Zobrazeno 1 - 10
of 385
pro vyhledávání: '"Checconi, A."'
Autor:
Wu, Hanjiang, Xu, Huan, Park, Joongun, Tithi, Jesmin Jahan, Checconi, Fabio, Wolfson-Pou, Jordi, Petrini, Fabrizio, Krishna, Tushar
Influence Maximization (IM) is vital in viral marketing and biological network analysis for identifying key influencers. Given its NP-hard nature, approximate solutions are employed. This paper addresses scalability challenges in scale-out shared mem
Externí odkaz:
http://arxiv.org/abs/2411.09473
Multi-hop reasoning (MHR) is a process in artificial intelligence and natural language processing where a system needs to make multiple inferential steps to arrive at a conclusion or answer. In the context of knowledge graphs or databases, it involve
Externí odkaz:
http://arxiv.org/abs/2406.07727
Autor:
Laukemann, Jan, Helal, Ahmed E., Anderson, S. Isaac Geronimo, Checconi, Fabio, Soh, Yongseok, Tithi, Jesmin Jahan, Ranadive, Teresa, Gravelle, Brian J, Petrini, Fabrizio, Choi, Jee
High-dimensional sparse data emerge in many critical application domains such as cybersecurity, healthcare, anomaly detection, and trend analysis. To quickly extract meaningful insights from massive volumes of these multi-dimensional data, scientists
Externí odkaz:
http://arxiv.org/abs/2403.06348
In this short paper, we introduce the Ridgeline model, an extension of the Roofline model [4] for distributed systems. The Roofline model targets shared memory systems, bounding the performance of a kernel based on its operational intensity, and the
Externí odkaz:
http://arxiv.org/abs/2209.01368
Autor:
Chacon-Hurtado, Andrea, Ruhland, Fanny, Drabo, Salimata, Smeets, Thibaut, Checconi, Brice, Campos-Herrera, Raquel, Verheggen, François J.
Publikováno v:
In Journal of Invertebrate Pathology November 2024 207
Autor:
Nguyen, Andy, Helal, Ahmed E., Checconi, Fabio, Laukemann, Jan, Tithi, Jesmin Jahan, Soh, Yongseok, Ranadive, Teresa, Petrini, Fabrizio, Choi, Jee W.
Tensor decomposition (TD) is an important method for extracting latent information from high-dimensional (multi-modal) sparse data. This study presents a novel framework for accelerating fundamental TD operations on massively parallel GPU architectur
Externí odkaz:
http://arxiv.org/abs/2201.12523
SU3\_Bench is a microbenchmark developed to explore performance portability across multiple programming models/methodologies using a simple, but nontrivial, mathematical kernel. This kernel has been derived from the MILC lattice quantum chromodynamic
Externí odkaz:
http://arxiv.org/abs/2103.00571
Autor:
Helal, Ahmed E., Laukemann, Jan, Checconi, Fabio, Tithi, Jesmin Jahan, Ranadive, Teresa, Petrini, Fabrizio, Choi, Jeewhan
The analysis of high-dimensional sparse data is becoming increasingly popular in many important domains. However, real-world sparse tensors are challenging to process due to their irregular shapes and data distributions. We propose the Adaptive Linea
Externí odkaz:
http://arxiv.org/abs/2102.10245
Publikováno v:
Frontiers in Cellular and Infection Microbiology, Vol 14 (2024)
Externí odkaz:
https://doaj.org/article/2208f5b305a24767871fae26f24e3624
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.