Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Besta, Maciej"'
Autor:
Besta, Maciej, Scheidl, Florian, Gianinazzi, Lukas, Klaiman, Shachar, Müller, Jürgen, Hoefler, Torsten
Higher-order graph neural networks (HOGNNs) are an important class of GNN models that harness polyadic relations between vertices beyond plain edges. They have been used to eliminate issues such as over-smoothing or over-squashing, to significantly e
Externí odkaz:
http://arxiv.org/abs/2406.12841
Autor:
Besta, Maciej, Kubicek, Ales, Niggli, Roman, Gerstenberger, Robert, Weitzendorf, Lucas, Chi, Mingyuan, Iff, Patrick, Gajda, Joanna, Nyczyk, Piotr, Müller, Jürgen, Niewiadomski, Hubert, Chrapek, Marcin, Podstawski, Michał, Hoefler, Torsten
Retrieval Augmented Generation (RAG) enhances the abilities of Large Language Models (LLMs) by enabling the retrieval of documents into the LLM context to provide more accurate and relevant responses. Existing RAG solutions do not focus on queries th
Externí odkaz:
http://arxiv.org/abs/2406.05085
Autor:
Besta, Maciej, Paleari, Lorenzo, Kubicek, Ales, Nyczyk, Piotr, Gerstenberger, Robert, Iff, Patrick, Lehmann, Tomasz, Niewiadomski, Hubert, Hoefler, Torsten
Large Language Models (LLMs) are revolutionizing various domains, yet verifying their answers remains a significant challenge, especially for intricate open-ended tasks such as consolidation, summarization, and extraction of knowledge. In this work,
Externí odkaz:
http://arxiv.org/abs/2406.02524
Autor:
Baumann, Yves, Ben-Nun, Tal, Besta, Maciej, Gianinazzi, Lukas, Hoefler, Torsten, Luczynski, Piotr
Contemporary accelerator designs exhibit a high degree of spatial localization, wherein two-dimensional physical distance determines communication costs between processing elements. This situation presents considerable algorithmic challenges, particu
Externí odkaz:
http://arxiv.org/abs/2404.12953
Autor:
Gianinazzi, Lukas, Ziogas, Alexandros Nikolaos, Huang, Langwen, Luczynski, Piotr, Ashkboos, Saleh, Scheidl, Florian, Carigiet, Armon, Ge, Chio, Abubaker, Nabil, Besta, Maciej, Ben-Nun, Tal, Hoefler, Torsten
Publikováno v:
PPoPP'24: Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (2024) 404-416
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, d
Externí odkaz:
http://arxiv.org/abs/2402.19364
Autor:
Besta, Maciej, Memedi, Florim, Zhang, Zhenyu, Gerstenberger, Robert, Piao, Guangyuan, Blach, Nils, Nyczyk, Piotr, Copik, Marcin, Kwaśniewski, Grzegorz, Müller, Jürgen, Gianinazzi, Lukas, Kubicek, Ales, Niewiadomski, Hubert, O'Mahony, Aidan, Mutlu, Onur, Hoefler, Torsten
The field of natural language processing (NLP) has witnessed significant progress in recent years, with a notable focus on improving large language models' (LLM) performance through innovative prompting techniques. Among these, prompt engineering cou
Externí odkaz:
http://arxiv.org/abs/2401.14295
Autor:
Besta, Maciej, Catarino, Afonso Claudino, Gianinazzi, Lukas, Blach, Nils, Nyczyk, Piotr, Niewiadomski, Hubert, Hoefler, Torsten
Publikováno v:
Proceedings of Learning on Graphs (LOG), 2023
Many graph representation learning (GRL) problems are dynamic, with millions of edges added or removed per second. A fundamental workload in this setting is dynamic link prediction: using a history of graph updates to predict whether a given pair of
Externí odkaz:
http://arxiv.org/abs/2311.18526
Chiplet architectures are a promising paradigm to overcome the scaling challenges of monolithic chips. Chiplets offer heterogeneity, modularity, and cost-effectiveness. The design space of chiplet architectures is huge as there are many degrees of fr
Externí odkaz:
http://arxiv.org/abs/2311.06081
Autor:
Blach, Nils, Besta, Maciej, De Sensi, Daniele, Domke, Jens, Harake, Hussein, Li, Shigang, Iff, Patrick, Konieczny, Marek, Lakhotia, Kartik, Kubicek, Ales, Ferrari, Marcel, Petrini, Fabrizio, Hoefler, Torsten
Publikováno v:
Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI '24) Santa Clara, CA, USA April 16-18, 2024
Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption of low-diameter networks, we design, implement, deploy, and evaluate
Externí odkaz:
http://arxiv.org/abs/2310.03742
Autor:
Bazinska, Julia, Ivanov, Andrei, Ben-Nun, Tal, Dryden, Nikoli, Besta, Maciej, Shen, Siyuan, Hoefler, Torsten
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However, the sparse nature of GNN computation poses new challenges for perform
Externí odkaz:
http://arxiv.org/abs/2308.12093