NVIDIA发布KvikIO读取GPU显存教程
NVIDIA发布技术教程，演示如何用KvikIO Python API通过GPUDirect Storage（GDS）把文件直接读入GPU显存。教程依次配置存储I/O、分配1 GiB的CuPy缓冲区、通过KvikIO的cuFile接口完成读取，再用gds_stats查看cuFile三级统计，判断数据走的是nvidia-fs加速路径还是经CPU中转的POSIX兼容路径。NVIDIA强调KvikIO与cuFile各自独立决定路径，因此调用到cuFile接口并不等于确认已启用GDS加速路径。文章还说明KvikIO属于面向数据科学的CUDA-X库，并被cuDF等库使用。
NVIDIA称GDS可避免经CPU内存的多余中转并降低系统整体CPU占用，并将其视为模型算力与训练数据集规模持续增长下的必要能力。
GPU applications are processing ever-growing datasets with increasingly powerful accelerators. According to the 2025 Stanford AI Index, compute for notable AI models has been doubling approximately every five months, while the size of datasets used to train LLMs has been doubling approximately every eight months. As computation and data scale together, moving data efficiently from storage becomes a necessity for keeping accelerators supplied.
NVIDIA Magnum IO GPUDirect Storage (GDS) addresses this challenge by enabling a direct data path between supported storage and GPU memory. This avoids unnecessary staging through CPU memory and reduces overall CPU utilization in the system. GDS is supported by a growing ecosystem that includes many major storage vendors.
At the application layer, Python plays a central role in modern AI development. As of August 2025, nearly half of all new AI projects on GitHub were built primarily in Python while the main application interface to GDS is the cuFile C API.
NVIDIA KvikIO, a Python and C++ library for high-performance file I/O, bridges this gap. It provides high-level, idiomatic Python and C++ interfaces, along with additional controls over how I/O requests are performed.
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