| Evolvegcn: Evolving graph convolutional networks for dynamic graphs A Pareja, G Domeniconi, J Chen, T Ma, T Suzumura, H Kanezashi, ... Proceedings of the AAAI conference on artificial intelligence 34 (04), 5363-5370, 2020 | 1292 | 2020 |
| There’s plenty of room at the Top: What will drive computer performance after Moore’s law? CE Leiserson, NC Thompson, JS Emer, BC Kuszmaul, BW Lampson, ... Science 368 (6495), eaam9744, 2020 | 527 | 2020 |
| A work-efficient parallel breadth-first search algorithm (or how to cope with the nondeterminism of reducers) CE Leiserson, TB Schardl Proceedings of the twenty-second annual ACM symposium on Parallelism in …, 2010 | 274 | 2010 |
| Scalable graph learning for anti-money laundering: A first look M Weber, J Chen, T Suzumura, A Pareja, T Ma, H Kanezashi, T Kaler, ... arXiv preprint arXiv:1812.00076, 2018 | 141 | 2018 |
| Tapir: Embedding fork-join parallelism into LLVM's intermediate representation TB Schardl, WS Moses, CE Leiserson Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of …, 2017 | 130 | 2017 |
| Ordering heuristics for parallel graph coloring W Hasenplaugh, T Kaler, TB Schardl, CE Leiserson Proceedings of the 26th ACM symposium on Parallelism in algorithms and …, 2014 | 126 | 2014 |
| On-the-fly pipeline parallelism ITA Lee, CE Leiserson, TB Schardl, Z Zhang, J Sukha ACM Transactions on Parallel Computing (TOPC) 2 (3), 1-42, 2015 | 93 | 2015 |
| Accelerating training and inference of graph neural networks with fast sampling and pipelining T Kaler, N Stathas, A Ouyang, AS Iliopoulos, T Schardl, CE Leiserson, ... Proceedings of Machine Learning and Systems 4, 172-189, 2022 | 68 | 2022 |
| Deterministic parallel random-number generation for dynamic-multithreading platforms CE Leiserson, TB Schardl, J Sukha ACM Sigplan Notices 47 (8), 193-204, 2012 | 68 | 2012 |
| The cilkprof scalability profiler TB Schardl, BC Kuszmaul, ITA Lee, WM Leiserson, CE Leiserson Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and …, 2015 | 50 | 2015 |
| Executing dynamic data-graph computations deterministically using chromatic scheduling T Kaler, W Hasenplaugh, TB Schardl, CE Leiserson ACM Transactions on Parallel Computing (TOPC) 3 (1), 1-31, 2016 | 40 | 2016 |
| Who needs crossings? Hardness of plane graph rigidity Z Abel, ED Demaine, ML Demaine, S Eisenstat, J Lynch, TB Schardl Dagstuhl Publishing, 2016 | 40 | 2016 |
| Brief announcement: Open cilk TB Schardl, ITA Lee, CE Leiserson Proceedings of the 30th on Symposium on Parallelism in Algorithms and …, 2018 | 34 | 2018 |
| Tapir: Embedding recursive fork-join parallelism into llvm’s intermediate representation TB Schardl, WS Moses, CE Leiserson ACM Transactions on Parallel Computing (TOPC) 6 (4), 1-33, 2019 | 31 | 2019 |
| Opencilk: A modular and extensible software infrastructure for fast task-parallel code TB Schardl, ITA Lee Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and …, 2023 | 26 | 2023 |
| Communication-efficient graph neural networks with probabilistic neighborhood expansion analysis and caching T Kaler, A Iliopoulos, P Murzynowski, T Schardl, CE Leiserson, J Chen Proceedings of Machine Learning and Systems 5, 477-494, 2023 | 25 | 2023 |
| The CSI framework for compiler-inserted program instrumentation TB Schardl, T Denniston, D Doucet, BC Kuszmaul, ITA Lee, CE Leiserson Proceedings of the ACM on Measurement and Analysis of Computing Systems 1 (2 …, 2017 | 23 | 2017 |
| Evolving graph convolutional networks for dynamic graphs J Chen, A Pareja, G Domeniconi, T Ma, T Suzumura, T Kaler, TB Schardl, ... US Patent 11,537,852, 2022 | 20 | 2022 |
| On the efficiency of localized work stealing W Suksompong, CE Leiserson, TB Schardl Information Processing Letters 116 (2), 100-106, 2016 | 20 | 2016 |
| Performance engineering of multicore software: Developing a science of fast code for the post-Moore era TB Schardl Massachusetts Institute of Technology, 2016 | 20 | 2016 |
Charles E. LeisersonMITVerified email at mit.edu
