Fix cudaErrorIllegalAddress: Illegal Memory Access in CUDA

Overview

cudaErrorIllegalAddress (error code 700) is the GPU equivalent of a segmentation fault. It occurs when a CUDA kernel attempts to access memory outside its valid address space - either reading from or writing to an invalid memory location. This error is particularly challenging because GPU errors are reported asynchronously, making the exact source difficult to pinpoint. However, tools like compute-sanitizer can help identify the exact thread and instruction causing the illegal access. This guide covers systematic debugging approaches for illegal memory access errors in CUDA.

Error Messages

CUDA error: an illegal memory access was encountered
cudaErrorIllegalAddress: an illegal memory access was encountered
CUDA_ERROR_ILLEGAL_ADDRESS
an illegal memory access was encountered at line X

Common Causes

• •Out-of-bounds array access in kernel code
• •Dereferencing null or uninitialized device pointers
• •Accessing freed GPU memory (use after free)
• •Race conditions corrupting pointer values
• •Integer overflow in index calculations
• •Stack overflow in deeply recursive kernels
• •Accessing host memory from device code
• •Misaligned memory access on certain data types

Solutions

# Run with memory checker
compute-sanitizer --tool memcheck ./your_program

# For Python
compute-sanitizer --tool memcheck python your_script.py

# With detailed backtrace
compute-sanitizer --tool memcheck --show-backtrace yes ./your_program

# Output shows exact thread, block, and instruction

# Set before running
export CUDA_LAUNCH_BLOCKING=1

# In Python
import os
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'

# Now errors show at the exact kernel call
# instead of at a later synchronization point

__global__ void kernel(float* data, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    
    // Always check bounds before access
    if (idx >= n) return;
    
    // Validate pointer is not null
    if (data == nullptr) return;
    
    // Safe access
    data[idx] = data[idx] * 2;
}

// Validate after allocation
float* d_data;
cudaError_t err = cudaMalloc(&d_data, size);
if (err != cudaSuccess || d_data == nullptr) {
    printf("Allocation failed\n");
    return;
}

// Check pointer attributes
cudaPointerAttributes attrs;
cudaPointerGetAttributes(&attrs, d_data);
if (attrs.type != cudaMemoryTypeDevice) {
    printf("Not a device pointer!\n");
}

// Common mistake pattern
cudaFree(d_data);
kernel<<<grid, block>>>(d_data, n);  // ERROR: use after free!

// Set pointer to null after free
cudaFree(d_data);
d_data = nullptr;

// Check before use
if (d_data != nullptr) {
    kernel<<<grid, block>>>(d_data, n);
}

Prevention Tips

• ✓Always bounds-check array accesses with if (idx < n)
• ✓Set pointers to nullptr after cudaFree
• ✓Use compute-sanitizer during development
• ✓Validate all pointers before kernel launch
• ✓Be careful with pointer arithmetic overflow
• ✓Use cudaDeviceSynchronize() after kernels during debugging
• ✓Consider using managed memory for simpler debugging
• ✓Implement RAII wrappers for GPU memory

Code Examples

Before (Problematic)

No bounds checking, accessing memory far beyond allocation.

__global__ void kernel(float* data) {
    int idx = threadIdx.x;  // Only valid up to blockDim!
    data[idx * 1000] = 1.0f;  // Way out of bounds!
}

After (Fixed)

Proper global index calculation, bounds check, and null check.

__global__ void kernel(float* data, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n && data != nullptr) {
        data[idx] = 1.0f;
    }
}

Frequently Asked Questions

Related Errors

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