high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators

Taras Sereda, Burak Bartan, Ankita Nayak, Tom St.John, Natalie Serrino, Zain Asgar

View

Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Intel, Intel Arc B580, nVidia, nVidia B200, PTX, Triton

June 8, 2026 by hgpu

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

Jie Li, Wenzhao Wu, Junqi Hu, Qinrui Zheng, Bowen Wu, Juepeng Zheng, Yutong Lu, Haohuan Fu

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, HPC, Linear Algebra, LLM, nVidia, Package

June 8, 2026 by hgpu

CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs

Shiyang Li, Zijian Zhang, Guangyan Sun, Yuebo Luo, Winson Chen, Yanzhi Wang, Mingyi Hong, Caiwen Ding

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, LLM, nVidia, nVidia RTX PRO 6000

May 20, 2026 by hgpu

Source-to-Source Transformations for GPU Code Generation

Julien de Castelnau, Thomas Koehler, Arthur Charguéraud, Clément Pit-Claudel

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce RTX 5060, Thesis

May 20, 2026 by hgpu

CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging

Shiyang Li, Haoyang Chen, Mattia Fazzini, Caiwen Ding

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia H200, nVidia RTX PRO 6000

May 20, 2026 by hgpu

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

Matija Dodović, Milica Veselinović, Marko Mišić

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 3080, Package

May 20, 2026 by hgpu

KEET: Explaining Performance of GPU Kernels Using LLM Agents

Joshua H. Davis, Klaudiusz Rydzy, Srinivasan Ramesh, Aadit Nilay, Daniel Nichols, Swapna Raj, Nikhil Jain, Abhinav Bhatele

View

Download (PDF)

Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Performance

May 11, 2026 by hgpu

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu, Jingwen Leng, Yun Lin, Shixuan Sun, Minyi Guo, Jin Song Dong

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, nVidia H100

May 11, 2026 by hgpu

Microbenchmark-Driven Analytical Performance Modeling Across Modern GPU Architectures

Aaron Jarmusch, Sunita Chandrasekaran

View

Download (PDF)

Tags: AMD, AMD Radeon Instinct MI250X, AMD Radeon Instinct MI300A, Benchmarking, Computer science, CUDA, nVidia, nVidia B200

May 11, 2026 by hgpu

FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow

Sina Heidari, Dimitrios S. Nikolopoulos

View

Download (PDF)

Tags: Computer science, CUBLAS, CUDA, Deep learning, nVidia, nVidia A100

May 3, 2026 by hgpu

Revealing NVIDIA Closed-Source Driver Command Streams for CPU-GPU Runtime Behavior Insight

Yuang Yan, Ian Karlin, Ryan Grant

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia A40, Performance

May 3, 2026 by hgpu

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Divakar Kumar Yadav, Tian Zhao, Deepak Kumar

View

Download (PDF)

Source codes

Tags: Computer science, CUBLAS, CUDA, LLM, nVidia, nVidia B200, nVidia H100, nVidia RTX PRO 6000, Package, Performance, Triton

May 3, 2026 by hgpu

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs

Source-to-Source Transformations for GPU Code Generation

CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

KEET: Explaining Performance of GPU Kernels Using LLM Agents

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

Microbenchmark-Driven Analytical Performance Modeling Across Modern GPU Architectures

FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow

Revealing NVIDIA Closed-Source Driver Command Streams for CPU-GPU Runtime Behavior Insight

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Recent source codes

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

Most viewed papers (last 30 days)