Why does PyTorch GPU matmul give correct results without torch.cuda.synchronize()?

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Summary

This incident examines why PyTorch GPU matrix multiplication returns correct results even without calling torch.cuda.synchronize(). Although CUDA operations are asynchronous, PyTorch inserts implicit synchronization points during certain tensor transfers and operations, which is why the results appear correct.

Root Cause

The core reason is that PyTorch automatically synchronizes when transferring data from GPU to CPU. The line:

C_gpu_cpu = C_gpu.cpu()

forces the CPU to wait until the GPU finishes computing C_gpu. This implicit synchronization ensures correctness even without an explicit torch.cuda.synchronize() call.

Why This Happens in Real Systems

Real GPU frameworks (including PyTorch) introduce synchronization implicitly for safety and usability:

  • Device-to-host transfers block until GPU work completes
  • Certain CUDA runtime calls enforce ordering guarantees
  • PyTorch’s autograd engine inserts sync points when needed
  • cuBLAS kernels (used for matmul) complete before dependent operations proceed

These behaviors prevent users from accidentally reading incomplete GPU results.

Real-World Impact

Implicit synchronization leads to:

  • Correct results even when users forget to synchronize
  • Confusion about when synchronization is required
  • Misleading performance measurements because hidden sync points slow down timing
  • Safer default behavior for beginners at the cost of reduced transparency

Example or Code (if necessary and relevant)

Below is a minimal example showing where synchronization implicitly occurs:

import torch

A = torch.randn(5000, 5000, device="cuda")
B = torch.randn(5000, 5000, device="cuda")

C = A @ B  # asynchronous launch

# Implicit synchronization happens here:
C_cpu = C.cpu()  # blocks until GPU finishes

How Senior Engineers Fix It

Experienced engineers understand where implicit synchronization occurs and use explicit sync only when needed:

  • Use torch.cuda.synchronize() for accurate timing
  • Avoid unnecessary host-device transfers
  • Use CUDA streams to control execution order
  • Profile kernels to detect hidden sync points
  • Batch operations to reduce synchronization overhead

They treat synchronization as a performance tool, not a correctness requirement.

Why Juniors Miss It

Less experienced engineers often assume:

  • Asynchronous means “results may be wrong”, which is not true
  • GPU operations behave like CPU operations, missing the subtleties of CUDA streams
  • Synchronization is required for correctness, when in reality it’s required mainly for timing and performance tuning
  • Data transfers are cheap, not realizing they enforce blocking behavior

The confusion arises because PyTorch hides many CUDA details to simplify the user experience.

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