02.11.2022

Four Papers accepted at SODA, ALENEX and HPEC

The Algorithm Engineering group is proud to contribute a paper to the Symposium on Discrete Algorithms (SODA) which will take place on January 22 - 25, 2023 in Florence. The paper by Kirill Simonov who recently joined our group, presents the first fixed-parameter algorithm for the problem of finding an (s, t)-path that contains at least k different colors, in a vertex-colored graph. The algorithm coincidentally improves the state of the art for the fundamental problem of finding an (s, t)-path of length at least k. The result also extends to the setting where colors are replaced by an arbitrary representable matroid over the vertex set.

Two more papers were accepted at Symposium on Algorithm Engineering and Experiments (ALENEX) which will be colocated with SODA. For the first one, our bachelor project partnered with TomTom developed the new routing algorithm SKARF, a combination of the skeleton dimension and Arc-Flags. On large real-world road networks it yields a reduction of 30% to 40% over the original Arc-Flags algorithm. The second paper was written by some of our Master students in the seminar Inferring infection chains. In the paper, the students implemented a previously unimplemented algorithm by Gabow et al. for computing directed minimum spanning trees and evaluated it against other algorithms om real-world and random graphs.

Additionally, a paper was accepted at this years IEEE conference on High Performance and Embedded Computing (HPEC), taking place virtually on September 19 - 23, 2022. The scope of the conference comprises computing hardware, software, systems and applications where performance matters. In this work, the authors research fault-tolerant training of machine learning models such as KMeans and GMM. They show that checkpointing the models' states during training using the new persistent memory NVRAM achieve a checkpointing speedup of a factor between 10 and 75x for KMeans and over 3x for GMM against the current state-of-the-art checkpointing approaches.

Clean Citation Style 005

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Fomin, Fedor V.; Golovach, Petr A.; Korhonen, Tuukka; Simonov, Kirill; Stamoulis Giannοs Fixed-Parameter Tractability of Maximum Colored Path and Beyond. Symposium on Discrete Algorithms (SODA) 2023

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Clean Citation Style 005

Khomutovskiy, Ivan; Dunker, Rebekka; Dierking, Jessica; Egbert, Julian; Helms, Christian; Schöllkopf, Finn; Casel, Katrin; Fischbeck, Philipp; Friedrich, Tobias; Isaac, Davis; Krogmann, Simon; Lenzner, Pascal Applying Skeletons to Speed Up the Arc-Flags Routing AlgorithmSIAM Symposium on Algorithm Engineering and Experiments (ALENEX) 2023: 110–122

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@inproceedings{casel2023applying,
  abstract = {The Single-Source Shortest Path problem is classically solved by applying Dijkstra's algorithm. However, the plain version of this algorithm is far too slow for real-world applications such as routing in large road networks. To amend this, many speed-up techniques have been developed that build on the idea of computing auxiliary data in a preprocessing phase, that is used to speed up the queries. One well-known example is the Arc-Flags algorithm that is based on the idea of precomputing edge flags to make the search more goal-directed. To explain the strong practical performance of such speed-up techniques, several graph parameters have been introduced. The skeleton dimension is one such parameter that has already been used to derive runtime bounds for some speed-up techniques. Moreover, it was experimentally shown to be low in real-world road networks. We introduce a method to incorporate skeletons, the underlying structure behind the skeleton dimension, to improve routing speed-up techniques even further. As a proof of concept, we develop new algorithms called SKARF and SKARF+ that combine skeletons with Arc-Flags, and demonstrate via extensive experiments on large real-world road networks that SKARF+ yields a significant reduction of the search space and the query time of about 30\% to 40\% over Arc-Flags. We also prove theoretical bounds on the query time of SKARF, which is the first time an Arc-Flags variant has been analyzed in terms of skeleton dimension.},
  author = {Khomutovskiy, Ivan and Dunker, Rebekka and Dierking, Jessica and Egbert, Julian and Helms, Christian and Schöllkopf, Finn and Casel, Katrin and Fischbeck, Philipp and Friedrich, Tobias and Isaac, Davis and Krogmann, Simon and Lenzner, Pascal},
  booktitle = {SIAM Symposium on Algorithm Engineering and Experiments (ALENEX)},
  keywords = {alenex davisissac katrincasel pascallenzner philippfischbeck simonkrogmann tobiasfriedrich year2023},
  pages = {110-122},
  title = {Applying Skeletons to Speed Up the Arc-Flags Routing Algorithm},
  year = 2023
}

Böther, Maximilian; Kißig, Otto; Weyand, Christopher Efficiently Computing Directed Minimum Spanning TreesSIAM Symposium on Algorithm Engineering and Experiments (ALENEX) 2023: 86–95

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Clean Citation Style 005

Wood, Andrew; Hershcovitch, Moshik; Ennmouri, Ilias; Zong, Weiyu; Chennuri, Saurav; Cohen, Sarel; Sundararaman, Swaminathan; Waddington, Daniel; Chin, Peter Towards Fast Crash-Consistent Cluster CheckpointingHigh Performance Extreme Computing Conference (HPEC) 2022

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@inproceedings{wood2022towards,
  abstract = {Machine Learning models are expensive to train: they require expensive high-compute hardware and have long training times. Therefore, models are extra sensitive to program faults or unexpected system crashes, which can erase hours if not days worth of work. While there are plenty of strategies designed to mitigate the risk of unexpected system downtime, the most popular strategy in machine learning is called checkpointing: periodically saving the state of the model to persistent storage. Checkpointing is an effective strategy, however, it requires carefully balancing two operations: how often a checkpoint is made (the checkpointing schedule), and the cost of creating a checkpoint itself. In this paper, we leverage Python Memory Manager (PyMM), which provides Python support for Persistent Memory and emerging Persistent Memory technology (Optane DC) to accelerate the checkpointing operation while maintaining crash consistency. We first show that when checkpointing models, PyMM with persistent memory can save from minutes to days of checkpointing runtime. We then further optimize the checkpointing operation with PyMM and demonstrate our approach with the KMeans and Gaussian Mixture Model algorithms on two real-world datasets: MNIST and MusicNet. Through evaluation, we show that these two algorithms achieve a checkpointing speedup of a factor between 10 and 75x for KMeans and over 3x for GMM against the current state-of-the-art checkpointing approaches. We also verify that our solution recovers from crashes, while traditional approaches cannot.},
  author = {Wood, Andrew and Hershcovitch, Moshik and Ennmouri, Ilias and Zong, Weiyu and Chennuri, Saurav and Cohen, Sarel and Sundararaman, Swaminathan and Waddington, Daniel and Chin, Peter},
  booktitle = {High Performance Extreme Computing Conference (HPEC)},
  keywords = {sarelcohen hpec year2022},
  title = {Towards Fast Crash-Consistent Cluster Checkpointing},
  year = 2022
}