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Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na, Cong Hao

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package

October 16, 2022 by hgpu

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

Wenzel Jakob, Sébastien Speierer, Nicolas Roussel, Delio Vicini

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Tags: 3D Graphics and Realism, Algorithms, Code generation, CUDA, nVidia, nVidia RTX A6000, Python, Rendering

February 6, 2022 by hgpu

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

Stefan Abi-Karam, Yuqi He, Rishov Sarkar, Lakshmi Sathidevi, Zihang Qiao, Cong Hao

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Tags: Computer science, FPGA, Machine learning, Neural networks, nVidia, nVidia RTX A6000, Package

January 30, 2022 by hgpu

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Martin Uray, Eduard Hirsch, Gerold Katzinger, Michael Gadermayr

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia RTX A6000

October 17, 2021 by hgpu

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

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

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high performance computing on graphics processing units: hgpu.org

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Recent source codes

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

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

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