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cudaErrorLaunchFailure (719)cudaErrorLaunchFailure (error code 719) indicates that a CUDA kernel execution failed. This is often caused by illegal memory access within the kernel, but the error is reported asynchronously, making it tricky to debug. This error is the GPU equivalent of a segmentation fault - your kernel tried to access memory it shouldn't, performed an illegal operation, or encountered an unrecoverable error during execution. This guide covers systematic approaches to identify and fix the root cause of kernel launch failures in your CUDA code.
CUDA error: unspecified launch failure cudaErrorLaunchFailure: unspecified launch failure CUDA kernel errors might be asynchronously reported an illegal memory access was encountered CUDA error: device-side assert triggered
CUDA errors are asynchronous by default. Enable sync checking to find the exact failing kernel.
# Set environment variable before running
export CUDA_LAUNCH_BLOCKING=1
# In Python
import os
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
# This makes all CUDA calls synchronous
# Error will be reported at the exact line that caused it
# Also check every CUDA call
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
printf("CUDA error: %s\n", cudaGetErrorString(err));
}compute-sanitizer detects memory errors like out-of-bounds access.
# Run with memory checker
compute-sanitizer --tool memcheck ./your_program
# For Python scripts
compute-sanitizer --tool memcheck python your_script.py
# More detailed output
compute-sanitizer --tool memcheck --show-backtrace yes ./your_program
# Check for race conditions
compute-sanitizer --tool racecheck ./your_programValidate all array indices before access.
// BAD: No bounds checking
__global__ void kernel(float* data, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
data[idx] = data[idx] * 2; // Crashes if idx >= n
}
// GOOD: With bounds checking
__global__ void kernel(float* data, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n) { // Guard against out-of-bounds
data[idx] = data[idx] * 2;
}
}
// Calculate grid size correctly
int threadsPerBlock = 256;
int blocksPerGrid = (n + threadsPerBlock - 1) / threadsPerBlock;
kernel<<<blocksPerGrid, threadsPerBlock>>>(data, n);Validate pointers before launching kernels.
// Validate device pointers
float* d_data;
cudaError_t err = cudaMalloc(&d_data, size);
if (err != cudaSuccess || d_data == nullptr) {
fprintf(stderr, "cudaMalloc failed: %s\n", cudaGetErrorString(err));
return;
}
// In PyTorch, check tensor device
def safe_kernel_launch(tensor):
assert tensor.is_cuda, "Tensor must be on GPU"
assert tensor.is_contiguous(), "Tensor must be contiguous"
assert tensor.data_ptr() != 0, "Tensor has null data pointer"Use printf to trace execution and find the failing thread.
__global__ void debugKernel(float* data, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Only print from a few threads to avoid flooding
if (idx < 10) {
printf("Thread %d: accessing data[%d]\n", idx, idx);
}
if (idx >= n) {
printf("ERROR: Thread %d out of bounds (n=%d)\n", idx, n);
return;
}
// Add assertions
assert(idx >= 0 && idx < n);
data[idx] = data[idx] * 2;
}Windows kills kernels running longer than 2 seconds by default.
# Increase TDR timeout (Windows Registry)
# HKEY_LOCAL_MACHINE\System\CurrentControlSet\Control\GraphicsDrivers
# Add DWORD: TdrDelay = 60 (seconds)
# Add DWORD: TdrDdiDelay = 60
# Or disable TDR (not recommended for production)
# TdrLevel = 0
# Better solution: break up long-running kernels
// Instead of one huge kernel
for (int batch = 0; batch < total_work; batch += batch_size) {
processKernel<<<grid, block>>>(data, batch, batch_size);
cudaDeviceSynchronize(); // Allow system to breathe
}threadIdx.x only handles threads within a block (max 1024). For larger arrays, must use blockIdx.x too.
__global__ void addVectors(float* a, float* b, float* c) {
int i = threadIdx.x; // Only valid up to 1024!
c[i] = a[i] + b[i]; // Crashes for large arrays
}
// Launch with too many threads
addVectors<<<1, N>>>(a, b, c); // N > 1024 failsUses global thread index, bounds checking, proper grid sizing, and error checking.
__global__ void addVectors(float* a, float* b, float* c, int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) { // Bounds check
c[i] = a[i] + b[i];
}
}
// Proper launch configuration
int threadsPerBlock = 256;
int blocksPerGrid = (n + threadsPerBlock - 1) / threadsPerBlock;
// Validate before launch
assert(a != nullptr && b != nullptr && c != nullptr);
assert(n > 0);
addVectors<<<blocksPerGrid, threadsPerBlock>>>(a, b, c, n);
cudaError_t err = cudaDeviceSynchronize();
if (err != cudaSuccess) {
printf("Kernel failed: %s\n", cudaGetErrorString(err));
}Set CUDA_LAUNCH_BLOCKING=1 to get synchronous errors. Use compute-sanitizer for memory access errors. The "unspecified" nature is because GPU errors are reported asynchronously.
With CUDA_LAUNCH_BLOCKING=1, the error occurs at the exact cudaLaunchKernel call. You can also add cudaDeviceSynchronize() and cudaGetLastError() after each kernel to isolate the issue.
Yes, a kernel that writes out-of-bounds can corrupt other allocations. This can cause cascading failures in subsequent kernels. Consider using compute-sanitizer in development.
Likely an index overflow or incorrect grid size calculation. Ensure you're using proper indexing (blockIdx.x * blockDim.x + threadIdx.x) and bounds checking. Also check for integer overflow in size calculations.
Specific case of invalid memory access
Can cause null pointers leading to launch failure
Device-side assert triggered
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