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Tao B. Schardl
Tao B. Schardl
Research Scientist in computer science, MIT CSAIL
Verified email at mit.edu - Homepage
Title
Cited by
Cited by
Year
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
11722020
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
4772020
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
2722010
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
1272017
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
1252014
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, 1-7, 2018
1242018
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
922015
Deterministic parallel random-number generation for dynamic-multithreading platforms
CE Leiserson, TB Schardl, J Sukha
ACM Sigplan Notices 47 (8), 193-204, 2012
722012
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
592022
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
512015
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
402016
Who needs crossings? Hardness of plane graph rigidity
Z Abel, ED Demaine, ML Demaine, S Eisenstat, J Lynch, TB Schardl
32nd International Symposium on Computational Geometry (SoCG 2016), 2016
372016
Brief announcement: Open cilk
TB Schardl, ITA Lee, CE Leiserson
Proceedings of the 30th on Symposium on Parallelism in Algorithms and …, 2018
332018
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
302019
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
252023
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
232017
Performance engineering of multicore software: Developing a science of fast code for the post-Moore era
TB Schardl
Massachusetts Institute of Technology, 2016
202016
Efficiently detecting races in cilk programs that use reducer hyperobjects
ITA Lee, TB Schardl
Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and …, 2015
202015
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
192023
On the efficiency of localized work stealing
W Suksompong, CE Leiserson, TB Schardl
Information Processing Letters 116 (2), 100-106, 2016
192016
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Articles 1–20