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GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference

Rithik Sharma, Reese Levine, Zheyuan Chen, Ananthajit Srikanth, Yanwen Xu, Tyler Sorensen

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Tags: AMD, AMD Radeon RX 7900 XT, Apple M2 Pro, ATI, Computer science, CUDA, Intel, Intel Arc A770, Intel Arc B580, LLM, nVidia, nVidia GeForce RTX 4070, nVidia GeForce RTX 5080, OpenCL, Performance

October 19, 2026 by hgpu

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

Yakup Koray Budanaz, Lorenzo Paleari, Marcin Copik, Alexandru Calotoiu, Timo Schneider, Benjamin Chen Ming Choong, Shriram Chandran, Lars Hulsbergen, Levi Widmer, Torsten Hoefler

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Tags: AMD, ATI, Benchmarking, Computer science, CUDA, Fortran, HIP, HPC, nVidia, nVidia GH200, OpenMP, Optimization, Package, Python, ROCm, Triton

October 19, 2026 by hgpu

A New Lens on Multi-GPU Communication: API-Level Memory Models for Portable Optimization

Johnathan Alsop, Bradford Beckmann, Brandon Potter, Jiakun Yan, Aurelien Bouteiller, Omri Mor, Tony Tye, Sameer Sahasrabuddhe

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Tags: AMD, AMD Radeon Instinct MI300X, ATI, Computer science, HPC, Memory model

October 19, 2026 by hgpu

Microarchitectural Memory Bandwidth Saturation, KV-Cache Paging Dynamics, and Time-to-First-Token Latency: A Comparative Benchmark of vLLM, TensorRT-LLM, and FlashAttention-3 on NVIDIA Hopper H100 versus AMD Instinct MI300X

Kartik Kothalkar

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Tags: AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, CUDA, LLM, nVidia, nVidia H100, Performance, ROCm

September 28, 2026 by hgpu

Xtrace: High-Fidelity GPU Intra-Kernel Tracing via Binary-Level Instruction Splicing

Zhuobin Huang, Kai Zhang, Weihao Cui, Hongshi Tan, Liang Luo, Christopher Dewan, Shen Li, Bingsheng He

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Tags: AMD Radeon Instinct MI300X, ATI, Computer science, LLM, nVidia, nVidia B300, nVidia H100, Performance

September 28, 2026 by hgpu

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Gang Liao, Hongsen Qin, Ying Wang, Alicia Golden, Michael Kuchnik, Yavuz Yetim, Jia Jiunn Ang, Chunli Fu, Yihan He, Samuel Hsia, Zewei Jiang, Dianshi Li, Uladzimir Pashkevich, Varna Puvvada, Feng Shi, Matt Steiner, Ruichao Xiao, Nathan Yan, Xiayu Yu, Zhou Fang, Abdul Zainul-Abedin, Ketan Singh, Hongtao Yu, Wenyuan Chi, Barney Huang, Sean Zhang, Noah Weller, Zach Marine, Wyatt Cook, Carole-Jean Wu, Gaoxiang Liu

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Tags: AI, AMD Radeon Instinct MI300X, AMD Radeon Instinct MI350X, ATI, Computer science, CUDA, Deep learning, Heterogeneous systems, LLM, nVidia, nVidia A100, nVidia H100, PTX, ROCm, Triton

January 4, 2026 by hgpu

PEAK: A Performance Engineering AI-Assistant for GPU Kernels Powered by Natural Language Transformations

Muhammad Usman Tariq, Abhinav Jangda, Angelica Moreira, Madan Musuvathi, Tyler Sorensen

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Tags: AI, AMD, AMD Radeon Instinct MI200, ATI, Computer science, CUDA, HIP, HLSL, LLM, NLP, nVidia, nVidia RTX A6000

December 29, 2025 by hgpu

Chopper: A Multi-Level GPU Characterization Tool & Derived Insights Into LLM Training Inefficiency

Marco Kurzynski, Shaizeen Aga, Di Wu

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Tags: AMD, AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, Data parallelism, LLM, ROCm

December 15, 2025 by hgpu

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

Ryan Swann, Muhammad Osama, Xiaohu Guo, Bryant Nelson, Lixun Zhang, Alex Brown, Yen Ong, Ali Yazdani, Sean Siddens, Ganesh Dasika, Alex Underwood

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Tags: AMD, AMD Radeon Instinct MI300X, AMD Radeon Instinct MI350X, ATI, BLAS, Computer science, HPC, Package, Performance, ROCm, Triton

December 7, 2025 by hgpu

AIvailable: A Software-Defined Architecture for LLM-as-a-Service on Heterogeneous and Legacy GPUs

Pedro Antunes, Ana Rita Ortigoso, Gabriel Vieira, Daniel Fuentes, Luís Frazão, Nuno Costa, António Pereira

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Tags: AMD Radeon Pro W6600, ATI, Computer science, CUDA, Heterogeneous systems, LLM, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3070, ROCm

November 23, 2025 by hgpu

Iris: First-Class Multi-GPU Programming Experience in Triton

Muhammad Awad, Muhammad Osama, Brandon Potter

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Tags: AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, Package, Python, Triton

November 23, 2025 by hgpu

The Anatomy of a Triton Attention Kernel

Burkhard Ringlein, Jan van Lunteren, Radu Stoica, Thomas Parnell

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Tags: AMD Radeon Instinct MI250, AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, DSL, HIP, LLM, nVidia, nVidia H100, Performance, Programming Languages, Triton

November 23, 2025 by hgpu

Qwen3-235B @ 1M tokens on AMD MI355X

CHUNDER: Enabling Million-Token Qwen3 Serving on AMD ROCm

CUDA++ for the NVIDIA DGX Spark

Reverse Engineering the CUDA Software Stack: A Clean-Room GPU Machine-Code Toolchain for the NVIDIA GB10

ATTUNE: Agentic Throughput Tuning for LLM Training

ATTUNE: Agentic Throughput Tuning for LLM Training

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

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

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

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

Introduction to Computer Vision

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

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