mlsanityData to 14 Oct 2026

EPIC: An Energy-Efficient, High-Performance GPGPU Computing Research Infrastructure

arXiv 1912.05848 ↗First posted 12 Dec 2019cs.DCcited by 23

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Abstract (arXiv)

The pursuit of many research questions requires massive computational resources. State-of-the-art research in physical processes using simulations, the training of neural networks for deep learning, or the analysis of big data are all dependent on the availability of sufficient and performant computational resources. For such research, access to a high-performance computing infrastructure is indispensable. Many scientific workloads from such research domains are inherently parallel and can benefit from the data-parallel architecture of general purpose graphics processing units (GPGPUs). However, GPGPU resources are scarce at Norway's national infrastructure. EPIC is a GPGPU enabled computing research infrastructure at NTNU. It enables NTNU's researchers to perform experiments that otherwise would be impossible, as time-to-solution would simply take too long.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Dec 2019.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 5; 2024: 3; 2025: 2; 2026: 2.

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  1. 2022DeepPrivacy2: Towards Realistic Full-Body Anonymizationcited by 51
  2. 2022Realistic Full-Body Anonymization with Surface-Guided GANscited by 15
  3. 2022Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networkscited by 13
  4. 2023Does Image Anonymization Impact Computer Vision Training?cited by 12
  5. 2023Learning Pairwise Interaction for Generalizable DeepFake Detectioncited by 11
  6. 2023NLEBench+NorGLM: A Comprehensive Empirical Analysis and Benchmark Dataset for Generative Language Models in Norwegiancited by 9
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  8. 2024PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Modelscited by 5
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  10. 2022Balancing Multi-Domain Corpora Learning for Open-Domain Response Generationcited by 3
  11. 2024Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Studycited by 3
  12. 2021Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areascited by 2
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  3. 2024CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimizationcited by 2
  4. 2022Evaluating and Improving Context Attention Distribution on Multi-Turn Response Generation using Self-Contained Distractionscited by 1
  5. 2026Favia: Forensic Agent for Vulnerability-fix Identification and Analysiscited by 1
  6. 2021Geometric Change Detection in Digital Twins using 3D Machine Learningcited by 0
  7. 2021Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexingcited by 0
  8. 2021Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentationcited by 0
  9. 2025SIRA: Scaled-Integer Range Analysis for Optimizing FPGA Dataflow Neural Network Acceleratorscited by 0
  10. 2025Compressing CNN models for resource-constrained systems by channel and layer pruningcited by 0
  11. 2026NorwAI's Large Language Models: Technical Reportcited by 0