high performance computing on graphics processing units: hgpu.org

Changxin Ke, Rui Zhang, Shuo Wang, Li Ding, Guangli Li, Yuanbo Wen, Shuoming Zhang, Ruiyuan Xu, Jin Qin, Jiaming Guo, Chenxi Wang, Ling Li, Qi Guo, Yunji Chen

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Tags: Code generation, Computer science, CUDA, HPC, LLM, Machine learning, nVidia, nVidia A100, nVidia GeForce RTX 4090, Package

July 13, 2025 by hgpu

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks

Joshua H. Davis, Daniel Nichols, Ishan Khillan, Abhinav Bhatele

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Tags: Benchmarking, Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, OpenMP, Package

July 6, 2025 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

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

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high performance computing on graphics processing units: hgpu.org

Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models

Managing Multi Instance GPUs for High Throughput and Energy Savings

Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture

BePilot: An AI Programming Assistant for Compiler Backend Development

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

AGFT: An Adaptive GPU Frequency Tuner for Real-Time LLM Inference Optimization

ConTraPh: Contrastive Learning for Parallelization and Performance Optimization

Kevin: Multi-Turn RL for Generating CUDA Kernels

Pre-Training LLMs on a budget: A comparison of three optimizers

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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

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

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

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

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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