Claude Authors PyTorch DevLog: Why Host-to-Device Syncs Hurt Performance
ezyang · x · 2026-08-11
PyTorch core contributor Edward Yang shared a deep dive into performance optimization, notably drafted by Anthropic's coding assistant Claude with editing by Yang himself.
The post explores a performance pitfall in PyTorch's eager mode: beyond the well-known device-to-host syncs, host-to-device syncs can also severely degrade execution efficiency.
- The real cost of syncs: In a healthy asynchronous state, the CPU enqueues kernels ahead of time to hide dispatch overhead. A forced sync (like cudaStreamSynchronize) eliminates this lead, causing the GPU queue to empty and resulting in actual performance loss.
- Sneaky triggers: Seemingly harmless API calls can implicitly trigger these syncs, such as torch.tensor(0, device="cuda") or advanced indexing like x[:, (0, 2, 3)].
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