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Because if I'm thinking about the gpu I'm fundamentally thinking about the hardware, the server. Serverless responding to http requests. Sure. I write some cod
by gpm 4y ago
Because if I'm thinking about the gpu I'm fundamentally thinking about the hardware, the server.
Serverless responding to http requests. Sure. I write some code. It gets fed data and returns data. I don't have to know how many cores the server has, or what microcode version the CPU is, or how many other things are running on the server, or if I'm writing an interpreted language (probably the case) even what architecture the CPU is.
But... I need to know all of that if I'm writing GPU code today.
- rcme 4y agoAre you really thinking about the hardware when thinking about the GPU? For instance, if you use pytorch to write a NN, don't you kind of expect it to execute on the GPU without needing to get into the gritty details of it?
- seabass-labrax 4y agoThe specific version of Nvidia CUDA or AMD ROCm that the GPU supports is often very important; some software needs to be compiled from a specific branch or with specific settings to support a given version. Case in point: the official PyTorch website offers four distinct builds for various platforms, two of those being different versions of CUDA. I think it'll still be useful for plenty of people to choose which GPU runs their code, even if it's compatible with any GPU offered by the service. You might want to choose an older, cheaper GPU for basic parallel computation, but as supply catches up with demand for a newer and more energy-efficient model you'll then want to switch to that. There are only so many GeForce 4090s to go round :)