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Pretraining large language models with MXFP4 on Native FP4 Hardware

Musa Cim, Poovaiah Palangappa, Miro Hodak, Ravi Dwivedula, Meena Arunachalam, Mahmut Taylan Kandemir

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Tags: AMD, AMD Radeon Instinct MI355X, Computer science, LLM, Precision, ROCm

May 20, 2026 by hgpu

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

Cole Ramos, Keith Lowery

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Tags: AMD, AMD Radeon Instinct MI210, AMD Radeon Instinct MI300X, AMD Radeon Pro W7900, Computer science, LLM, Package, Python, ROCm, Triton

May 11, 2026 by hgpu

Microbenchmark-Driven Analytical Performance Modeling Across Modern GPU Architectures

Aaron Jarmusch, Sunita Chandrasekaran

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Tags: AMD, AMD Radeon Instinct MI250X, AMD Radeon Instinct MI300A, Benchmarking, Computer science, CUDA, nVidia, nVidia B200

May 11, 2026 by hgpu

ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants

Haohui Mai, Xiaoyan Guo, Xiangyun Ding, Daifeng Li, Qiuchu Yu, Chenzhun Guo, Cong Wang, Jiacheng Zhao, Christos Kozyrakis, Binhang Yuan

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Tags: AMD, AMD Radeon Instinct MI300X, Computer science, DSL, LLM, Triton

May 3, 2026 by hgpu

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

Hao Wu, Qidong Zhao, Songqing Chen, Yang Chen, Yueming Hao, Tony CW Liu, Sijia Chen, Adnan Aziz, Keren Zhou

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Tags: AMD, AMD Radeon Instinct MI250X, Computer science, Deep learning, nVidia, nVidia GeForce RTX 4090, ROCm, Triton

March 22, 2026 by hgpu

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

Ruijia Yang, Zeyi Wen

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Tags: AMD, AMD Radeon RX 7900 XT, Computer science, Heterogeneous systems, nVidia, nVidia A100, nVidia GeForce RTX 4090, Triton

March 18, 2026 by hgpu

Diagnosing FP4 inference: a layer-wise and block-wise sensitivity analysis of NVFP4 and MXFP4

Musa Cim, Burak Topcu, Mahmut Taylan Kandemir

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Tags: AMD, Computer science, LLM, nVidia, nVidia GeForce RTX 5090, nVidia RTX PRO 6000, Precision

March 15, 2026 by hgpu

Ray Tracing using HIP

Atsushi Yoshimura, Kenta Eto, Daniel Meister, Takahiro Harada

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Tags: 3D Graphics and Realism, AMD, Computer science, HIP, Package, Raytracing, Rendering

March 8, 2026 by hgpu

Joint Training on AMD and NVIDIA GPUs

Jon Hu, Thomas Jia, Jing Zhu, Zhendong Yu

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Tags: AMD, AMD Radeon Instinct MI325X, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia H200, ROCm

March 1, 2026 by hgpu

Execution-Centric Characterization of FP8 Matrix Cores, Asynchronous Execution, and Structured Sparsity on AMD MI300A

Aaron Jarmusch, Connor Vitz, Sunita Chandrasekaran

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Tags: AMD, AMD Radeon Instinct MI300A, Benchmarking, Computer science, HIP, Performance, ROCm

February 16, 2026 by hgpu

HetCCL: Accelerating LLM Training with Heterogeneous GPUs

Heehoon Kim, Jaehwan Lee, Taejeoung Kim, Jongwon Park, Jinpyo Kim, Pyongwon Suh, Ryan H. Choi, Sangwoo Lee, Jaejin Lee

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Tags: AMD, AMD FirePro W7800, Computer science, Deep learning, GPU cluster, Heterogeneous systems, LLM, nVidia, Tesla V100

February 8, 2026 by hgpu

Towards Automated Kernel Generation in the Era of LLMs

Yang Yu, Peiyu Zang, Chi Hsu Tsai, Haiming Wu, Yixin Shen, Jialing Zhang, Haoyu Wang, Zhiyou Xiao, Jingze Shi, Yuyu Luo, Wentao Zhang, Chunlei Men, Guang Liu, Yonghua Lin

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Tags: AMD, Benchmarking, Computer science, CUDA, LLM, nVidia, ROCm, Triton

January 25, 2026 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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

Agentic Code Optimization via Compiler-LLM Cooperation

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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