high performance computing on graphics processing units: hgpu.org

Zihao Ye, Yingyi Huang, Hongyi Jin, Bohan Hou, Junru Shao, Zhongming Yu, Jinqi Chen, Meghan Cowan, Shiyi Cao, Shanli Xing, Hanfeng Chen, Vinod Grover, Tianqi Chen, Luis Ceze

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Tags: Compilers, Computer science, CUDA, nVidia, PTX

August 17, 2026 by hgpu

Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra

Teng-Ruei Chen

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Tags: Computer science, nVidia, nVidia B300, PTX, Triton

August 17, 2026 by hgpu

Real FP4 Tensor-Core Code in Pure Rust on a Gaming GPU – with NVIDIA’s Own Compiler

Carter Richardson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 5070 Ti, PTX, Rust

July 13, 2026 by hgpu

Leveraging AI Ecosystem for Portable and Sustainable GPU Kernels in HPC

Yanbo Zhao, Zhaonan Meng, Sai Krishna Teja Varma Manthena, Xu Liu, Ajay Panyala, Jiajia Li

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Tags: AMD, AMD Radeon Instinct MI300X, Computer science, DSL, HPC, nVidia, nVidia GeForce RTX 4090, nVidia H100, PTX, Python, ROCm, Triton

June 17, 2026 by hgpu

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

Yee Hin Chong, Jiaming Wu, Youhui Zhang, Peng Qu

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Tags: Computer science, CUDA, Heterogeneous systems, LLM, nVidia, Package, PTX

June 8, 2026 by hgpu

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

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

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

June 8, 2026 by hgpu

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

Brian2 Atlas

A Unified Intermediate Representation and Execution Architecture for Heterogeneous and Distributed Neural Simulation

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelLens: Deploying Triton Kernels to C++ Inference Engines

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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* * *

high performance computing on graphics processing units: hgpu.org

KernelLens: Deploying Triton Kernels to C++ Inference Engines

AI as a Compiler: Compiling Triton kernels without the Triton compiler

Hardware-Aware FP4 FlashAttention-4

Taming Bitwise Behavior in GPU Kernels with Tensor Core: Black-Box Reconstruction, Compiler Enforcement, and Static Verification

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4

CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution

Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra

Real FP4 Tensor-Core Code in Pure Rust on a Gaming GPU – with NVIDIA’s Own Compiler

Leveraging AI Ecosystem for Portable and Sustainable GPU Kernels in HPC

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

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

Recent source codes

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

Brian2 Atlas

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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