Posts by Fuwei Yang
GEAK V3: Agent-Driven, Repository-Level GPU Kernel Optimization across HIP, Triton, and FlyDSL on AMD GPUs
- 20 July 2026
In the ever-evolving world of GPU computing, optimizing kernels for performance and efficiency is a critical challenge. Hand-tuning kernels demands deep technical expertise and manual iteration. In this blog, you will read about how GEAK v3, the latest iteration of the agent-driven framework, tackles this problem using enhanced features such as task planning, test-harness discovery, patch-based handling of multi-file kernels, dynamic memory system and expert knowledge database. Our results show improvements across three kernel languages (HIP, Triton, and FlyDSL) and both CDNA and RDNA GPUs.
Triton-Based Optimization of Video Sparse Attention on ROCm
- 13 July 2026
Video generation has become a major frontier in generative modeling, driven by large-scale data and increasingly scalable architectures. Among modern architectures, Diffusion Transformers (DiTs) have emerged as a dominant paradigm [1,2,3,4] by representing videos as spatio-temporal token sequences, enabling long-range interactions across frames and spatial regions, as well as flexible multimodal conditioning with text or audio. However, full self-attention scales quadratically with token count, making it increasingly expensive as spatio-temporal resolution and model size grow. Video sparse attention (VSA) [5,6] mitigates this cost by approximating full attention with a subset of informative token interactions, but its practical efficiency in both training and inference depends heavily on hardware-aware Triton kernel implementations.
Out-of-the-Box ROLL Support on AMD GPUs: Accelerating Reinforcement Learning at Scale
- 01 June 2026
Reinforcement learning (RL) is rapidly becoming a foundational technology for Large Language Models (LLMs)—powering key abilities such as reasoning and agentic behaviors. As RL workloads grow more complex and computationally intensive, the ecosystem increasingly depends on scalable, high-performance frameworks that can fully utilize modern GPU clusters.
Accelerating Autonomous Driving Model Training on AMD ROCm™ Software
- 08 December 2025
The autonomous driving industry is undergoing rapid growth, driven by advances in AI and the increasing demand for safer, smarter transportation. At the core of this transformation are complex deep learning models that enable vehicles to perceive, reason, and navigate dynamic environments. Efficiently training these models at scale has become a key challenge for companies working to bring autonomous systems to the road.