Blar i Department of Informatics på forfatter "Langguth, Johannes"
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Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural Networks
Langedal, Kenneth; Langguth, Johannes; Manne, Fredrik; Schroeder, Daniel Thilo (Journal article; Peer reviewed, 2022)Minimum weighted vertex cover is the NP-hard graph problem of choosing a subset of vertices incident to all edges such that the sum of the weights of the chosen vertices is minimum. Previous efforts for solving this in ... -
Enabling unstructured-mesh computation on massively tiled AI processors: An example of accelerating in silico cardiac simulation
Burchard, Luk Bjarne; Hustad, Kristian Gregorius; Langguth, Johannes; Cai, Xing (Journal article; Peer reviewed, 2023)A new trend in processor architecture design is the packaging of thousands of small processor cores into a single device, where there is no device-level shared memory but each core has its own local memory. Thus, both the ... -
Impacts of Covid-19 on Norwegian salmon exports: A firm-level analysis
Straume, Hans-Martin; Asche, Frank; Oglend, Atle; Abrahamsen, Eirik Bjorheim; Birkenbach, Anna M.; Langguth, Johannes; Lanquepin, Guillaume; Roll, Kristin Helen (Journal article; Peer reviewed, 2022)A rapidly growing literature investigates how the recent Covid-19 pandemic has affected international seafood trade along multiple dimensions, creating opportunities as well as challenges. This suggests that many of the ... -
Optimizing Approximate Weighted Matching on Nvidia Kepler K40
Naim, Md.; Manne, Fredrik; Halappanavar, Mahantesh; Tumeo, Antonino; Langguth, Johannes (Chapter; Peer reviewed, 2018)Matching is a fundamental graph problem with numerous applications in science and engineering. While algorithms for computing optimal matchings are difficult to parallelize, approximation algorithms on the other hand ... -
Targeting performance and user-friendliness: GPU-accelerated finite element computation with automated code generation in FEniCS
Trotter, James David; Langguth, Johannes; Cai, Xing (Journal article; Peer reviewed, 2023)This paper studies the use of automated code generation to provide user-friendly GPU acceleration for solving partial differential equations (PDEs) with finite element methods. By extending the FEniCS framework and its ...