GDS

Magnum IO GPUDirect Storage

GPUDirect Storage Overview

GDS enables a direct data path for direct memory access (DMA) transfers between GPU memory and storage. This direct path increases system bandwidth and decreases the latency and utilization load on the CPU.

Benefits of GDS include:

  • Reduces the performance impact and dependence on CPUs to process storage data transfer.
  • Performance force multiplier on top of the compute advantage for computational pipelines that are fully migrated to the GPU so that the GPU, rather than the CPU, has the first and last touch of data that moves between storage and the GPU.
  • Supports interoperability with other OS-based file access, which enables data to be transferred to and from the device by using traditional file IO, which is then accessed by a program that uses the cuFile APIs.

Benefits that are provided by the cuFile APIs and their implementations:

  • A family of APIs that provide CUDA applications with the best-performing access to local or distributed file and block storage.

    Block storage validation might be added in the future.

  • These APIs are consistent with the long-term direction of the Linux community, for example, with respect to peer to peer RDMA.
  • When transferring to and from the GPU, increased performance relative to existing standard Linux file IO.
  • Greater ease of use by removing the need for the careful expert management of memory allocation and data movement.
  • A simpler API sequence that is relative to existing implicit file-GPU data movement methods, which require a more complex management of memory and data movement on and between the CPU and GPU.
  • Broader support for unaligned transfers than POSIX pread and pwrite APIs with O_DIRECT.

    In the application code, the POSIX APIs require a buffered IO or unaligned handling.

  • Generality across a variety of storage types that span various local and distributed filesystems, block interfaces, and namespace systems, including standard Linux and third-party solutions.

Here are the benefits that are provided by the Stream subset of the cuFile APIs:

  • Asynchronous offloaded operations are ordered with respect to a CUDA stream.
    • O after compute: The GPU kernel produces data before it is transferred to IO.
    • Compute after IO: After the data transfer is complete, the GPU kernel can proceed.
  • Available concurrency across streams.
    • Using different CUDA streams allows the possibility of concurrent execution and the concurrent use of multiple DMA engines.

The full GDS documentation is available at GPUDirect Storage.

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