Publications reporting eX3 research

Whenever you publish research based on the eX3 infrastructure, please notify us by email to ex3-contact@simula.no. Moreover, in the acknowledgment section of your paper, please include the following statement or similar:

Journal papers

2023

V. Thambawita, S. A. Hicks, A. M. Storås, et al., “VISEM-Tracking, a human spermatozoa tracking dataset”, Sci Data, vol. 10, 2023. (DOI)

2202

A. Thune, S. -A. Reinemo, T. Skeie and X. Cai, “Detailed Modeling of Heterogeneous and Contention-Constrained Point-to-Point MPI Communication”, IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 5, pp. 1580-1593, 2023 (DOI)

B. Luk, K. G. Hustad, J. Langguth and X. Cai, “Enabling unstructured-mesh computation on massively tiled AI processors: An example of accelerating in silico cardiac simulation”, Frontiers in Physics, 2023 (DOI)

A. Souche and K. Valen-Sendstad, “High-fidelity fluid structure interaction simulations of turbulent-like aneurysm flows reveals high-frequency narrowband wall vibrations: A stimulus of mechanobiological relevance?”, Journal of Biomechanics, 2022 (DOI)

K. G. Hustad and X. Cai, “Resource-Efficient Use of Modern Processor Architectures For Numerically Solving Cardiac Ionic Cell Models,” Front. Physiol., 2022 (DOI)

J. Trotter, X. Cai, and S. W. Funke, “On Memory Traffic and Optimisations for Low-order Finite Element Assembly Algorithms on Multi-core CPUs”, ACM Transactions on Mathematical Software 48:2, pp. 1–31, 2022 (DOI)

Marie Roald, Carla Schenker, Vince D. Calhoun, Tülay Adalı, Rasmus Bro, Jeremy E. Cohen and Evrim Acar, “An AO-ADMM approach to constraining PARAFAC2 on all modes”, SIAM Journal on Mathematics of Data Science. (In review)

R. Khadga, D. Jha, S. Ali, S. Hicks, V. Thambawita, M. A. Riegler, and P. Halvorsen, “Meta-learning with implicit gradients in a few-shot setting for medical image segmentation”, Computers in Biology and Medicine, 105227, 2022. (DOI)

2021

Debesh Jha, Sharib Ali, Håvard D Johansen, Dag D Johansen, Jens Rittscher, Michael A Riegler, and Pål Halvorsen, “Real-Time Polyp Detection, Localisation and Segmentation in Colonoscopy Using Deep Learning”, IEEE Access, pp. 40496-40510, 2021 (DOI)

O. A. N. Rongved, M. Stige, S. Hicks, V. Thambawita, C. Midoglu, E. Zouganeli, D. Johansen, M. Riegler and P. Halvorsen, “Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities”, Machine Learning and Knowledge Extraction, 3(4), pp. 1030-1054, 2021. (DOI)

Vajira Thambawita, et al., “SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation”, 2021 (In review)

V. Thambawita, I. Strümke, S. Hicks, P. Halvorsen, S. Parasa, and M. Riegler, “Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images”, Diagnostics, 11(12), 2021. (DOI)

J. O. Valand, H. Kadragic, S. Hicks, V. Thambawita, C. Midoglu, T. Kupka, D. Johansen, M. Riegler, and P. Halvorsen, “AI-Based Video Clipping of Soccer Events”, Machine Learning and Knowledge Extraction, 3(4), pp. 990-1008, 2021. (DOI)

James D. Trotter, Xing Cai, and Simon Funke, “On memory traffic and optimisations for low-order finite element assembly algorithms on multi-core CPUs”, ACM Transactions on Mathematical Software, 2021. (Accepted)

Q. Do, S. A. M. Acuña, J. I. Kristiansen, K. Agarwal, P. H. Ha, “Highly Efficient and Scalable Framework for High-Speed Super-Resolution Microscopy”, IEEE Access (9), pp. 97053 - 97067, 2021. (DOI)

N. K. Tomar, et al., “FANet: A feedback attention network for improved biomedical image segmentation”, IEEE Transactions on Neural Networks and Learning Systems, 2021. (In review, arXiv preprint) 

D. Jha, et al., “A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging”, Medical image analysis, 70, 2021. (DOI)

A. Srivastava, et al., “MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation”, IEEE Journal of Biomedical and Health informatics, 2021. (DOI).

S. Ali, et al., PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment, Scientific Data, 2021. (In review).

P. H. Smedsrud, et al., “Kvasir-Capsule, a video capsule endoscopy dataset”, Scientific Data, 8(1), pp. 1-10, 2021. (DOI)

Ming-Chang Lee, Jia-Chun Lin, and Ernst Gunnar Gran, “DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Network,” Transportation Research Record, 2021 (DOI)

2020

H. Borgli, et al., “HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy”, Scientific Data, 7(1), pp. 1-14, 2020. (DOI)

D. Jha, P. H. Smedsrud, D. Johansen, T. d Lange, H. D. Johansen, P. Halvorsen, and M. A. Riegler, “A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation”, IEEE Journal of Biomedical and Health Informatics, 2020 (DOI)

Tobias Ross, et al., “Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge”, Medical Image Analysis, 2020 (DOI)

Karoline Horgmo Jæger, Kristian Gregorius Hustad, Xing Cai, and Aslak Tveito, “Efficient numerical solution of the EMI model representing the extracellular space (E), cell membrane (M) and intracellular space (I) of a collection of cardiac cells”, Frontiers in Physics, 2020 (DOI)

James David Trotter, Johannes Langguth, and Xing Cai, “Cache simulation for irregular memory traffic on multi-core CPUs: case study on performance models for sparse matrix-vector multiplication”, Journal of Parallel and Distributed Computing, Volume 144, pp. 189-205, 2020 (DOI)

A. Bergersen, A. Slyngstad, S. Gjertsen, A. Souche, and K. Valen-Sendstad, "turtleFSI: A Robust and Monolithic FEniCS-based Fluid-Structure Interaction Solver”, Journal of Open Source Software, 5(50):2089, 2020 (DOI)

2019

P. H. Ha, O. J. Anshus, and I. Umar, “Efficient Concurrent Search Trees Using Portable Fine-Grained Locality”, IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 7, pp. 1580-1595, 2019 (DOI)

Peer-reviewed Proceedings papers and BOOK CHAPTERS

2023

A. Srivastava, D. Jha, B. Aydogan, M. E. Abazeed, U. Bagci, “Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation”. Head and Neck Tumor Segmentation and Outcome Prediction - HECKTOR 2022. (DOI)

2022

A. Maulana, K. Pogorelov, D. T. Schroeder, J. Langguth, “Graph Neural Network for Fake News Detection and Classification of Unlabelled Nodes”, MediaEval, 2022. (Link)

J. Moe, K. Pogorelov, D. T. Schroeder, J. Langguth, “Implementing Spatio-Temporal Graph Convolutional Networks on Graphcore IPUs”, 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 45-54, 2022. (DOI)

K. Pogorelov, D. T. Schroeder, S. Brenner, A. Maulana, J. Langguth, “Combining Tweets and Connections Graph for FakeNews Detection”, MediaEval, 2022. (DOI)

Huber, D. T. Schroeder, K. Pogorelov, C. Griwodz and J. Langguth, "A Streaming System for Large-scale Temporal Graph Mining of Reddit Data," 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 1153-1162, 2022. (DOI).

K. Langedal, J. Langguth, F. Manne, D. T. Schroeder, “Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural Networks”, 20th International Symposium on Experimental Algorithms (SEA 2022), pp. 12:1-12:7, 2022. (DOI)

J. Rocher-González, E. G. Gran, S.-A. Reinemo, T. Skeie, J. Escudero-Sahuquillo, P. J. García, F. J. Quiles Flor, “Adaptive Routing in InfiniBand Hardware”, CCGRID, IEEE, pp. 463-472, 2022. (DOI)

D. Marques, R. Campos, S. Santander-Jiménez, Z. Matveev, L. Sousa, and A. Ilic., “Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three-way Gene Interaction Study”, 36th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2022. (accepted, arXiv preprint)

A. Srivastava, S. Chanday, D. Jha, U. Pal, and S. Ali, “GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation”, ICPR 2022, 2021. (In review, arXiv preprint).

2021

K. Pogorelov, D. T. Schroeder, S. Brenner, J. Langguth, “FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task”, MediaEval, 2021.

V. Thambawita, et al., “DeepSynthBody: the beginning of the end for data deficiency in medicine”, The International Conference on Applied Artificial Intelligence (ICAPAI), pp. 1-8, 2021. (DOI)

J. Valand, H. Kadragic, S. Hicks, V. Thambawita, C. Midoglu, T. Kupka, D. Johansen, M. Riegler, and P. Halvorsen, “Automated Clipping of Soccer Events using Machine Learning”, IEEE International Symphosium of Multimedia (ISM), 2021. (DOI)

V. Thambawita, S. Hicks, P. Halvorsen, and M. Riegler, “DivergentNets: Medical Image Segmentation by Network Ensemble”, EndoCV, 2021.

J. Langguth, I Panagiotas, and B. Uçar, “Shared-memory implementation of the Karp-Sipser kernelization process”, 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC), 2021. (DOI)

J. Langguth, A. Tumanis, and A. Azad, “K. Pogorelov, et al., “Incremental Clustering Algorithms for Massive Dynamic Graphs”, 2021 International Conference on Data Mining Workshops (ICDMW), 2021. (DOI)

K. Pogorelov, D. T. Schroeder, P. Filkuková, S. Brenner, and J. Langguth, “WICO Text: A Labeled Dataset of Conspiracy Theory and 5G-Corona Misinformation Tweets”, OASIS '21: Proceedings of the 2021 Workshop on Open Challenges in Online Social Networks, 2021. (DOI)

P. Bernabé, A. Gotlieb, B. Legeard, F. O. Sem-Jacobsen, and H. Spieker, “Apprentissage auto-supervisé pour la détection d’actions illégales lors de la surveillance du trafic maritime”, Conférence Nationale d’Intelligence Artificielle Année, 2021.

A. Srivastava, S. Chanda, D. Jha, M. A. Riegler, P. Halvorsen, D. Johansen, and U. Pal, “PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation”, Proceedings of the 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), 2021. (DOI)

L. Burchard, X. Cai, and J. Langguth, "iPUG for Multiple Graphcore IPUs: Optimizing Performance and Scalability of Parallel Breadth-First Search," 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC), 2021. (DOI)

S. Dhandhania, A. Deodhar, K. Pogorelov, S. Biswas, and J. Langguth, “Explaining the Performance of Supervised and Semi-Supervised Methods for Automated Sparse Matrix Format Selection”, DUAC workshop at the International Conference on Parallel Processing (ICPP'21), 2021.

L. Burchard, J. Moe, D. T. Schroeder, K. Pogorelov, J. Langguth, “iPUG: Accelerating Breadth-First Graph Traversals Using Manycore Graphcore IPUs”, in Chamberlain B.L., Varbanescu AL., Ltaief H., Luszczek P. (eds) High Performance Computing. ISC High Performance 2021, Lecture Notes in Computer Science, vol 12728. Springer, 2021. (DOI)

K. H. Jæger, K.G. Hustad, X. Cai, A. Tveito, “Operator Splitting and Finite Difference Schemes for Solving the EMI Model”, in Tveito A., Mardal KA., Rognes M.E. (eds) Modeling Excitable Tissue, Simula SpringerBriefs on Computing, vol 7. Springer, 2021. (DOI)

N. K. Tomar, N.  Ibtehaz, D. Jha, P. Halvorsen, S.  Ali, “Improving Generalizability in Polyp Segmentation using Ensemble Convolutional Neural Network,” in Proceedings of the 3rd International Workshop and Challenge on Computer  Vision in Endoscopy (EndoCV 2021), 2021. (URL)

D. Jha, S. Ali, N. K. Tomar, M. A. Riegler, D. Johansen, H. D. Johansen, and P. Halvorsen, “Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy”, in IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 1-4, 2021. (DOI)

D. Jha, et al., “NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy”, IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp.37-43, 2021. (DOI)

S. A. Hicks, D. Jha, V. Thambawita, P. Halvorsen, H. L. Hammer, and M. A. Riegler, “The EndoTect 2020 challenge: evaluation and comparison of classification, segmentation and inference time for endoscopy”, in International Conference on Pattern Recognition, pp. 263-274, Springer, 2021.

Ming-Chang Lee, Jia-Chun Lin, and Ernst Gunnar Gran, “SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series”, IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 2021. (arXiv preprint)

Ming-Chang Lee, Jia-Chun Lin, and Ernst Gunnar Gran, “How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?”, Proceedings of the 35th International Conference on Advanced Information Networking and Applications (AINA 2021), pp. 136-148, 2021. (arXiv preprint)

D. T. Schroeder, F. Schaal, P. Filkukova, K. Pogorelov, and J. Langguth, “WICO Graph: A Labeled Dataset of Twitter Subgraphs based on Conspiracy Theory and 5G-Corona Misinformation Tweets”, 2021.

Michela Bozzetto, Alban Souche, Andrea Remuzzi, and Kristian Valen-Sendstad, “Turbulent-like Arteriovenous Fistula Flows Cause Wall Vibrations; A Specific Stimulus For Stenosis Formation?”, ESBiomech, 2021.

Debesh Jha, et al., “Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy”, Multimedia Modeling, 2021. (DOI)

2020

R. Khadka, “Transfer of Knowledge: Fine-tuning for Polyp Segmentation with Attention”, MediaEval, 2020. (Link)

L. Burchard et al., “A Scalable System for Bundling Online Social Network Mining Research”, 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1-6, 2020. (DOI)

V. Thambawita, S. Hicks, P. Halvorsen, and M. Riegler, “Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus”, MediaEval 2020, 2020.

O. A. N. Rongved, S. Hicks, V. Thambawita, H. K. Stensland, E. Zouganeli, D. Johansen, M. Riegler, and P. Halvorsen, “Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks”, 2020 IEEE International Symposium on Multimedia (ISM), 2020. (DOI)

K. Pogorelov, D. T. Schroeder, P. Filkukova, and J. Langguth, “A System for High Performance Mining on GDELT Data”, IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 1101-1111, 2020.

L. Burchard, D. T. Schroeder, S. Becker, and J. Langguth, “Resource Efficient Algorithms for Message Sampling in Online Social Networks”, Seventh IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1-8, 2020.

K. Pogorelov, D. T. Schroeder, L. Burchard, J. Moe, S. Brenner, P. Filkukova, and J. Langguth, “FakeNews: Corona Virus and 5G Conspiracy Task”, MediaEval, 2020.

D. T. Schroeder, K. Pogorelov, and J. Langguth, “Evaluating Standard Classifiers for Detecting COVID-19 related Misinformation”, MediaEval, 2020.

Nikhil Kumar Tomar, Debesh Jha, Sharib Ali, Håvard D. Johansen, Dag Johansen, Michael A. Riegler, and Pål Halvorsen, “DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation”, MediaEval, 2020.

S. Alam, N. K. Tomar, A. Thakur, D. Jha, and A. Rauniyar, “Automatic Polyp Segmentation using U-Net-ResNet50”, MediaEval, 2020.

Debesh Jha, Anis Yazidi, Michael A. Riegler, Dag Johansen, Håvard D. Johansen, and Pål Halvorsen, “LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification”, PDCAT-PAAP, 2020.

Debesh Jha, Michael A. Riegler, Dag Johansen, Pål Halvorsen, and Håvard D. Johansen, “DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation”, IEEE 33rd International Symposium on Computer Based Medical Systems (CBMS), 2020.

Ming-Chang Lee, Jia-Chun Lin, and Ernst Gunnar Gran, "ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series", IEEE 44th Computer Society Signature Conference on Computers, Software, and Applications (COMPSAC), 2020 (arXiv).

Ming-Chang Lee, Jia-Chun Lin, and Ernst Gunnar Gran, “Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing”, 26th International European Conference on Parallel and Distributed Computing (EURO-PAR), Springer, 2020 (arXiv).

Håvard Heitlo Holm, Martin Lilleeng Sætra and André Rigland Brodtkorb, "Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs", Finite Volumes for Complex Applications IX, 2020.

Debesh Jha, Pia Smedsrud, Michael Riegler, Pål Halvorsen, Håvard D. Johansen, Thomas De Lange, and Dag Johansen,  “Kvasir-SEG: A Segmented Polyp Dataset”, 26th International conference on multimedia and modeling, 2020. 

Ming-Chang Lee, Jia-Chun Lin, and Ernst Gunnar Gran, “RePAD: Real-time Proactive Anomaly Detection for Time Series,” 34th International Conference on Advanced Information Networking and Applications (AINA), 2020 (arXiv).

Ming-Chang Lee and Jia-Chun Lin, “DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction,” 34th International Conference on Advanced Information Networking and Applications (AINA), 2020 (arXiv).

Alban Souche and Kristian Valen-Sendstad, “Turbulent-like aneurysm flows trigger distinct high-frequent wall vibrations”, 14th WCCS ECCOMAS Congress, 2020.

Debesh Jha, Pia Smedsrud, Michael Riegler, Håvard D. Johansen, Dag Johansen, Thomas De Lange, and Pål Halvorsen, “ResUNet++: An Advanced Architecture for Medical Image Segmentation”,  21st IEEE Symposium on Multimedia, 2020.

2019

D. Castro, P. Romano, A. Ilic, and A. M. Khan, “HeTM: Transactional Memory for Heterogeneous Systems”, 28th International Conference on Parallel Architectures and Compilation Techniques (PACT), 2019

P. Tunstad, A. M. Khan, and P. H. Ha, “HyperProv: Decentralized Resilient Data Provenance at the Edge with Blockchains”, Middleware '19, 2019.

Debesh Jha, Michael Riegler, and Pål Halvorsen, “A RASNet-based deep learning approach for the Binary Segmentation Task in the 2019 ROBUST-MIS Challenge”, Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019, Endoscopic vision challenge (part of MICCAI), 2019.

Vajira Thambawita, Pål Halvorsen, Hugo Hammer, Michael Riegler, and Trine B. Haugen, “Stacked dense optical flows and dropout layers to predict sperm motility and morphology”, MediaEval, 2019.

Vajira Thambawita, Pål Halvorsen, Hugo Hammer, Michael Riegler, and Trine B. Haugen “Extracting temporal features into a spatial domain using autoencoders for sperm video analysis”, MediaEval, 2019.

PhD Theses

This list is reserved for PhD theses in HPC that are fully based on use of eX3. In addition, there are several tens of other PhD theses containing studies based on eX3. Such contributions are listed above as journal or conference papers.

2022

Debesh Jha, “Machine Learning-based Classification, Detection, and Segmentation of Medical Images”, PhD Thesis, University of Tromsø, 2022.

Daniel Thilo Schröder, “Explaining News Spreading Phenomena in Social Networks”, PhD Thesis, TU Berlin, 2022.

2021

Vajira Thambawita, “DeepSynthBody: the beginning of the end for data deficiency in medicine”, PhD Thesis, Oslo Metropolitan University, 2021.

James Trotter, “High-performance finite element computations. Performance modelling, optimisation, GPU acceleration & automated code generation”, PhD Thesis, University of Oslo, 2021.

Master Theses

2022

Birk Sebastian Frostelid Torpmann-Hagen, “On the Generalizability of Deep Learning-based Medical Image Segmentation Methods”, MSc Thesis, University of Oslo, 2022.

Jakob Skrede and Sigurd Sonne, “MPI Over PCI Express Networks”, MSc Thesis, University of Oslo, 2022.

Johannes Moe, “High Performance Computing on Intelligence Processing Units: Accelerator Performance Analysis on Spatio-Temporal Graph Convolutional Networks”, MSc Thesis, University of Oslo, 2022.

2021

Bernhard Nornes Lotsberg, “LSTM Models Applied on Hydrological Time Series And Their Potential Physical Implications”, MSc Thesis, University of Oslo, 2021.

Haris Kadragic, “Machine learning-based approach for automated clipping of soccer events - using scene boundary and logo detection”, MSc Thesis, University of Oslo, 2021.

Joakim Olav Valand, “Machine learning-based approach for automated clipping of soccer events - using scene boundary and logo detection”, MSc Thesis, University of Oslo, 2021.

Rabindra Khadka, “Meta-Learning for Medical Image Segmentation”, MSc Thesis, Oslo Metropolitan University, 2021.

Svein Gunnar Fagerheim, “Benchmarking Persistent Memory with Respect to Performance and Programmability”, MSc Thesis, University of Oslo, 2021.

Simen Håpnes, “Solving Partial Differential Equations by the Finite Difference Method on a Specialized Processor”, MSc Thesis, University of Oslo, 2021.

Christian Stafset, “Connectivity algorithms on multiple GPUs”, MSc Thesis, University of Bergen, 2021.

Eirik Haugen, “Investigating the effects of dynamic approximation methods on machine learning (ML) algorithms running on ML-specialized platforms”, MSc Thesis, University of Tromsø, 2021.

Andreas Huber, “Observing Reddit’s Interaction Network: A stream-based approach for large scale Network Analysis on Reddit”, MSc Thesis, University of Oslo, 2021.

Nora Elisabeth Qi Eck Pålsrud, “Exploring Neural Machine Translation Architectures for Automated Code Repair”, MSc Thesis, University of Oslo, 2021.

Simen Mailund Svendsen, “In Search of Lost Time. A Deep Dive in Overlapping Computation and Communication in Memory Bound MPI Applications”, MSc Thesis, University of Oslo, 2021.

Aigars Tumanis, “Graph Clustering for Long Term Twitter Observations Community Detection in Incremental Graphs”, MSc Thesis, University of Oslo, 2021.

Luk Burchard, “Accelerating Breadth First traversals using AI accelerators”, MSc Thesis, TU Berlin, 2021.

2020

Ole Jørgen Abusdal, “Transformations for array programming” MSc Thesis, University of Bergen, 2020.

Ole Magnus Morken, “K-Core Decomposition with CUDA”, MSc Thesis, University of Bergen , 2020.

Espen Næss, “Pyramidal Segmentation of Medical Images via Generative Adversarial Networks”, MSc Thesis, University of Oslo, 2020.

Hanna Svennevik, “Applying artificial intelligence to performance climate predictions”, MSc Thesis, University of Oslo, 2020.

Henrik Gjestang, “A self-learning teacher-student framework for gastrointestinal image classification”, MSc Thesis, University of Oslo, 2020.

Henrik Svoren, “Emotional Mario: Using Super Mario Bros. To Train Emotional Intelligent Machines”, MSc Thesis, University of Oslo, 2020.

Lucas Georges Gabriel Charpentier, “To prune or not to prune: Exploring the effects of nodes in neural networks”, MSc Thesis, University of Oslo, 2020.

Martin Kristoffer Svensen, “Reidentifying Anonymised Data Using Machine Learning”, MSc Thesis, University of Oslo, 2020.

Oda Olsen Nedrejord, “Artificial Video Generation for Improved Performance on Polyp Detection”, MSc Thesis, University of Oslo, 2020.

Olav Rongved, “Automatic event detection in soccer videos”, MSc Thesis, University of Oslo, 2020.

Rabindra Khadka, “Meta-Learning for Medical Image Segmentation”, MSc Thesis, Oslo Metropolitan University, 2020.

Ferdinand Schaal, “Using Graph Neural Networks to classify Distribution Graphs from Twitter”, MSc Thesis, Technical University of Denmark, 2020.

André Berge, “A parallel version of the Random Order Augmentation Matching Algorithm”, MSc Thesis, University of Bergen, 2020.

Amund Lindberg, “Computing Twitter Influence with a GPU”, MSc Thesis, University of Bergen, 2020.

2019

Kristian Gregorius Hustad, “Solving the monodomain model efficiently on GPUs”, MSc Thesis, University of Oslo, 2019.

Other publications

2021

J. Trotter. J. Langguth, and X. Cai, “Automated Code Generation for GPU-Based Finite Element Computations in FEniCS”, SIAM Conference on Computational Science and Engineering (CSE21), 2021 (poster).

X. Cai, A. M. Bruaset, E. G. Gran, and T. Larsen, “eX3: Experimental Infrastructure for Exploration of Exascale Computing”, ISC High Performance, 2021 (poster).

2020

A. B. Ovesen, A. M. Khan, P. N. Chau, P. H. Ha, “SaddlebagX: High-Performance Data Processing with PGAS and UPC++”, ACM/IEEE International Conference for High-Performance Computing, Networking, Storage and Analysis (SC'20), 2020 (poster).

D. T. Schroeder, P. Lind, K. Pogorelov, and J. Langguth, “A Framework for Interaction-based Propagation Analysis in Online Social Networks”, Complex Networks, 2020 (extended abstract, paper forthcoming).

J. Langguth, D. T. Schroeder, K. Pogorelov, and P. Filkukova, “Graph Structure Based Monitoring of Digital Wildfires”, 6th International Conference on Computational Social Science, 2020 (poster).

Kristian Gregorius Hustad, Xing Cai, Johannes Langguth, and Hermenegild Arevalo, “Efficient simulations of patient-specific electrical heart activity on the DGX-2”, Nvidia GPU Technology Conference (GTC), 2020 (poster).

Håvard Heitlo Holm, Martin Lilleeng Sætra and André Rigland Brodtkorb, "Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs", Geilo Winter School, 2020 (poster).

2019

Johannes Langguth, Hermenegild Arevalo, Kristian Gregorius Hustad, and Xing Cai, “Towards detailed real-time simulations of cardiac arrhythmia”, Computing in Cardiology; 2019-09-08 - 2019-09-11 (poster).