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cudaErrorInvalidPc (718)cudaErrorInvalidPc (error code 718) is a critical error that occurs when the GPU encounters an invalid program counter during kernel execution. This typically indicates the kernel jumped to an invalid memory address, executed illegal instructions, or encountered stack corruption. This error is one of the most challenging to debug as it indicates severe kernel execution problems like infinite recursion, buffer overflows, corrupted function pointers, or compiler bugs. It often results from undefined behavior in device code. This guide covers the common causes, debugging techniques, and preventive measures to identify and fix invalid program counter errors in CUDA applications.
CUDA error: invalid program counter cudaErrorInvalidPc: an illegal instruction was encountered RuntimeError: CUDA error: an illegal instruction was encountered GPU exception: illegal instruction at PC
Use cuda-memcheck and debug builds to identify the exact failure point.
// Compile with debug information
nvcc -g -G kernel.cu -o program
// Run with cuda-memcheck
cuda-memcheck ./program
// Or with specific tools
cuda-memcheck --tool memcheck ./program
cuda-memcheck --tool racecheck ./program
cuda-memcheck --tool synccheck ./program
// Add error checking after kernel
myKernel<<<blocks, threads>>>(args);
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
printf("Kernel launch failed: %s\n", cudaGetErrorString(err));
}
cudaDeviceSynchronize();
err = cudaGetLastError();
if (err != cudaSuccess) {
printf("Kernel execution failed: %s\n", cudaGetErrorString(err));
}Reduce local memory usage and avoid deep recursion.
// Bad: Large local arrays cause stack overflow
__global__ void stackOverflowKernel() {
int large_array[10000]; // Too large for stack!
// ...
}
// Good: Use dynamic allocation or shared memory
__global__ void fixedKernel(int* g_buffer) {
// Option 1: Use global memory
int idx = threadIdx.x;
g_buffer[idx * 10000] = 0;
// Option 2: Use shared memory if size allows
__shared__ int shared_array[1024];
shared_array[idx] = 0;
}
// Check and increase stack size if needed
size_t stack_size;
cudaDeviceGetLimit(&stack_size, cudaLimitStackSize);
printf("Current stack size: %zu\n", stack_size);
// Increase if necessary (in bytes)
cudaDeviceSetLimit(cudaLimitStackSize, 8192); // 8KB per threadAvoid deep or infinite recursion in device code.
// Bad: Unbounded recursion
__device__ int factorial(int n) {
if (n <= 1) return 1;
return n * factorial(n - 1); // Can overflow stack
}
// Better: Iterative version
__device__ int factorial(int n) {
int result = 1;
for (int i = 2; i <= n; i++) {
result *= i;
}
return result;
}
// If recursion needed, limit depth
__device__ int safeRecursive(int n, int depth) {
if (depth > 10) return 0; // Safety limit
if (n <= 1) return 1;
return n * safeRecursive(n - 1, depth + 1);
}
// Set recursion limit
cudaDeviceSetLimit(cudaLimitDevRuntimeSyncDepth, 5);Ensure function pointers are properly initialized before use.
// Device function pointer type
typedef void (*DeviceFunc)(int*);
__device__ void func1(int* data) {
*data = 1;
}
__device__ void func2(int* data) {
*data = 2;
}
__global__ void functionPointerKernel(int* data, int choice) {
DeviceFunc func = nullptr;
// Initialize function pointer
if (choice == 0) {
func = func1;
} else {
func = func2;
}
// Validate before calling
if (func != nullptr) {
func(data);
} else {
// Handle error - don't call null pointer!
*data = -1;
}
}
// Array of function pointers
__device__ DeviceFunc func_table[] = {func1, func2};
__global__ void tableLookupKernel(int* data, int idx) {
// Bounds check!
if (idx >= 0 && idx < 2) {
func_table[idx](data);
}
}Add bounds checking to prevent memory corruption.
__global__ void unsafeKernel(int* data, int size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Bad: No bounds check - can overflow
// data[idx] = idx;
// Good: Bounds checking
if (idx < size) {
data[idx] = idx;
}
}
// Shared memory bounds checking
__global__ void sharedMemKernel() {
__shared__ float s_data[256];
int idx = threadIdx.x;
// Ensure idx is within bounds
if (idx < 256) {
s_data[idx] = idx * 2.0f;
}
__syncthreads();
// Check before accessing neighbors
if (idx > 0 && idx < 255) {
float val = s_data[idx - 1] + s_data[idx + 1];
}
}Ensure code is compiled for compatible GPU architecture.
// Check device compute capability
int device;
cudaGetDevice(&device);
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, device);
printf("Compute capability: %d.%d\n", prop.major, prop.minor);
// Compile for multiple architectures
nvcc -gencode arch=compute_60,code=sm_60 \
-gencode arch=compute_70,code=sm_70 \
-gencode arch=compute_75,code=sm_75 \
-gencode arch=compute_80,code=sm_80 \
kernel.cu
// Or use CMake
set(CMAKE_CUDA_ARCHITECTURES 60 70 75 80 86)
// Check at runtime
if (prop.major < 6) {
printf("GPU too old, requires compute capability 6.0+\n");
exit(1);
}Deep recursion causes stack overflow. GPU stack is limited, and 10000 recursions will exceed it, causing invalid PC error.
__device__ int recursiveSum(int n) {
// Infinite recursion if n is large!
if (n <= 0) return 0;
return n + recursiveSum(n - 1); // Stack overflow for large n
}
__global__ void badKernel() {
int result = recursiveSum(10000); // Will crash!
}Iterative solution avoids recursion entirely. Formula solution is even better - constant time with no stack usage.
__device__ int iterativeSum(int n) {
int sum = 0;
for (int i = 1; i <= n; i++) {
sum += i;
}
return sum;
}
// Or use formula
__device__ int formulaSum(int n) {
return n * (n + 1) / 2;
}
__global__ void goodKernel(int* output) {
int idx = threadIdx.x;
// Iterative solution - no stack issues
output[idx] = iterativeSum(idx);
// Or formula - even better
output[idx] = formulaSum(idx);
}Default stack size varies by GPU but is typically 1KB per thread. You can check with cudaDeviceGetLimit(cudaLimitStackSize) and increase with cudaDeviceSetLimit, but this reduces occupancy. Avoid large local arrays.
Yes, but with limitations. Recursion requires compute capability 2.0+ and should be shallow (< 10 levels). Prefer iterative solutions. Deep recursion quickly exhausts the limited per-thread stack.
Compile with -g -G flags to include debug information. Without these, cuda-memcheck can only show approximate locations. Note that -G disables optimizations, so performance will be worse.
Rarely, but possible. Try different optimization levels (-O0, -O1, -O3), update CUDA toolkit, or simplify code to isolate. Most often it's undefined behavior in your code (buffer overflow, uninitialized pointers, etc.).
General kernel execution failures
Memory access causing execution errors
Invalid instructions from corruption
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