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After using the compiler flags suggested by n. 1.8e9-where's-my-share m. to diagnose memory access violations I've discovered that the memory corruption is indeed caused by a function that is called just before the one in my original question.

The offending function reads in data from a text file using sscanf, and would allocate a 3-element array for each line of the file (for the 3 numbers to be read in per line). The next part is conjecture, but I think that the problem arose because sscanf returns a NULL at the end of a sequence it reads. I'm surmising that this NULL was written to the next byte along from the 3 allocated, such that the next time malloc tried to allocate data the first thing it saw was a NULL, causing it to return without actually having allocated any space. Then the next function to try and use the allocated memory would come along and crash because it's trying to access unassigned memory that malloc had reported to be allocated.

I was able to fix the bug by changing the size of the allocated array in the read function from 3 to 4 elements. This would seem to allow the NULL character to be stored without it interfering with subsequent memory allocations.

I think that nvidia-smi is only available so far for NVIDIA discrete GPUs, but Jetsons have an integrated GPU (sharing physical memory with system).

You can find details about your GPU specs with deviceQuery utility in CUDA samples:

torch.version.cuda is just defined as a string. It doesn't query anything. It doesn't tell you which version of CUDA you have installed. It only tells you that the PyTorch you have installed is meant for that (10.2) version of CUDA. But the version of CUDA you are actually running on your system is 11.4.

If you installed PyTorch with, say,

If you already have a compiled version with debug information (-g) then you do not need to include the header files again.

Just remove the line "preLaunchTask": "C/C++: g++.exe compilar archivo activo" from the configuration since your program is already compiled.

Your GTX770 GPU is a "Kepler" architecture compute capability 3.0 device. These devices were deprecated during the CUDA 10 release cycle and support for them dropped from CUDA 11.0 onwards

The CUDA 10.2 release is the last toolkit with support for compute 3.0 devices. You will not be able to make CUDA 11.0 or newer work with your GPU. The query and bandwidth tests use APIs which don't attempt to run code on your GPU, that is why they work where any other example will not work.

Up front: StéphaneLaurent's answer is the first thing you need to fix. Below is not causing that error, though I still recommend the changes for other reasons.

In your rows and cols functions, you are accessing input$ directly. This is wrong for at least two reasons:

1. (general functional programming) Your functions are breaching scope, reaching out to things they were not explicitly passed. This can be a bit about programming style, but functions that use variables not explicitly passed to it can be difficult to troubleshoot.

2. input$ can only be accessed from within a reactive*, observe*, or render* block (i.e., something that is shiny-reactive). Nothing outside any of those should try to do anything with input$ or output$.

As a fix, make the functions agnostic to shiny by making them self-contained and just working scalars/vectors. (I'll also reduce the logic a little.)

Maybe you could change your code to this:

Because x only ranged from 0 to len(numbers) - 1, counter cannot be equal to len(numbers) because even if everytime in the for x in range(len(numbers) - 1) loop counter increased by 1, the maximum it can get is still len(numbers) - 1. So change your if statement to

You're using C++ style comments in your code, i.e. //.

Many C compilers will allow these types of comments as an extension, however strict ANSI C does not. You'll need to use C style comments /* ... */.

So instead of this: