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Questions regarding the project #41
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Since Emu has wider cross-platform support than OpenCL and CUDA, it's particularly well-suited for expensive on-device computation. The idea here is that instead of off-loading expensive compute to a server, doing it on the user's device reduces latency and better respects user's privacy. This is especially useful in ML (where Apple, Google, and others actively work on on-device machine learning specifically for privacy) but even for stuff like GIS/mapping, data processing, recommendation systems, etc.
WebGPU is a bit of a misnomer. Emu is built on top of
For the most part stable. Emu is a transparent abstraction over WebGPU, meaning that you can always remove the abstraction and work directly with WebGPU at no cost. This switching between Emu and the underlying WebGPU is not something you will likely do often and naturally isn't stable yet since internal implementation details will probably change in the near future.
You can expect performance as close as possible to Vulkan/Metal/DX that remains cross-platform. If you don't want to write 3 different versions of your code, then this is probably the best performance you can get. I should mention - the biggest change yet to come to Emu is a few internal optimizations (no API changes). These are mentioned here. |
I am interested in using emu but I have a few questions regarding this project:
What does it mean by being privacy-focused compute?
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