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Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Meng Wang, Jinshuo Liu, Zimeng Xu, Chang Ding, Xiaomeng Huang

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 4090, Package

April 13, 2025 by hgpu

GigaAPI for GPU Parallelization

M. Suvarna, O. Tehrani

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia RTX 6000 Ada, Package

April 13, 2025 by hgpu

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

Michele Martone, Julia Lawall

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Tags: Computer science, CUDA, HIP, HPC, nVidia, OpenMP, Package, Programming Languages

March 30, 2025 by hgpu

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Abhishek Ghosh, Ajay Nayak, Ashish Panwar, Arkaprava Basu

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia RTX A6000, PyTorch

March 30, 2025 by hgpu

Efficient allocation of image recognition and LLM tasks on multi-GPU system

Marcin Lawenda, Krzesimir Samborski, Kyrylo Khloponin, Łukasz Szustak

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Tags: Computer science, CUDA, Data parallelism, Image recognition, LLM, Machine learning, nVidia, nVidia H100, Performance

March 30, 2025 by hgpu

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

Timothée David--Cléris, Guillaume Laibe, Yona Lapeyre

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Tags: AMD, AMD Radeon Instinct MI250X, Astrophysics, CUDA, MPI, nVidia, nVidia A100, OpenMP, Package, Physics, PTX, ROCm, SYCL

March 23, 2025 by hgpu

ML-Triton, A Multi-Level Compilation and Language Extension to Triton GPU Programming

Dewei Wang, Wei Zhu, Liyang Ling, Ettore Tiotto, Quintin Wang, Whitney Tsang, Julian Opperman, Jacky Deng

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Tags: AI, Code generation, Compilers, Computer science, CUDA, Intel Data Center GPU Max 1550, nVidia, oneAPI, SYCL

March 23, 2025 by hgpu

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Russel Arbore, Aaron Councilman, Xavier Routh, Ryan Ziegler, Praneet Rathi, Vikram Adve

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, LLVM, nVidia, nVidia GeForce RTX 2080 Ti, Package, Programming Languages, Rust

March 23, 2025 by hgpu

WgPy: GPU-accelerated NumPy-like array library for web browsers

Masatoshi Hidaka, Tatsuya Harada

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 4070, Python

March 10, 2025 by hgpu

SUperman: Efficient Permanent Computation on GPUs

Deniz Elbek, Fatih Taşyaran, Bora Uçar, Kamer Kaya

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Tags: Computer science, CUDA, HPC, Numerical Analysis, nVidia, nVidia A100, nVidia Quadro GV100, OpenMPI, Package

March 10, 2025 by hgpu

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

Lingqi Zhang, Jiajun Huang, Sheng Di, Satoshi Matsuoka, Mohamed Wahib

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Tags: Computer science, CUDA, nVidia, nVidia A100, Performance

March 10, 2025 by hgpu

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

Changqing Shi, Yufei Sun, Rui Chen, Jiahao Wang, Qiang Guo, Chunye Gong, Yicheng Sui, Yutong Jin, Yuzhi Zhang

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Tags: Code generation, Computer science, CUDA, HPC, Memory model, nVidia, OpenCL, Package

March 3, 2025 by hgpu

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI for GPU Parallelization

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Efficient allocation of image recognition and LLM tasks on multi-GPU system

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

ML-Triton, A Multi-Level Compilation and Language Extension to Triton GPU Programming

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

WgPy: GPU-accelerated NumPy-like array library for web browsers

SUperman: Efficient Permanent Computation on GPUs

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

Recent source codes

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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