STD Classes in CUDA Kernel

Question

I know that there is no way using std classes such as string, vector, map or set in CUDA kernel. However, it's very uncomfortable without them. I have to write a lot of code in CUDA kernel, so I would like to use at least strings and vectors. I'm not talking about something like thrust. I want to be able to write something like this:

__global__ void kernel()
{
    cuda_vector<int> a;
    for(int i=0;i<10;i++)
        a.push_back(i);
}

int main()
{
    kernel<<<1,512>>>();
    return 0;
}

This should create 512 threads and in each thread I want to create cuda_vector class and use it as std::vector. I didn't find any solution on the internet and I started to write my own class. Each function of this class is defined as "__ host __ " and " __ device __" function so that I can use it on both CPU and GPU. Theoretically, it can be implemented, however only on Fermi architecture. Because, we need to allocate memory dynamically. I have GTX 580 and started to write my own Vector. But it's tiring and needs a lot of time. Isn't there any implementation which I can use? I can't believe that there isn't any. Do so many software developers write on CUDA without it? And noone tried to write his/her own version?

Just know that something like that will not be good at all for performance. Read the Cuda C programming guide and the best practices guide and you will understand why. — jmsu
I understand that. However, I need strings and vectors rarely. I mean, in the computationally intense parts I don't have to use them. So, it won't affect performance too much. — Tsotne Tabidze

jkysam jkysam · Accepted Answer · 2011-11-16T15:17:05

The reason you don't find something like std::vector for cuda is performance. Your traditional vector object doesn't fit well with the CUDA model. If you are planning on using only 512 threads and each one will be managing a std::vector like object your performance is going to be worse than running the same code on the CPU.

GPU threads are not like CPU threads, they should be as light as possible. Use thread blocks and shared memory to have the threads cooperate. If you are manipulating a string, each thread should be working on one character, if you are using vectors in the CPU pass an array of that to the GPU, and have each thread work on one element. Basically, think about how to solve the problem with the CUDA programming model as apposed to solving it with a CPU approach and then translating it to CUDA.

STD Classes in CUDA Kernel

2 Answers