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pro vyhledávání: '"Schardl, Tao B."'
Autor:
Kaler, Tim, Iliopoulos, Alexandros-Stavros, Murzynowski, Philip, Schardl, Tao B., Leiserson, Charles E., Chen, Jie
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 applications such as recommendation systems and financial forensics.
Externí odkaz:
http://arxiv.org/abs/2305.03152
Autor:
Kaler, Tim, Stathas, Nickolas, Ouyang, Anne, Iliopoulos, Alexandros-Stavros, Schardl, Tao B., Leiserson, Charles E., Chen, Jie
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 of computation and data movement due to the exponential growth of mul
Externí odkaz:
http://arxiv.org/abs/2110.08450
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 in a many-processor execution. Although tools exist for measuring mem
Externí odkaz:
http://arxiv.org/abs/1910.12340
Autor:
Schardl, Tao B., Samsi, Siddharth
This work introduces TapirXLA, a replacement for TensorFlow's XLA compiler that embeds recursive fork-join parallelism into XLA's low-level representation of code. Machine-learning applications rely on efficient parallel processing to achieve perform
Externí odkaz:
http://arxiv.org/abs/1908.11338
Autor:
Pareja, Aldo, Domeniconi, Giacomo, Chen, Jie, Ma, Tengfei, Suzumura, Toyotaro, Kanezashi, Hiroki, Kaler, Tim, Schardl, Tao B., Leiserson, Charles E.
Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With th
Externí odkaz:
http://arxiv.org/abs/1902.10191
Autor:
Weber, Mark, Chen, Jie, Suzumura, Toyotaro, Pareja, Aldo, Ma, Tengfei, Kanezashi, Hiroki, Kaler, Tim, Leiserson, Charles E., Schardl, Tao B.
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" in a human trafficking industry enslaving an estimated 40 million peopl
Externí odkaz:
http://arxiv.org/abs/1812.00076
Publikováno v:
Information Processing Letters, 116(2):100-106 (2016)
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, processors can benefit from working on their own work. Consequently, w
Externí odkaz:
http://arxiv.org/abs/1804.04773
Publikováno v:
Theory of Computing Systems, 58(2):223-240 (2016)
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 computation can be modeled by rooted trees. In particular, we show th
Externí odkaz:
http://arxiv.org/abs/1706.03184
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Publikováno v:
In Information Processing Letters February 2016 116(2):100-106
