GPU Acceleration

GPU: Graphics Processing Unit

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7 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

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Latest papers 125

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PySIFT: GPU-Resident Deterministic SIFT for Deep Learning Vision Pipelines
May 18, 2026Sivakumar K. S., Mohammad Daniyalur Rahman, Gopi Raju MattaGPU Acceleration Image Matching
1Indian Institute of Technology Madras
AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents
May 16, 2026Sharareh Younesian, Wenwen Ouyang, Sina Rafati +11GPU Kernel Optimization GPU Acceleration
AMD
DualKV: Shared-Prompt Flash Attention for Efficient RL Training with Large Rollouts and Long Contexts
May 14, 2026Jiading Gai, Shuai Zhang, Xiang Song +2GPU Acceleration Self-Attention
1Amazon Web Services · 2Google · University of Minnesota
GQLA: Group-Query Latent Attention for Hardware-Adaptive Large Language Model Decoding
May 14, 2026Fanxu MengGrouped-Query Attention GPU Acceleration
Institute for Artificial Intelligence, Peking University
Parallelizing Counterfactual Regret Minimization
May 14, 2026Juho Kim, Tuomas SandholmGPU Acceleration Imperfect-Information Games
Computer Science Department Carnegie Mellon University · Computer Science Department, CMU · Strategic Machine, Inc. +2
EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization
May 14, 2026Zhiye Song, Kyungmi Lee, Eun Kyung Lee +3LLM Inference Efficiency Energy-Efficient ML
Massachusetts Institute of Technology Cambridge, MA 02139 · IBM Research Yorktown Heights, NY 10598
BlitzGS: City-Scale Gaussian Splatting at Lightning Speed
May 13, 2026Zhongtao Wang, Huishan Au, Yilong Li +63D Gaussian Splatting 3D Gaussian Pruning
Peking University, China
The Illusion of Power Capping in LLM Decode: A Phase-Aware Energy Characterisation Across Attention Architectures
May 12, 2026Bole Ma, Ayesha Afzal, Jan Eitzinger +1LLM Inference Efficiency Language Model Decoding
Erlangen National High Performance Computing Center, Erlangen, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Ada-MK: Adaptive MegaKernel Optimization via Automated DAG-based Search for LLM Inference
May 12, 2026Wenxin Dong, Mingqing Hu, Guanghui Yu +7GPU Kernel Optimization GPU Acceleration
Baidu Inc.
Fast MoE Inference via Predictive Prefetching and Expert Replication
May 12, 2026Ankit Jyothish, Ali Jannesari, Aishwarya Sarkar +1Expert Load Balancing GPU Acceleration
Iowa State University Ames, USA
ChunkFlow: Communication-Aware Chunked Prefetching for Layerwise Offloading in Distributed Diffusion Transformer Inference
May 11, 2026Han Meng, Danny Willow Liu, Dong LiDiffusion Transformer GPU Acceleration
University of California, Merced · University of Chicago · University of California, Merced and Yotta Labs +1
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
May 11, 2026Liang Luo, Yinbin Ma, Quanyu Zhu +21Mixed-Precision Quantization GPU Kernel Optimization
Meta AI
Novel GPU Boruta algorithms for feature selection from high-dimensional data
May 11, 2026Xurui Li, Zhiguo Gan, Jiaming Zhang +2GPU Acceleration Feature Selection
Department of Chemical Engineering, Tsinghua University, 2026 Shuangqing Road, Beijing, 100084, China.
SceneFactory: GPU-Accelerated Multi-Agent Driving Simulation with Physics-Based Vehicle Dynamics
May 8, 2026Yicheng Zhu, Yang Chen, Tao Li +1GPU Acceleration Vehicle Dynamics
Rochester Institute of Technology · City University of Hong Kong
CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs
May 8, 2026Shiyang Li, Zijian Zhang, Guangyan Sun +5LLM Evaluation GPU Kernel Optimization
University of Minnesota · Northeastern University
An Efficient Hybrid Sparse Attention with CPU-GPU Parallelism for Long-Context Inference
May 8, 2026Feiyu Yao, Zhixiong Niu, Xiaqing Li +3GPU Acceleration Self-Attention
Beijing University of Technology Beijing, China · Microsoft Research Beijing, China · Beijing Jiaotong University Beijing, China
Kernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts
May 8, 2026Zixuan Huang, Da Chen, Kecheng Huang +5GPU Kernel Optimization GPU Acceleration
1The Chinese University of Hong Kong · 2Noah’s Ark Lab, Huawei
GPU-Accelerated Synthesis of Mixed-Boolean Arithmetic: Beyond Caching
May 7, 2026Gabriel Bathie, Baptiste Mouillon, Nathanaël FijalkowGPU Acceleration Program Synthesis
CNRS, LaBRI, University of Bordeaux, France
CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels
May 6, 2026Xing Ma, Yangjie Zhou, Wu Sun +6GPU Kernel Optimization GPU Acceleration
Shanghai Jiao Tong University · National University of Singapore · Shanghai Qi Zhi Institute
KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels
May 6, 2026Han Wang, Jintao Zhang, Kai Jiang +3GPU Kernel Optimization GPU Acceleration
∗Equal contribution.
One Pool, Two Caches: Adaptive HBM Partitioning for Accelerating Generative Recommender Serving
May 6, 2026Wenjun Yu, Shuguang Han, Amelie Chi ZhouLLM Inference Efficiency GPU Acceleration
Hong Kong Baptist University · Hong Kong, China · Alibaba Inc +1
Tile-Level Activation Overlap for Efficient LLM Inference
May 5, 2026Abhinav Jangda, Tyler Sorensen, Sebastian Burckhardt +3LLM Inference Efficiency GPU Kernel Optimization
Microsoft Research · Microsoft
Stochastic Sparse Attention for Memory-Bound Inference
May 3, 2026Kyle Lee, Corentin Delacour, Kevin Callahan-Coray +5GPU Acceleration Self-Attention
Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA · Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA · Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
VUDA: Breaking CUDA-Vulkan Isolation for Spatial Sharing of Compute and Graphics on the Same GPU
May 2, 2026Bin Xu, Pengfei Hu, Wenxin Zheng +2GPU Acceleration Heterogeneous Computing
Shanghai Jiao Tong University Shanghai, China
SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data
May 1, 2026Shashank Kapadia, Deep Narayan Mishra, Sujal Reddy Alugubelli +3Efficient Neural Network Inference GPU Acceleration
Walmart Inc. · USA
VkSplat: High-Performance 3DGS Training in Vulkan Compute
Apr 30, 2026Jingxiang Chen, Mohamed Ibrahim, Yang Liu3D Gaussian Splatting 3DGS Acceleration
Huawei Canada
DPU or GPU for Accelerating Neural Networks Inference -- Why not both? Split CNN Inference
Apr 30, 2026Ali Emre Oztas, Mahir Demir, James Garside +1AI Accelerator Inference GPU Acceleration
The University of Manchester
Real-Time GPU-Accelerated Monte Carlo Evaluation of Safety-Critical AEB Systems Under Uncertainty
Apr 29, 2026Akshay Karjol, Shadi AlawnehCollision Avoidance Uncertainty Quantification
Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309 USA
Efficient Training on Multiple Consumer GPUs with RoundPipe
Apr 29, 2026Yibin Luo, Shiwei Gao, Huichuan Zheng +2GPU Acceleration LLM Fine-Tuning
Tsinghua University Beijing, China
FloatSOM: GPU-Accelerated, Distributed, Topology-Flexible Self-Organizing Maps
Apr 29, 2026Tony Xu, Sarah Klamt, Katherine Turner +3GPU Acceleration High-Performance Computing
John Curtin School of Medical Research, Australian National University · Department of Information Technology and Electrical Engineering, ETH Zürich · Mathematical Data Science Centre, Australian National University +2

May 18, 2026, cs.CVJ/K move · Enter open · S save

PySIFT: GPU-Resident Deterministic SIFT for Deep Learning Vision Pipelines

Sivakumar K. S., Mohammad Daniyalur Rahman, Gopi Raju Matta

1Indian Institute of Technology Madras

GPU Acceleration Image Matching Keypoint Detection Local Feature Matching

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A widespread assumption in local feature research holds that classical handcrafted descriptors are accuracy-limited relics best replaced by learned alternatives. We show this is wrong. Through an 8-configuration ablation spanning four benchmarks (HPatches, ROxford5K, IMC Phototourism, MegaDepth), we demonstrate that classical SIFT with DSP multi-scale pooling outperforms neural descriptor and orientation replacements (HardNet, OriNet) on every accuracy metric--while running 2--18

\times

faster--and that learned matchers (LightGlue) complement rather than supersede classical features. The conclusion reframes a decade of work: not "replace SIFT" but "compose with SIFT," classical extraction paired with learned matching only where geometric context demands it. This finding was invisible because no prior GPU SIFT kept the complete pipeline in VRAM or offered modularity for controlled classical-vs-learned ablations. We present PySIFT, the first fully GPU-resident SIFT, implemented in CuPy/Numba CUDA kernels with DLPack zero-copy handoff to downstream DL frameworks--submillisecond O(1) metadata swap regardless of keypoint count. On a laptop-grade NVIDIA RTX 3050 (4 GB VRAM), PySIFT achieves: (i) higher Mean Matching Accuracy (MMA) than OpenCV SIFT on HPatches, (ii) 383 ms faster per pair on high-resolution MegaDepth, (iii) higher geometric accuracy on cross-dataset benchmarks (+5.6 pp AUC@10

{}^\circ

on MegaDepth, more inliers on IMC Phototourism), and (iv) bitwise deterministic output--identical keypoints and descriptors across runs, with detection reproducing identically even across GPU architectures: a guarantee that learned extractors cannot match without significant performance sacrifice, and cannot achieve at all across GPU architectures due to cuDNN's architecture-dependent algorithm selection. PySIFT is open-source, requiring no C++ compilation.

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