本文旨在梳理作者学习路径，带领读者共同探索 GPU Kernel 性能分析从宏观到微观的技术演进。 引言 作为一名使用eBPF进行CPU性能分析的工程师，在转向学习GPU性能优化分析时，一直在思考GPU上是否有技术也可以实现用户自定义探针式性能分析？学习NVIDIA Nsight ...

通过更改数据加载器默认值（num_workers，batch_size，pin_memory，预取因子等）来充分利用GPU。 通过使用混合精度（fp16，bf16）最大 ...

The optimisation of GPU kernels through performance tuning and auto-tuning approaches has become essential in maximising computational efficiency on modern heterogeneous architectures. Researchers ...

这项由香港科技大学、字节跳动、香港中文大学（深圳）以及南洋理工大学联合开展的研究发表于2026年，研究团队开发出了一套完整的训练系统，让大语言模型学会编写高性能的GPU内核代码。这项突破性工作首次系统性地解决了用强化学习训练AI模型编写内核 ...

Researchers from Stanford, Nvidia, and Together AI have developed a new technique that can discover new solutions to very complex problems. For example, they managed to optimize a critical GPU kernel ...

Graphics processing units (GPUs) are traditionally designed to handle graphics computational tasks, such as image and video processing and rendering, 2D and 3D graphics, vectoring, and more.

Back in 2000, Ian Buck and a small computer graphics team at Stanford University were watching the steady evolution of computer graphics processors for gaming and thinking about how such devices could ...

Graphics processing units (GPUs) were originally designed to perform the highly parallel computations required for graphics rendering. But over the last couple of years, they’ve proven to be powerful ...

The CPU and the GPU share access to some pages of memory. New Linux code helps the kernel keep track of memory holding data for the GPU. The management of video hardware has long been an area of ...