GPU Emulator for CUDA programming without the hardware [closed] - cuda

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Question: Is there an emulator for a Geforce card that would allow me to program and test CUDA without having the actual hardware?
Info:
I'm looking to speed up a few simulations of mine in CUDA, but my problem is that I'm not always around my desktop for doing this development. I would like to do some work on my netbook instead, but my netbook doesn't have a GPU. Now as far as I know, you need a CUDA capable GPU to run CUDA. Is there a way to get around this? It would seem like the only way is a GPU emulator (which obviously would be painfully slow, but would work). But whatever way there is to do this I would like to hear.
I'm programming on Ubuntu 10.04 LTS.

Answer 1

For those who are seeking the answer in 2016 (and even 2017) ...
Disclaimer
I've failed to emulate GPU after all.
It might be possible to use gpuocelot if you satisfy its list of
dependencies.
I've tried to get an emulator for BunsenLabs (Linux 3.16.0-4-686-pae #1 SMP
Debian 3.16.7-ckt20-1+deb8u4 (2016-02-29) i686 GNU/Linux).
I'll tell you what I've learnt.
nvcc used to have a -deviceemu option back in CUDA Toolkit 3.0
I downloaded CUDA Toolkit 3.0, installed it and tried to run a simple
program:
#include <stdio.h>
__global__ void helloWorld() {
printf("Hello world! I am %d (Warp %d) from %d.\n",
threadIdx.x, threadIdx.x / warpSize, blockIdx.x);
}
int main() {
int blocks, threads;
scanf("%d%d", &blocks, &threads);
helloWorld<<<blocks, threads>>>();
cudaDeviceSynchronize();
return 0;
}
Note that in CUDA Toolkit 3.0 nvcc was in the /usr/local/cuda/bin/.
It turned out that I had difficulties with compiling it:
NOTE: device emulation mode is deprecated in this release
and will be removed in a future release.
/usr/include/i386-linux-gnu/bits/byteswap.h(47): error: identifier "__builtin_bswap32" is undefined
/usr/include/i386-linux-gnu/bits/byteswap.h(111): error: identifier "__builtin_bswap64" is undefined
/home/user/Downloads/helloworld.cu(12): error: identifier "cudaDeviceSynchronize" is undefined
3 errors detected in the compilation of "/tmp/tmpxft_000011c2_00000000-4_helloworld.cpp1.ii".
I've found on the Internet that if I used gcc-4.2 or similarly ancient instead of gcc-4.9.2 the errors might disappear. I gave up.
gpuocelot
The answer by Stringer has a link to a very old gpuocelot project website. So at first I thought that the project was abandoned in 2012 or so. Actually, it was abandoned few years later.
Here are some up to date websites:
GitHub;
Project's website;
Installation guide.
I tried to install gpuocelot following the guide. I had several errors during installation though and I gave up again. gpuocelot is no longer supported and depends on a set of very specific versions of libraries and software.
You might try to follow this tutorial from July, 2015 but I don't guarantee it'll work. I've not tested it.
MCUDA
The MCUDA translation framework is a linux-based tool designed to
effectively compile the CUDA programming model to a CPU architecture.
It might be useful. Here is a link to the website.
CUDA Waste
It is an emulator to use on Windows 7 and 8. I've not tried it though. It doesn't seem to be developed anymore (the last commit is dated on Jul 4, 2013).
Here's the link to the project's website: https://code.google.com/archive/p/cuda-waste/
CU2CL
Last update: 12.03.2017
As dashesy pointed out in the comments, CU2CL seems to be an interesting project. It seems to be able to translate CUDA code to OpenCL code. So if your GPU is capable of running OpenCL code then the CU2CL project might be of your interest.
Links:
CU2CL homepage
CU2CL GitHub repository

Answer 2

This response may be too late, but it's worth noting anyway. GPU Ocelot (of which I am one of the core contributors) can be compiled without CUDA device drivers (libcuda.so) installed if you wish to use the Emulator or LLVM backends. I've demonstrated the emulator on systems without NVIDIA GPUs.
The emulator attempts to faithfully implement the PTX 1.4 and PTX 2.1 specifications which may include features older GPUs do not support. The LLVM translator strives for correct and efficient translation from PTX to x86 that will hopefully make CUDA an effective way of programming multicore CPUs as well as GPUs. -deviceemu has been a deprecated feature of CUDA for quite some time, but the LLVM translator has always been faster.
Additionally, several correctness checkers are built into the emulator to verify: aligned memory accesses, accesses to shared memory are properly synchronized, and global memory dereferencing accesses allocated regions of memory. We have also implemented a command-line interactive debugger inspired largely by gdb to single-step through CUDA kernels, set breakpoints and watchpoints, etc... These tools were specifically developed to expedite the debugging of CUDA programs; you may find them useful.
Sorry about the Linux-only aspect. We've started a Windows branch (as well as a Mac OS X port) but the engineering burden is already large enough to stress our research pursuits. If anyone has any time and interest, they may wish to help us provide support for Windows!
Hope this helps.
[1]: GPU Ocelot - https://code.google.com/archive/p/gpuocelot/
[2]: Ocelot Interactive Debugger - http://forums.nvidia.com/index.php?showtopic=174820

Answer 3

You can check also gpuocelot project which is a true emulator in the sense that PTX (bytecode in which CUDA code is converted to) will be emulated.
There's also an LLVM translator, it would be interesting to test if it's more fast than when using -deviceemu.

Answer 4

The CUDA toolkit had one built into it until the CUDA 3.0 release cycle. I you use one of these very old versions of CUDA, make sure to use -deviceemu when compiling with nvcc.

Answer 5

https://github.com/hughperkins/cuda-on-cl lets you run NVIDIA® CUDA™ programs on OpenCL 1.2 GPUs (full disclosure: I'm the author)

Answer 6

Be careful when you're programming using -deviceemu as there are operations that nvcc will accept while in emulation mode but not when actually running on a GPU. This is mostly found with device-host interaction.
And as you mentioned, prepare for some slow execution.

Answer 7

GPGPU-Sim is a GPU simulator that can run CUDA programs without using GPU.
I created a docker image with GPGPU-Sim installed for myself in case that is helpful.