483 citations · 1k across the 69 of their papers we have counts for
8 papers · 1 filter
Performance Foundations of Parallel & Distributed Reasoning Language Models
Maciej Besta, Leonard Schmidt, Lara Nonino +7
Reinforcement Learning with Verifiable Rewards (RLVR) and other RL-style post-training paradigms have been used for aligning large language models (LLMs) with reasoning standards.…
BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers
Patrik Okanovic, Sameer Deshmukh, Grzegorz Kwasniewski +8
The energy consumption of large-scale ML models is dominated by data movement, shuffling billions of parameters across memory hierarchies and data centers. Sparsification offers a…
Demystifying Higher-Order Graph Neural Networks
Maciej Besta, Florian Scheidl, Lukas Gianinazzi +4
Higher-order graph neural networks (HOGNNs) and the related architectures from Topological Deep Learning are an important class of GNN models that harness polyadic relations betwee…
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers
Maciej Besta, Afonso Claudino Catarino, Lukas Gianinazzi +4
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…
Cached Operator Reordering: A Unified View for Fast GNN Training
Julia Bazinska, Andrei Ivanov, Tal Ben-Nun +4
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,…
Neural Graph Databases
Maciej Besta, Patrick Iff, Florian Scheidl +5
Graph databases (GDBs) enable processing and analysis of unstructured, complex, rich, and usually vast graph datasets. Despite the large significance of GDBs in both academia and i…
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis
Maciej Besta, Torsten Hoefler
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph…
Learning Combinatorial Node Labeling Algorithms
Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden +3
We present a novel neural architecture to solve graph optimization problems where the solution consists of arbitrary node labels, allowing us to solve hard problems like graph colo…