72 citations · 107 across the 7 of their papers we have counts for
7 papers
Who Needs Crossings?: Noncrossing Linkages are Universal, and Deciding (Global) Rigidity is Hard
Zachary Abel, Erik D. Demaine, Martin L. Demaine +3
We exactly settle the complexity of graph realization, graph rigidity, and graph global rigidity as applied to three types of graphs: "globally noncrossing" graphs, which avoid cro…
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching
Tim Kaler, Alexandros-Stavros Iliopoulos, Philip Murzynowski +3
Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in…
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
Tim Kaler, Nickolas Stathas, Anne Ouyang +4
Improving the training and inference performance of graph neural networks (GNNs) is faced with a challenge uncommon in general neural networks: creating mini-batches requires a lot…
Cilkmem: Algorithms for Analyzing the Memory High-Water Mark of Fork-Join Parallel Programs
Tim Kaler, William Kuszmaul, Tao B. Schardl +1
Software engineers designing recursive fork-join programs destined to run on massively parallel computing systems must be cognizant of how their program's memory requirements scale…
Scalable Graph Learning for Anti-Money Laundering: A First Look
Mark Weber, Jie Chen, Toyotaro Suzumura +6
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people per year are "exported" i…
On the Efficiency of Localized Work Stealing
Warut Suksompong, Charles E. Leiserson, Tao B. Schardl
This paper investigates a variant of the work-stealing algorithm that we call the localized work-stealing algorithm. The intuition behind this variant is that because of locality,…
Upper Bounds on Number of Steals in Rooted Trees
Charles E. Leiserson, Tao B. Schardl, Warut Suksompong
Inspired by applications in parallel computing, we analyze the setting of work stealing in multithreaded computations. We obtain tight upper bounds on the number of steals when the…