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There was a paper a few months back in deep reinforcement learning that got record setting RL results using only a CPU [0]. Previously, these algorithms would p

There was a paper a few months back in deep reinforcement learning that got record setting RL results using only a CPU [0]. Previously, these algorithms would play fewer games and run a gpu over and over on the few games they had played. By using a CPU you can generate more samples that are up to date with your learning algorithm. It sounds obvious in hindsight, but you can't exactly run 60 atari sims on a gpu. [0] - https://arxiv.org/abs/1602.01783 https://arxiv.org/abs/1602.01783

lightcatcher 10y ago
> It sounds obvious in hindsight, but you can't exactly run 60 atari sims on a gpu. Why not? It's certainly easier to run parallel Atari sims on CPU, because CPU programs are typically written as single-threaded or with parameterized number of threads. Running parallel Atari on GPU is completely possible, with either running an Atari game on the each of ~30 SMs or each of the 32 * n_SMs ~= 1000 warps. However, because GPU code is typically written and delivered as kernels which utilize the full GPU, this type of embarrassing parallelism over SMs or warps typically can't be gained from using an existing library.
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