| Efficient lipophilicity prediction of molecules employing deep-learning models R Datta, D Das, S Das Journal of Chemometrics and Intelligent Laboratory Systems 213, 2021 | 20 | 2021 |
| Function inlining versus function cloning D Das ACM SIGPLAN Notices 38 (6), 23-29, 2003 | 20 | 2003 |
| A practical and fast iterative algorithm for φ-function computation using DJ graphs D Das, U Ramakrishna ACM Transactions on Programming Languages and Systems (TOPLAS) 27 (3), 426-440, 2005 | 17 | 2005 |
| DeepBBBP: High Accuracy Blood‐brain‐barrier Permeability Prediction with a Mixed Deep Learning Model S Cherian Parakkal, R Datta, D Das Molecular Informatics 41 (10), 2100315, 2022 | 15 | 2022 |
| Method and apparatus for providing class hierarchy information for function devirtualization D Das US Patent 7,743,368, 2010 | 15 | 2010 |
| Deep Learning-based Approximate Graph-Coloring Algorithm for Register Allocation D Das, SA Ahmad, V Kumar LLVM-HPC Workshop (co-located with Supercomputing 2020), 2020 | 13 | 2020 |
| Experiences of using a dependence profiler to assist parallelization for multi-cores D Das, P Wu 2010 IEEE International Symposium on Parallel & Distributed Processing …, 2010 | 13 | 2010 |
| Compiler-controlled extraction of computation-communication overlap in MPI applications D Das, M Gupta, R Ravindran, W Shivani, P Sivakeshava, R Uppal 2008 IEEE International Symposium on Parallel and Distributed Processing, 1-8, 2008 | 11 | 2008 |
| Speeding up stl set/map usage in c++ applications D Das, M Valluri, M Wong, C Cambly SPEC International Performance Evaluation Workshop, 314-321, 2008 | 9 | 2008 |
| OpenMP technical report 1 on directives for attached accelerators E Stotzer, J Beyer, D Das, G Jost, P Raghavendra, J Leidel, A Duran, ... The OpenMP Architecture Review Board, Tech. Rep, 2012 | 7 | 2012 |
| Implementing Cross-Device Atomics in Heterogeneous Processors M Gupta, D Das, P Raghavendra, T Tye, L Lobachev, A Agarwal, ... IPDPSW, 659-668, 2015 | 6 | 2015 |
| High efficiency compilation framework for streamlining the execution of compiled code M Kandasamy, M Gupta, V Ranganathan, D Das US Patent 8,250,552, 2012 | 6 | 2012 |
| Process mapping in parallel computing D Das, N Kathiresan, R Ravindran, B Venkatsubramaniam US Patent 8,161,127, 2012 | 5 | 2012 |
| A new method for transparent fault tolerance of distributed programs on a network of workstations using alternative schedules D Das, P Dasgupta, PP Das Proceedings of 3rd International Conference on Algorithms and Architectures …, 1997 | 5 | 1997 |
| Scalable partial vectorization R Ramanarayanan, M Gupta, SS Chakraborty, D Das US Patent 9,158,511, 2015 | 4 | 2015 |
| A heuristic for the maximum processor requirement for scheduling layered task graphs with cloning D Das, P Dasgupta, PP Das Journal of Parallel and Distributed Computing 49 (2), 169-181, 1998 | 4 | 1998 |
| Learning to Combine Instructions in LLVM Compiler S Mannarswamy, D Das arXiv preprint arXiv:2202.12379, 2022 | 3 | 2022 |
| SPECNet: Predicting SPEC scores using deep learning D Das, P Raghavendra, A Ramachandran Companion of the 2018 ACM/SPEC International Conference on Performance …, 2018 | 3 | 2018 |
| Efficient liveness computation using merge sets and DJ-graphs D Das, B Dupont De Dinechin, R Upadrasta ACM Transactions on Architecture and Code Optimization (TACO) 8 (4), 1-18, 2012 | 3 | 2012 |
| Method and system for mpi_wait sinking for better computation-communication overlap in mpi applications D Das, M Gupta, R Ravindran, B Venkatsubramaniam US Patent App. 12/189,258, 2010 | 3 | 2010 |
Manish GuptaGoogle ResearchVerified email at google.com
