Monika Bambouskova, Laurent Gorvel, Vicky Lampropoulou, Alexey Sergushichev, Ekaterina Loginicheva, Kendall Johnson, Daniel Korenfeld, Mary Elizabeth Mathyer, Hyeryun Kim, Li-Hao Huang, Dustin Duncan, Howard Bregman, Abdurrahman Keskin, Andrea Santeford, Rajendra S. Apte, Raghav Sehgal, Britney Johnson, Gaya K. Amarasinghe, Miguel P. Soares, Takashi Satoh, Shizuo Akira, Tsonwin Hai, Cristina de Guzman Strong, Karine Auclair, Thomas P. Roddy, Scott A. Biller, Marko Jovanovic, Eynav Klechevsky, Kelly M. Stewart, Gwendalyn J. Randolph & Maxim N. Artyomov
1st International Conference on Computational Intelligence and Networks (CINE),
Irina Shchukina, Juhi Bagaitkar, Oleg Shpynov, Ekaterina Loginicheva, Sofia Porter, Denis A. Mogilenko, Erica Wolin, Patrick Collins, German Demidov, Mykyta Artomov, Konstantin Zaitsev, Sviatoslav Sidorov, Christina Camell, Monika Bambouskova, Laura Arthur, Amanda Swain, Alexandra Panteleeva, Aleksei Dievskii, Evgeny Kurbatsky, Petr Tsurinov, Roman Chernyatchik, Vishwa Deep Dixit, Marko Jovanovic, Sheila A. Stewart, Mark J. Daly, Sergey Dmitriev, Eugene M. Oltz, Maxim N. Artyomov
Recently proposed matrix multiplication based algorithm for context-free path querying (CFPQ) offloads the most performance-critical parts onto boolean matrices multiplication. Thus, it is possible to achieve high performance of CFPQ by means of modern parallel hardware and software. In this paper, we provide results of empirical performance comparison of different implementations of this algorithm on both real-world data and synthetic data for the worst cases.
Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA),