GPU Acceleration

GPU: Graphics Processing Unit

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Overview

GPU acceleration studies how to map computation onto many parallel cores to speed workloads such as language-model inference, rendering, robotics, and scientific simulation. Common approaches include hand-tuned CUDA and Triton kernels, tiling and operator fusion, lower-precision arithmetic, and algorithm reformulations that expose more parallel work. Compilers and coding agents increasingly help generate and tune kernels, while multi-GPU systems focus on scheduling, memory use, and communication. A central challenge is that speedups depend on the workload and hardware: specialized kernels can be fast but difficult to port, and gains in isolated operations may disappear once memory movement, dispatch, or numerical accuracy is considered.

Latest papers

CardsList

174 papers17 papers in the last four weeks, up 31% on the four weeks before. 0.1% of all new papers.

  1. FastLoop: Parallel Loop Closing with GPU-Acceleration in Visual SLAM

    Mar 17, 2026Soudabeh Mohammadhashemi, Shishir Gopinath, Kimia Khabiri +3Visual SLAMLoop Closure Detection

    Simon Fraser University. · University at Buffalo.

  2. An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

    Mar 17, 2026Ruijia Yang, Zeyi WenFine-TuningMemory-Efficient Fine-Tuning

    HKUST(GZ) Guangzhou, China · HKUST Guangzhou, China

  3. Collapse or Preserve: Data-Dependent Temporal Aggregation for Spiking Neural Network Acceleration

    Mar 14, 2026Jiahao QinSpiking Neural NetworksEfficient Neural Network Inference

  4. KernelFoundry: Hardware-aware evolutionary GPU kernel optimization

    Mar 12, 2026Nina Wiedemann, Quentin Leboutet, Michael Paulitsch +2GPU Kernel OptimizationGPU Acceleration

    Intel Corporation

  5. Parallel-in-Time Nonlinear Optimal Control via GPU-native Sequential Convex Programming

    Mar 11, 2026Yilin Zou, Zhong Zhang, Maxime Robic +1Optimal ControlGPU Acceleration

    School of Aerospace Engineering, Tsinghua University, 100084 Beijing, China. · Department of Aerospace Science and Technology, Politecnico di Milano, 20156 Milan, Italy.

  6. GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework

    Mar 10, 2026Nathan Haut, Ilya Basin, Marzieh Kianinejad +4Genetic ProgrammingSymbolic Regression

    Michigan State University East Lansing, Michigan, USA · Noblis, Inc. Virginia, USA

  7. FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling

    Mar 5, 2026Ted Zadouri, Markus Hoehnerbach, Jay Shah +3Efficient AttentionGPU Kernel Optimization

    Princeton University · Together AI · Meta +3

  8. mlx-vis: GPU-Native Dimensionality Reduction on Apple Silicon

    Mar 4, 2026Han XiaoDimensionality ReductionGPU Acceleration

    Elastic

  9. cuNRTO: GPU-Accelerated Nonlinear Robust Trajectory Optimization

    Mar 3, 2026Jiawei Wang, Arshiya Taj Abdul, Evangelos A. TheodorouSecond-Order Cone ProgrammingTrajectory Optimization

    University of California, San Diego · Georgia Institute of Technology, Atlanta

  10. RoboGPU: Accelerating GPU Collision Detection for Robotics

    Mar 2, 2026Lufei Liu, Liwei Xue, Yuan Hsi Chou +4GPU AccelerationRobotics

    University of British Columbia Vancouver, Canada

  11. pathsig: A GPU-Accelerated Library for Truncated and Projected Path Signatures

    Feb 27, 2026Tobias NygaardPath SignaturesGPU Acceleration

    Artificial Intelligence and Mathematics Research Lab, UK

  12. A GPU-Accelerated Hybrid Method for a Class of Multi-Depot Vehicle Routing Problems

    Feb 24, 2026Zhenyu Lei, Jin-Kao HaoVehicle RoutingMetaheuristic Optimization

    LERIA, Université d’Angers, 2 Boulevard Lavoisier, 49045 Angers, France

  13. SoftDTW-CUDA-Torch: Memory-Efficient GPU-Accelerated Soft Dynamic Time Warping for PyTorch

    Feb 19, 2026Ron Shapira Weber, Oren FreifeldDynamic Time WarpingGPU Acceleration

    The Faculty of Computer and Information Science Ben-Gurion University of the Negev

  14. DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers

    Feb 2, 2026Ionut-Vlad Modoranu, Philip Zmushko, Erik Schultheis +2Deep Learning OptimizationGPU Acceleration

    Institute of Science and Technology Austria · Lancaster University · Red Hat AI

  15. KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

    Dec 29, 2025Gang Liao, Hongsen Qin, Ying Wang +36GPU Kernel OptimizationGPU Acceleration

    KernelEvolve Team, Meta Platforms

  16. Cerium: A Multi-GPU Framework for Terabyte-Scale Encrypted Inference

    Dec 12, 2025Siddharth Jayashankar, Joshua Kim, Michael B. Sullivan +2Homomorphic EncryptionGPU Kernel Optimization

    Carnegie Mellon University · UT Austin · NVIDIA

  17. Block Sparse Flash Attention

    Dec 7, 2025Daniel Ohayon, Itay Lamprecht, Itay Hubara +3GPU AccelerationSelf-Attention

    Technion – Israel Institute of Technology, Haifa, Israel · Intel - Habana Labs · Intel – Habana Labs, Tel Aviv, Israel

  18. CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

    Dec 2, 2025Songqiao Su, Xiaoya Li, Albert Wang +3Reinforcement LearningGPU Kernel Optimization

    DeepReinforce Team

  19. Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference

    Nov 19, 2025Kexin Chu, Dawei Xiang, Zixu Shen +3Mixed-Precision QuantizationGPU Acceleration

    University of Connecticut · University of California, Santa Cruz · Independent Researcher

  20. Bit-Accurate Modeling of GPU Matrix Multiply-Accumulate Units: Demystifying Numerical Discrepancy and Accuracy

    Nov 14, 2025Peichen Xie, Shuotao Xu, Yang Wang +2GPU Acceleration

  21. NOSA: Native and Offloadable Sparse Attention

    Oct 15, 2025Yuxiang Huang, Pengjie Wang, Jicheng Han +9KV-Cache OffloadingGPU Acceleration

    NLP Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China. · OpenBMB, China. · BUPT, Beijing, China. +1

  22. FastTrack: GPU-Accelerated Tracking for Visual SLAM

    Sep 13, 2025Kimia Khabiri, Parsa Hosseininejad, Shishir Gopinath +2GPU AccelerationVisual SLAM

    Simon Fraser University · University at Buffalo

  23. FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel

    Aug 25, 2025Ran Yan, Youhe Jiang, Zhuoming Chen +3GPU Kernel OptimizationGPU Acceleration

    1. The Hong Kong University of Science and Technology · 2. Carnegie Mellon University

  24. CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning

    Jul 18, 2025Xiaoya Li, Albert Wang, Guoyin Wang +2Reinforcement LearningGPU Kernel Optimization

    DeepReinforce Team