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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

WiLLM: An Open Wireless LLM Communication System

Boyi Liu, Yongguang Lu, Jianguo Zhao, Qiang Yang, Wen Wu, Lin Chen, Jagmohan Chauhan, Jun Zhang

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Tags: Computer science, LLM, Network communications, nVidia, nVidia GeForce RTX 4090, Package

June 29, 2025 by hgpu

Omniwise: Predicting GPU Kernels Performance with LLMs

Zixian Wang, Cole Ramos, Muhammad A. Awad, Keith Lowery

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Tags: AMD, AMD Radeon Instinct MI250, AMD Radeon Instinct MI300X, Artificial intelligence, Benchmarking, Computer science, LLM, Neural networks, Performance, ROCm

June 29, 2025 by hgpu

A First Look at Bugs in LLM Inference Engines

Mugeng Liu, Siqi Zhong, Weichen Bi, Yixuan Zhang, Zhiyang Chen, Zhenpeng Chen, Xuanzhe Liu, Yun Ma

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Tags: AI, Computer science, LLM, Software Engineering

June 22, 2025 by hgpu

CUDA-LLM: LLMs Can Write Efficient CUDA Kernels

Wentao Chen, Jiace Zhu, Qi Fan, Yehan Ma, An Zou

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Tags: Artificial intelligence, Code generation, Computer sceince, CUDA, LLM, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3090 Ti

June 15, 2025 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Jiaqi Lv, Xufeng He, Yanchen Liu, Xu Dai, Yang Hu, Shouyi Yin

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Tags: AI, Benchmarking, Compilers, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, Package, performance portability

June 15, 2025 by hgpu

Acceleration as a Service (XaaS) Source Containers

Eiman Alnuaimi

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Tags: Computer science, Heterogeneous systems, HPC, Intel, Intel Data Center GPU Max 1550, LLM, MPI, nVidia, nVidia GH200, nVidia V100, Optimization, Package, performance portability, Thesis

June 8, 2025 by hgpu

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning

Yong-Cheng Liaw, Shuo-Han Chen

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Tags: Artificial intelligence, Benchmarking, Computer science, LLM, Memory, nVidia, nVidia H100, nVidia RTX A5000

June 8, 2025 by hgpu

Performance of Confidential Computing GPUs

Antonio Martínez Ibarra, Julian James Stephen, Aurora González Vidal, K. R. Jayaram, Antonio Fernando Skarmeta Gómez

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

May 25, 2025 by hgpu

Can Large Language Models Predict Parallel Code Performance?

Gregory Bolet, Giorgis Georgakoudis, Harshitha Menon, Konstantinos Parasyris, Niranjan Hasabnis, Hayden Estes, Kirk W. Cameron, Gal Oren

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Tags: Benchmarking, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 3080, OpenMP, Package, Performance, performance portability

May 18, 2025 by hgpu

GPU Performance Portability needs Autotuning

Burkhard Ringlein, Thomas Parnell, Radu Stoica

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Tags: AMD Radeon Instinct MI250, ATI, Auto-Tuning, Computer science, CUDA, DSL, HIP, LLM, nVidia, nVidia A100, Performance, performance portability

May 18, 2025 by hgpu

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Neha Prakriya, Zijian Ding, Yizhou Sun, Jason Cong

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Tags: AMD Radeon Instinct MI250, ATI, Computer science, FPGA, HLS, LLM, Machine learning, Neural networks, PyTorch

May 4, 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

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

WiLLM: An Open Wireless LLM Communication System

Omniwise: Predicting GPU Kernels Performance with LLMs

A First Look at Bugs in LLM Inference Engines

CUDA-LLM: LLMs Can Write Efficient CUDA Kernels

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Acceleration as a Service (XaaS) Source Containers

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning

Performance of Confidential Computing GPUs

Can Large Language Models Predict Parallel Code Performance?

GPU Performance Portability needs Autotuning

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

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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