🧠 When Lazy Kernels Hang - A Quirky Tale of CUDA, Streams, and Warmups

Summary:
Ever had your CUDA kernels mysteriously hang, even though everything looked fine? You’re not alone. This post walks through a deceptively simple code snippet that deadlocks — and explains how lazy loading, asynchronous streams, and cold GPUs all conspire to make benchmarking and debugging… interesting. We’ll break down what happens, why it matters, and how to keep your GPU pipelines warm and humming.

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💥 The Problem: A Hanging CUDA Program

Let’s jump straight into the puzzle. Here’s a CUDA C++ program that may either hang or complete depending on which kernel variant you choose:

#include <iostream>

__device__ volatile int s = 0;

__global__ void k1(){
  while (s == 0) {};
}

__global__ void k2(){
  s = 1;
}

__global__ void k(int x) {
  if (x == 0) {
    while (s == 0) {};
  } else {
    s = 1;
  }
}

int main() {
  cudaStream_t s1, s2;
  cudaStreamCreate(&s1);
  cudaStreamCreate(&s2);
#if 1 //!!!hang
  k1<<<1,1,0,s1>>>();
  k2<<<1,1,0,s2>>>();
#else // works
  k<<<1,1,0,s1>>>(0);
  k<<<1,1,0,s2>>>(1);
#endif
  cudaDeviceSynchronize();
  cudaError_t err = cudaGetLastError();
  if (err != cudaSuccess)
    std::cout << cudaGetErrorString(err) << std::endl;
}

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🧪 What’s Going On?

Let’s break it down:

• k1 is a kernel that spins in a loop until s == 1.
• k2 sets s = 1.
• Both are launched in different streams (s1 and s2), so they can execute concurrently.

But here’s the twist: this code hangs in the first case, but not in the second.

Why? Because of lazy kernel loading.

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🐢 Lazy Loading: CUDA’s Optimization Trick

By default, CUDA uses lazy loading (CUDA_MODULE_LOADING=LAZY). This means:

• Kernels aren’t actually loaded onto the GPU until just before they run.
• Loading kernels might require context synchronization - If a kernel is blocked (e.g., k1 is spinning forever waiting on s), it may never yield, so k2’s module never gets loaded.
• Result: k2 never executes → s never becomes 1 → k1 spins forever → 💀 deadlock.

✅ Fix It: Set Module Loading to Eager

To avoid this, set:

export CUDA_MODULE_LOADING=EAGER

This loads all kernels up front, ensuring both k1 and k2 are resident on the GPU before either begins execution.

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🔥 Why Warmups Matter: The Hidden Complexity of GPU Power States

The module loading behavior isn’t the only reason to warm up your GPU before benchmarking. There’s a deeper, more hardware-level reason involving GPU power states.

Here’s what happens when your GPU has been sitting idle:

• It enters a low-power state — sometimes almost completely powered down.
• Components like the memory subsystem, caches, clocks, and even compute cores may be shut off.
• Bringing the GPU back up is a complex orchestration:
  • Power up voltage rails
  • Wake up clock generators
  • Initialize memory controllers, pin drivers, DRAM
  • Perform ECC scrub (initialize memory with ECC tags)

This process takes time — seconds in some cases. So your first CUDA call isn’t benchmarking your kernel; it’s measuring hardware wake-up time.

👁️ How to Observe Power States

• Use nvidia-smi to see GPU power state (P0 = max performance, P8 = idle).
• Warning: Running nvidia-smi itself may change the power state. Sneaky.

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🧠 Other Reasons to Warm Up Your Kernels

1. JIT Compilation: CUDA may compile kernels on-the-fly the first time you call them.
2. Page Faults: Unified memory may need to fault and allocate actual device memory.
3. Memory Pooling: Allocators may initialize memory pools only after the first allocation.
4. Clock Boosting: GPU frequency scaling may take a few seconds to reach peak clock.

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💡 Best Practices for Benchmarking

• Always run a few dummy kernel launches before recording performance.
• Explicitly set CUDA_MODULE_LOADING=EAGER for critical benchmarking.
• Use cudaDeviceSynchronize() after warmups to make sure everything is fully initialized.
• Pin memory ahead of time to avoid host-to-device delays on first transfer.

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🎯 Conclusion

That innocent-looking while (s == 0) loop just taught us some deep truths:

• CUDA uses lazy loading that can lead to hangs if you’re not careful.
• GPUs sleep — and waking them up is not instant coffee.
• Benchmarking isn’t just about timing kernels; it’s about ensuring a consistent environment.

So next time your kernel runs “slow” the first time, don’t blame the compiler — it might just be your GPU stretching and yawning after a nap. 😴