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Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic

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Tags: AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia RTX A5000, nVidia RTX A6000, Package, Performance, ROCm

October 19, 2025 by hgpu

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Gabriel Kosmacher, Eric T. Phipps, Sivasankaran Rajamanickam

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Tags: Algorithms, Computer science, CUDA, Kokkos, Mathematical Software, nVidia, nVidia H100, OpenMP, Package

October 19, 2025 by hgpu

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

M. D. Lepinzan, G. Lacopo, D. Goz, G. Taffoni, P. Monaco, P. J. Elahi, U. Varetto, M. Cytowski

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Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Benchmarking, Heterogeneous systems, HIP, HPC, Instrumentation and Methods for Astrophysics, nVidia, nVidia A100, OpenMP, Package

October 12, 2025 by hgpu

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

Lingcheng Kong, Jiateng Wei, Hanzhang Shen, Huan Wang

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Tags: AI, Code generation, Computer science, CUDA, Deep learning, LLM, Machine learning, nVidia, Package

October 12, 2025 by hgpu

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Zeyu Sun, Jingjing Liang, Weiyi Wang, Chenyao Suo, Junjie Chen, Fanjiang Xu

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Tags: Computer science, Neural networks, nVidia, nVidia GeForce RTX 4090, OpenCL, Package, Software Engineering

October 12, 2025 by hgpu

High-Performance Computing: from Optimization to Automation

Bérenger Bramas

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia T600, Performance, Thesis

October 12, 2025 by hgpu

EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models

Ping Guo, Chenyu Zhu, Siyuan Chen, Fei Liu, Xi Lin, Zhichao Lu, Qingfu Zhang

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Tags: AI, Code generation, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia GeForce RTX 4090, nVidia H100, PyTorch

October 12, 2025 by hgpu

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Shun-ichiro Hayashi, Koki Morita, Daichi Mukunoki, Tetsuya Hoshino, Takahiro Katagiri

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Tags: AI, Code generation, Computer science, CUDA, HPC, LLM, nVidia, OpenACC, OpenMP, Package, Tesla V100

October 5, 2025 by hgpu

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

Tihomir Thomas Bicanic

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, Performance, Rust, Security

October 5, 2025 by hgpu

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

Weiyi Xiaa, Maxim Moraru, Ying Wai Li, Cai-Zhuang Wang

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Tags: Algorithms, Benchmarking, Condensed matter, Materials Science, Package, Physics

October 5, 2025 by hgpu

Performance and Numerical Aspects of Decompositional Factorizations with FP64 Floating-Point Emulation in INT8

Piotr Luszczek, Vijay Gadepally, LaToya Anderson, William Arcand, David Bestor, William Bergeron, Alex Bonn, Daniel J. Burrill, Chansup Byun, Michael Houle, Matthew Hubbell, Hayden Jananthan, Michael Jones, Peter Michaleas, Guillermo Morales, Julia Mullen, Andrew Prout, Albert Reuther, Antonio Rosa, Charles Yee, Jeremy Kepner

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Tags: Computer science, Factorization, Mathematics, Numerical Analysis, nVidia

October 5, 2025 by hgpu

Opal: A Modular Framework for Optimizing Performance using Analytics and LLMs

Mohammad Zaeed, Tanzima Z. Islam, Vladimir Inđić

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Tags: AI, AMD Radeon Instinct MI210, ATI, Computer science, CUDA, LLM, nVidia, nVidia H100, Performance, ROCm

October 5, 2025 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

Inside VOLT: Designing an Open-Source GPU Compiler (Tool)

SciDef: Automated Definition Extraction from Scientific Literature

SciDef: Automating Definition Extraction from Academic Literature with Large Language Models

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics

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