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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
8762020
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
3502020
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
2672010
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
1222017
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
1152014
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
1032018
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
862015
Deterministic parallel random-number generation for dynamic-multithreading platforms
CE Leiserson, TB Schardl, J Sukha
ACM Sigplan Notices 47 (8), 193-204, 2012
672012
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
502015
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
412016
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
332022
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
332016
Brief announcement: Open cilk
TB Schardl, ITA Lee, CE Leiserson
Proceedings of the 30th on Symposium on Parallelism in Algorithms and …, 2018
322018
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
252019
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
242017
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
192015
On the efficiency of localized work stealing
W Suksompong, CE Leiserson, TB Schardl
Information Processing Letters 116 (2), 100-106, 2016
182016
Performance engineering of multicore software: Developing a science of fast code for the post-Moore era
TB Schardl
Massachusetts Institute of Technology, 2016
182016
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
92022
Finding a hamiltonian path in a cube with specified turns is hard
Z Abel, ED Demaine, ML Demaine, S Eisenstat, J Lynch, TB Schardl
Information and Media Technologies 8 (3), 685-694, 2013
92013
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