What is GPUDirect Storage

What is GPUDirect Storage?

For AI, machine learning, and working with large amounts of data, you need specialized hardware to do the job well. These processes use a lot of data, which means they need hardware that can give them the bandwidth and processing power they need. Here comes GPUDirect Storage from NVIDIA.

GPUDirect is a Direct Memory Access (DMA) technology that lets the NVIDIA graphics card on a device access the storage unit so that data can be sent and received more easily. This technology can also be used in networked systems where data can be shared without the CPU or system memory being involved.

The NVIDIA GPUDirect technology promises to speed up the device and cut down on the time it takes to load, all while putting less stress on the CPU.

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As GPU-accelerated applications process more and more data sets, it becomes more important to make storage and GPU memory less crowded. For example, to train a neural network, you need to be able to look at several sets of files several times a day. Your AI model’s training time will be greatly affected by how well data is sent to the GPU.

There are also checkpoints in training for deep learning. During this process, trained weights are saved to disks at different times. Since it’s in the important I/O path, it’s possible to speed up model recovery by cutting down on the extra work.

How Does GPU Direct Storage Work?

When data needs to be processed by the graphics card in a traditional computer, it first goes from the storage to the CPU, which copies the data into the RAM. Then, the RAM sends the data back to the CPU, which sends it to the GPU for the last time. This makes data flow to the GPU a complicated process that needs to be handled and managed by the CPU.

GPUDirect changes the way that data should be handled in a system. Its way of getting to the data directly from the storage cuts processing times by a lot. In order to do that, it uses the highly specialized building blocks and execution engine of the graphics card.

With this technology, the data doesn’t have to compete with other processes for memory bandwidth. This means that the data doesn’t have to wait as long to get where it needs to go.

Deep Learning and AI tasks can be done very quickly with the help of the NVIDIA Magnum I/O software stack and the DALI (Data Loading Library).

There are benefits to GPUDirect Storage

GPUDirect Storage’s fast access to data from multiple sources is one of its most important features. Even if system memory and internal NVMe are used, RAID storage and NVMe-oF are still possible. Bandwidths that work in both directions allow for more complex choreography, which can bring data from local disk caches or storage area networks.

Data structures in the system memory, also make it easier for CPUs to work together. GPUDirect Storage is helpful in many ways, such as:

  • With direct data transfers between the GPU and the storage, the bandwidth is two to eight times as high.
  • Transfers of data that are fast and don’t bounce buffers.
  • Stable data transfers with more GPUs working at the same time.

When DMA engines are close to storage, they cause less interference with the load on the GPU. When GPUDirect Storage is bigger, the ratio of bandwidth to fractional CPU use can be higher. The GPU is the hardware with the most IO bandwidth, and it also has the most comprehensive bandwidth-computing engine.

All of the above benefits can be had no matter where the data is stored. When your systems switch to GPU execution, GPUDirect storage becomes a force multiplier. This change is especially helpful when the system memory can’t keep up with the growing size of the datasets.

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How Does Performance Improve With GPUDirect Storage?

A new technology called GPUDirect Storage establishes a direct data connection between local or distant storage and GPU memory. It avoids the annoyance of making multiple copies using the bounce buffer in the CPU memory. Instead of taxing the CPU, it enables storage to transport data straight to or from the GPU.

GPUDirect Storage technology is independent of the storage location. In a rack, enclosure, or storage area network, it is manageable (SAN). Large data blocks are sent via PCIe using Direct Memory Access (DMA), which frees up computer power for other tasks by using a copy engine. GPUs, NVMe drivers, storage controllers, and other storage-related parts often have DMA engines.

Advantages of GPUDirect Storage

NVIDIA asserts that if implemented, GPUDirect would boost and improve system performance while offering a significant amount of parallel processing capacity. This also implies that the technology may be utilized for data warehousing and artificial intelligence, which both handle enormous volumes of data. Some advantages of GPUDirect include the following:

  1. Reduces CPU and System Memory Utilization: By minimizing the amount of I/O (Input/Output) activities, GPUDirect storage technology helps to lessen the load on the CPU and system memory.
  2. Reduces load times and boosts hardware data decompression rate: GPUDirect speeds up the process and helps unload the instructions that will be transmitted to the CPU if you play games or carry out any specialized tasks that call for the CPU to handle huge quantities of data.
  3. Facilitates Deep Learning and AI: These complicated processes need multi-level processing. As opposed to processing big amounts of data the conventional way, GPUDirect allows you to handle enormous chunks of data rather rapidly.
  4. Bypass CPU Bottleneck Issue: GPUDirect improves system efficiency by lowering the processing strain and overheads placed on the CPU, allowing it to concentrate more on logical activities than on managing data transmission to the GPU.
  5. Enhances gaming performance on consoles and supported hardware: The data and resources of the games you play will load considerably more quickly thanks to the GPU’s parallel processing capabilities. By incorporating GPUDirect technology into games, graphics quality, draw distance, environmental assets, and particle effects may all be improved.

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Limitations of GPUDirect Storage

Despite all of its advantages, NVIDIA GPUDirect is still a very young technology, thus it has certain limitations. This technology may not be widely used due to problems including restricted compatibility and a difficult setup procedure, among other things. This technology has a few shortcomings, including:

Limited I/O Compatibility: Since NVIDIA GPUDirect I/O acceleration technology was only introduced in 2019, it is only partially compatible with other systems.

Additional Setup is Necessary for Operation: By default, NVIDIA GPUDirect Storage is not turned on. For this to work on their devices, users will need to manually set up and install the required drivers and software. Magnum I/O software stack installation is also necessary to enable file systems, do big data operations, and perform AI-related tasks.

Limited Hardware and Applications Support: Due to GPUDirect’s newness, there is a dearth of software that can effectively use this functionality. As a result, GPUDirect offers only a limited amount of support for outdated systems. To function, the functionality needs a CUDA parallel computing platform and graphics cards from the 8. x series or above.

Conclusion

Advanced computer hardware and software are needed for emerging technologies like artificial intelligence (AI) and machine learning to reach their full potential. By integrating effective data transport and management techniques, GPUDirect Storage is a cutting-edge choice that raises overall performance. This technology will undoubtedly enhance data analytics while also providing ease, longevity, and financial value.