robert J Harrison, So Hirata, Sriram Krishnamoorthy, Sandhya Krishnan, Chi-Chung Lam, Qingda Lu, Marcel Nooijen, Russell M Pitzer, J Ramanujam, Alex Sibiryakov, and P. Sadayappan