Cuda-gdb aborted (original) (raw)
version
exec: /usr/local/cuda/bin/cuda-gdb-python3.10-tui --version
NVIDIA (R) cuda-gdb 12.6
Portions Copyright (C) 2007-2024 NVIDIA Corporation
Based on GNU gdb 13.2
Copyright (C) 2023 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later http://gnu.org/licenses/gpl.html
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
code
include <cuda.h>
include <stdio.h>
// compute vector sum C = A + B
// each thread peforms one pair-wise addition
global void vecAddKernel(float *A, float *B, float *C, int n) {
int i = threadIdx.x + blockDim.x * blockIdx.x;
if (i < n) {
C[i] = A[i] + B[i];
}
}
// What is the canonical way to check for errors using the CUDA runtime API? - Stack Overflow
define gpuErrchk(ans) { gpuAssert((ans), FILE, LINE); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true) {
if (code != cudaSuccess) {
fprintf(stderr, “GPUassert: %s %s %d\n”, cudaGetErrorString(code), file, line);
if (abort) {
exit(code);
}
}
}
inline unsigned int cdiv(unsigned int a, unsigned int b) {
return (a + b - 1) / b;
}
void vecAdd(float *A, float *B, float *C, int n) {
float *A_d, *B_d, *C_d;
size_t size = n * sizeof(float);
cudaMalloc((void **)&A_d, size);
cudaMalloc((void **)&B_d, size);
cudaMalloc((void **)&C_d, size);
cudaMemcpy(A_d, A, size, cudaMemcpyHostToDevice);
cudaMemcpy(B_d, B, size, cudaMemcpyHostToDevice);
const unsigned int numThreads = 256;
unsigned int numBlocks = cdiv(n, numThreads);
vecAddKernel<<<numBlocks, numThreads>>>(A_d, B_d, C_d, n);
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
cudaMemcpy(C, C_d, size, cudaMemcpyDeviceToHost);
cudaFree(A_d);
cudaFree(B_d);
cudaFree(C_d);
}
int main() {
const int n = 1000;
float A[n];
float B[n];
float C[n];
// generate some dummy vectors to add
for (int i = 0; i < n; i += 1) {
A[i] = float(i);
B[i] = A[i] / 1000.0f;
}
vecAdd(A, B, C, n);
// print result
for (int i = 0; i < n; i += 1) {
if (i > 0) {
printf(“, “);
if (i % 10 == 0) {
printf(”\n”);
}
}
printf(“%8.3f”, C[i]);
}
printf(“\n”);
return 0;
}
compile
nvcc -g -G vector_add.cu -o vector_add
device
Wed Nov 20 02🔞33 2024
±----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.6 |
|-----------------------------------------±-----------------------±---------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA A100-SXM4-80GB On | 00000000:00:0B.0 Off | 0 |
| N/A 33C P0 62W / 400W | 0MiB / 81920MiB | 0% Default |
| | | Disabled |
±----------------------------------------±-----------------------±---------------------+
| 1 NVIDIA A100-SXM4-80GB On | 00000000:00:0C.0 Off | 0 |
| N/A 31C P0 62W / 400W | 0MiB / 81920MiB | 0% Default |
| | | Disabled |
±----------------------------------------±-----------------------±---------------------+
| 2 NVIDIA A100-SXM4-80GB On | 00000000:00:0D.0 Off | 0 |
| N/A 33C P0 63W / 400W | 0MiB / 81920MiB | 0% Default |
| | | Disabled |
±----------------------------------------±-----------------------±---------------------+
| 3 NVIDIA A100-SXM4-80GB On | 00000000:00:0E.0 Off | 0 |
| N/A 32C P0 61W / 400W | 0MiB / 81920MiB | 0% Default |
| | | Disabled |
±----------------------------------------±-----------------------±---------------------+
±----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
±----------------------------------------------------------------------------------------+
debug
cuda-gdb vector_add
error info
NVIDIA (R) cuda-gdb 12.6
Portions Copyright (C) 2007-2024 NVIDIA Corporation
Based on GNU gdb 13.2
Copyright (C) 2023 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later http://gnu.org/licenses/gpl.html
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
Type “show copying” and “show warranty” for details.
This CUDA-GDB was configured as “x86_64-pc-linux-gnu”.
Type “show configuration” for configuration details.
For bug reporting instructions, please see:
https://forums.developer.nvidia.com/c/developer-tools/cuda-developer-tools/cuda-gdb.
Find the CUDA-GDB manual and other documentation resources online at:
https://docs.nvidia.com/cuda/cuda-gdb/index.html.
For help, type “help”.
Type “apropos word” to search for commands related to “word”…
Reading symbols from vector_addition…
(cuda-gdb) b main
Breakpoint 1 at 0xae0a: file /root/lectures/lecture_002/vector_addition/vector_addition.cu, line 53.
(cuda-gdb) run
Starting program: /root/lectures/lecture_002/vector_addition/vector_addition
warning: Error disabling address space randomization: Operation not permitted
[Thread debugging using libthread_db enabled]
Using host libthread_db library “/usr/lib/x86_64-linux-gnu/libthread_db.so.1”.
Breakpoint 1, main () at /root/lectures/lecture_002/vector_addition/vector_addition.cu:53
Fatal signal: Segmentation fault
----- Backtrace -----
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0xffffffffffffffff ???
A fatal error internal to GDB has been detected, further
debugging is not possible. GDB will now terminate.
This is a bug, please report it. For instructions, see:
https://forums.developer.nvidia.com/c/developer-tools/cuda-developer-tools/cuda-gdb.
Segmentation fault (core dumped)