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cudaErrorInvalidAddressSpace (717)cudaErrorInvalidAddressSpace (error code 717) occurs when a CUDA operation tries to access memory from an incompatible address space. CUDA has multiple memory spaces (global, shared, local, constant) each with specific access rules and restrictions. This error typically appears when mixing pointer types incorrectly, attempting operations on wrong memory spaces, or when address space qualifiers don't match the actual memory location. This guide explains CUDA's memory hierarchy, common address space mistakes, and best practices for correct memory space usage in your kernels.
CUDA error: invalid address space cudaErrorInvalidAddressSpace: invalid address space RuntimeError: CUDA error: invalid address space Error: Operation not permitted on this address space
Apply appropriate qualifiers for different memory spaces.
// Shared memory - use __shared__ qualifier
__global__ void sharedMemKernel() {
__shared__ float shared_data[256];
int idx = threadIdx.x;
shared_data[idx] = idx * 2.0f;
__syncthreads();
}
// Global memory - no qualifier needed
__global__ void globalMemKernel(float* global_data) {
int idx = threadIdx.x;
global_data[idx] = idx * 2.0f;
}
// Constant memory - use __constant__ qualifier
__constant__ float const_data[256];
// Local/register memory - automatic for local variables
__global__ void localMemKernel() {
float local_var = 5.0f; // In registers or local memory
}Don't use operations designed for one address space on another.
__global__ void correctAddressSpaces() {
__shared__ float s_data[256];
int idx = threadIdx.x;
// Wrong: Can't use cudaMemcpy on shared memory
// cudaMemcpy(s_data, source, size, cudaMemcpyDeviceToDevice);
// Correct: Use direct assignment or thread cooperation
s_data[idx] = idx * 2.0f;
__syncthreads();
// For global memory, this is fine
float* g_data;
// cudaMemcpy(g_data, source, size, cudaMemcpyDeviceToDevice);
}
// Wrong: Atomic on constant memory
__constant__ int const_val;
// atomicAdd(&const_val, 1); // ERROR!
// Correct: Atomic on global or shared memory
__global__ void atomicKernel(int* g_data) {
__shared__ int s_data;
atomicAdd(g_data, 1); // OK on global
atomicAdd(&s_data, 1); // OK on shared
}Match pointer parameters with actual memory space.
// Function accepting global memory pointer
__device__ void processGlobal(float* global_ptr) {
*global_ptr = 42.0f;
}
// Function accepting shared memory pointer
__device__ void processShared(float* shared_ptr) {
*shared_ptr = 42.0f;
}
__global__ void kernel(float* g_data) {
__shared__ float s_data[256];
int idx = threadIdx.x;
// Correct usage
processGlobal(&g_data[idx]); // Pass global memory
processShared(&s_data[idx]); // Pass shared memory
// Modern CUDA can infer, but be explicit for clarity
}Properly allocate and access dynamic shared memory.
// Kernel with dynamic shared memory
__global__ void dynamicSharedKernel(float* g_output) {
// Declare extern shared memory
extern __shared__ float s_data[];
int idx = threadIdx.x;
s_data[idx] = idx * 2.0f;
__syncthreads();
g_output[idx] = s_data[idx];
}
// Launch with shared memory size
int main() {
float* d_output;
cudaMalloc(&d_output, 256 * sizeof(float));
// Third parameter is shared memory bytes
dynamicSharedKernel<<<1, 256, 256 * sizeof(float)>>>(d_output);
cudaDeviceSynchronize();
cudaFree(d_output);
return 0;
}Use generic addressing for flexibility when needed.
// Generic pointer that can point to any space
__device__ void genericFunction(void* ptr) {
// Check what space pointer is in
cudaPointerAttributes attr;
// Note: This is host-side function, example for concept
// In device code, use appropriate operations
// Cast to specific type when space is known
}
// Address space qualifiers (CUDA 9.0+)
__device__ void restrictedFunction(
float* __restrict__ global_ptr, // Hint it's global
float* __restrict__ shared_ptr) { // Different space
// Compiler can optimize better with __restrict__
}
// Use generic addressing in device code
__device__ float* selectMemory(float* g_ptr, bool use_global) {
__shared__ float s_data[256];
return use_global ? g_ptr : s_data;
}Each memory space supports different operations.
__global__ void memoryOperations(float* g_data) {
__shared__ float s_data[256];
__shared__ int s_atomic;
int idx = threadIdx.x;
// Global memory: All operations supported
atomicAdd(&g_data[idx], 1.0f);
float val = g_data[idx];
// Shared memory: Most operations, fast intra-block
__syncthreads(); // Only works with shared memory
s_data[idx] = val;
atomicAdd(&s_atomic, 1);
// Constant memory: Read-only, cached
// extern __constant__ float c_data[256];
// float c_val = c_data[idx]; // OK
// c_data[idx] = 5.0f; // ERROR - read only
// Texture memory: Special access patterns
// Use texture objects or references
}
// Wrong: Trying to modify constant memory
__constant__ float const_array[256];
__global__ void wrongKernel() {
// const_array[0] = 1.0f; // ERROR!
}
// Correct: Initialize from host
void hostCode() {
float h_data[256];
cudaMemcpyToSymbol(const_array, h_data, 256 * sizeof(float));
}Missing __shared__ qualifier means s_data is in local memory, not shared. __syncthreads() doesn't work correctly with local memory.
__global__ void badKernel(float* g_input, float* g_output) {
// Missing __shared__ qualifier!
float s_data[256];
int idx = threadIdx.x;
s_data[idx] = g_input[idx];
// This will fail - s_data is not actually in shared memory
__syncthreads(); // May cause undefined behavior
g_output[idx] = s_data[idx];
}Proper __shared__ qualifier ensures memory is in shared space. __syncthreads() works correctly for intra-block synchronization.
__global__ void goodKernel(float* g_input, float* g_output) {
// Correct __shared__ qualifier
__shared__ float s_data[256];
int idx = threadIdx.x;
s_data[idx] = g_input[idx];
// Proper synchronization with shared memory
__syncthreads();
// Process shared data
if (idx > 0) {
s_data[idx] += s_data[idx - 1];
}
__syncthreads();
g_output[idx] = s_data[idx];
}CUDA has several memory spaces: Global (large, slow, accessible by all), Shared (fast, per-block, limited size), Local (per-thread, registers or local mem), Constant (read-only, cached), and Texture (read-only, cached, 2D spatial locality). Each has specific use cases and restrictions.
No, cudaMemcpy only works with global memory. Shared memory is per-block and exists only during kernel execution. Use direct assignments or thread cooperation to move data in/out of shared memory.
If you forgot __shared__ qualifier, your array is in local memory, not shared. __syncthreads() only synchronizes threads within a block for shared memory. Also, __syncthreads() must be called by all threads or none (no conditional calls in divergent code).
It depends on GPU architecture. Modern GPUs typically have 48-96KB per SM. Check with cudaDeviceGetAttribute(cudaDevAttrMaxSharedMemoryPerBlock). Excessive shared memory use reduces occupancy.
Memory access violations across spaces
Invalid memory space parameters
Address alignment in different spaces
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