I am trying to test both variants of VGICP using the same data set and the cuda variant seems to have a bug and is simply not returning real results. When I use the cuda variant it seems to be taking some time to do the covariance calcs, but the LM optimization just returns all ...
它内部的cudaDeviceReset()调用将释放当前设备上以前分配的所有内存。因此，d_data变为无效，并导致内核...
allocateDeviceMemoryis called from host code and is expected to run on the host. It should not be marked with__device__. executeKernelis host code. It should not be marked with__global__. deallocateMemoryis host code. It should not be marked with__device__. perform...
[1] Note that the Next-Gen CUDA debugger only supports local debugging. Remote debugging is not currently supported.Open the Sample Project and Set Breakpoints Open the sample project in the CUDA SDK called matrixMul. For assistance in locating sample applications, see Working with Samples. ...
printf("After populateMemory 2: bucket 0, 1 .. 63: %d %d .. %d\n", bucket[0], bucket[1], bucket[numThreads]); cudaFree(pool); exit(0); } In the code example, you create a memory pool (helpfully calledpool!) of size 4096 integers. You then assign a section of ...
(&prop, device))\n" " printf(\"%d.%d \", prop.major, prop.minor);\n" " }\n" " return 0;\n" "}\n") execute_process( COMMAND "${CMAKE_CUDA_COMPILER}" "--run" "${cufile}" WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/" RESULT_VARIABLE nvcc_res OUTPUT_VARIABLE ...
The workaround described in Multiple Debuggers does not work with MPI applications. If CUDA_VISIBLE_DEVICES is set, it may cause problems with the GPU selection logic in the MPI application. It may also prevent CUDA IPC working between GPUs on a node. In order to start multiple CUDA-GDB ...
printf("bar called\n");} void bar(){ foo(); k<<<1,1>>>(); cudaDeviceSynchronize(); } # cat main.cpp void bar(); int main(){ bar();} # g++ -fPIC -c test.cpp # g++ -fPIC -shared test.o -o libfinal.so # nvcc -shared -Xcompiler -fPIC libfinal.so cudatest.cu -o ...
printf("Optimal <H> = %lf\n", opt_val); The main components required are the parameterized ansatz CUDA-Q kernel expression, shown in the code example as a lambda taking astd::vector<double>. The actual body of this lambda is dependent on the problem at hand, but you are free to bui...
printf("This function is defined to run on the GPU.\n"); } int main() { CPUFunction(); GPUFunction<<<1, 1>>>(); cudaDeviceSynchronize(); } 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.