Distributed GPU Compute Across Devices in C# on Browser and Desktop
The article discusses the upcoming capabilities of the SpawnDev.ILGPU and SpawnDev.ILGPU.ML libraries, which enable distributed GPU compute and model inference across multiple devices, including browsers and desktops.
Why it matters
This technology enables more efficient and accessible distributed GPU compute and AI inference, allowing users to leverage their collective device resources for computationally intensive tasks.
Key Points
- 1SpawnDev.ILGPU's new 'AcceleratorType.P2P' backend distributes GPU kernels across connected devices
- 2SpawnDev.ILGPU.ML enables splitting large ML models across multiple devices for distributed inference
- 3Volunteer compute pools allow users to donate idle GPU time for distributed AI workloads in the browser
Details
The article introduces upcoming features in the SpawnDev.ILGPU and SpawnDev.ILGPU.ML libraries that enable distributed GPU compute and model inference across devices. The SpawnDev.ILGPU library's new 'AcceleratorType.P2P' backend allows developers to write a single GPU kernel that can run transparently across multiple connected devices, leveraging the P2P network built with the SpawnDev.WebTorrent library. Additionally, the SpawnDev.ILGPU.ML library will support splitting large ML models across multiple devices for distributed inference, allowing models that don't fit on a single device to run across a user's phone, laptop, tablet, and desktop. The article also mentions the concept of 'volunteer compute pools', where users can opt-in to donate their idle GPU time for distributed AI workloads, similar to Folding@Home, but running in the browser.
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