I'm taking a course that teaches CUDA. I would like to use it my personal laptop, but I don't have Nvidia graphics processor. mine is ATI . so I was thinking is there any Virtual Hardware simulator that I can use? or that there is no other way than using a PC with CUDA Graphics processor.
Thank you very much
The CUDA toolkit used to ship with a host CPU emulation mode, but that was deprecated early in the 3.0 release cycle and has been fully removed from toolkits for the best part of two years.
Your only real option today is to use Ocelot. It has a PTX assembly translator and a pretty reliable reimplementation of the CUDA runtime for x86 CPUs, and there is also a rather experimental PTX to AMD IL translator (I have no experience with the latter). On a modern linux system with an up to date GNU toolchain, Ocelot is reasonably easy to get running. I am not sure if there is a functioning Windows port or not.
CUDA has its own emulation mode witch runs everything on CPU. Problem is that in such case you don't have real concurrency so programs that runs successfully in emulation mode can fail (and usually does) in normal mode. You can develop your code in emulation mode, but then you have to debug it on computer with CUDA card.
Related
I have a very simple Toshiba Laptop with i3 processor. Also, I do not have any expensive graphics card. In the display settings, I see Intel(HD) Graphics as display adapter. I am planning to learn some cuda programming. But, I am not sure, if I can do that on my laptop as it does not have any nvidia's cuda enabled GPU.
In fact, I doubt, if I even have a GPU o_o
So, I would appreciate if someone can tell me if I can do CUDA programming with the current configuration and if possible also let me know what does Intel(HD) Graphics mean?
At the present time, Intel graphics chips do not support CUDA. It is possible that, in the nearest future, these chips will support OpenCL (which is a standard that is very similar to CUDA), but this is not guaranteed and their current drivers do not support OpenCL either. (There is an Intel OpenCL SDK available, but, at the present time, it does not give you access to the GPU.)
Newest Intel processors (Sandy Bridge) have a GPU integrated into the CPU core. Your processor may be a previous-generation version, in which case "Intel(HD) graphics" is an independent chip.
Portland group have a commercial product called CUDA x86, it is hybrid compiler which creates CUDA C/ C++ code which can either run on GPU or use SIMD on CPU, this is done fully automated without any intervention for the developer. Hope this helps.
Link: http://www.pgroup.com/products/pgiworkstation.htm
If you're interested in learning a language which supports massive parallelism better go for OpenCL since you don't have an NVIDIA GPU. You can run OpenCL on Intel CPUs, but at best you can learn to program SIMDs.
Optimization on CPU and GPU are different. I really don't think you can use Intel card for GPGPU.
Intel HD Graphics is usually the on-CPU graphics chip in newer Core i3/i5/i7 processors.
As far as I know it doesn't support CUDA (which is a proprietary NVidia technology), but OpenCL is supported by NVidia, ATi and Intel.
in 2020 ZLUDA was created which provides CUDA API for Intel GPUs. It is not production ready yet though.
I know CUDA only got UVA (Unified Virtual Addressing) with version 4.0. But - is that only a software feature? Or does it require some kind of hardware support (on the GPU side I mean)?
Notes:
In this GTC 2011 presentation it says a Fermi-class GPU is necessary for P2P copies, but it doesn't say that's necessary for UVA itself.
Note: I know UVA is not a good idea on a 32-bit-CPU system, I don't mean that kind of hardware support.
The UVA which was introduced back in May 2011 with CUDA 4.0 requires for hardware support some Fermi-class GPUs. So, this implies compute capability 2.0 onwards.
But apparently, that's not enough since, according to slide #17 of this presentation of the new features of CUDA 4.0, it seems to be only supported in 64-bit (which makes sense since otherwise you would run out of address space pretty quick), and with TCC (Tesla Compute Cluster) when on Windows. I'm not sure if this later limitation still exists since I never ever developed on Windows.
I have problem running samples provided by Nvidia in their GPU Computing SDK (there's a library of compiled sample codes).
For cuda I get message "No CUDA-capable device is detected", for OpenCL there's error from function that should find OpenCL capable units.
I have installed all three parts from Nvidia to develop with OpenCL - devdriver for win7 64bit v.301.27, cuda toolkit 4.2.9 and gpu computing sdk 4.2.9.
I think this might have to do with Optimus technology that reroutes output from Nvidia GPU to Intel to render things (this notebook has also Intel 3000HD accelerator), but in Nvidia control pannel I set to use high performance Nvidia GPU, set power profile to prefer maximum performance and for PhysX I changed from automatic selection to Nvidia processor again. Nothing has changed though, those samples won't run (not even those targeted for GF8000 cards).
I would like to play somewhat with OpenCL and see what it is capable of but without ability to test things it's useless. I have found some info about this on forums, but it was mostly about linux users where you need Bumblebee to access Nvidia GPU. There's no such problem on Windows however, drivers are better and so you can access it without dark magic (or I thought so until I found this problem).
My laptop has a GeForce 540M as well, in an Optimus configuration since my Sandy Bridge CPU also has Intel's integrated graphics. To run CUDA codes, I have to:
Install NVIDIA Driver
Go to NVIDIA Control Panel
Click 3D Settings -> Manage 3D Settings -> Global Settings
In the Preferred Graphics processor drop down, select "High-performance NVIDIA processor"
Apply the settings
Note that the instructions above apply the settings for all applications, so you don't have to worry about CUDA errors any more. But it will drain more battery.
Here is a video recap as well. Good luck!
Ok this has proven to be totally crazy solution. I was thinking if something isn't hooking between the hardware and application and only thing that came to my mind was AV software. I'm using Comodo with sandbox and Defense+ on and after turning them off I could run all those samples. What's more, only Defense+ needs to be turned off.
Now I just think about how much apps could have been blocked from accessing that GPU..
That's most likely because of the architecture of Optimus. So I'd suggest you to read
NVIDIA CUDA Developer Guide for NVIDIA Optimus Platforms, especially the section "Querying for a CUDA Device" which addresses this issue, I believe.
I want to learn GPGPU and CUDA programming. But I know that only Nvidia card support it. My laptop has an Intel HD Graphic Card. So I need to search if it is possible to do GPGPU or something like that with Intel graphic card. Thanks for any information.
To develop in CUDA your options are:
Use an NVIDIA GPU - all NVIDIA server, desktop and laptop GPUs support CUDA since around 2006, since your laptop does not have one you could try using one remotely.
Use PGI CUDA x86, not free but does what you want.
Use gpuocelot to execute the PTX on the CPU, that's an open-source project in development so YMMV.
You cannot do GPGPU on Intel HD Graphics cards today, unless you do shader-based programming (which was common practice in the days before CUDA and OpenCL).
In my experience, the PGI X86 stuff seems to have fallen flat and I'm not aware of anyone using that. Ocelot is another attempt at the same, but it is very reasearchy and not fully robust at this point.
The only OpenCL compliant devices from Intel are the latest CPUs (Sandy Bridge and Ivy Bridge).
What CPU do you have in your system?
CUDA is Nvidia specific as starter. The GPU emulators are always there in CUDA, so you can use them without a graphics card easily, though it will be slow. A faster solution is the
the x86 implementation. Any of these will allow you to learn the basics of CUDA without using the GPU at all.
If you are want to learn GPGPU in general you still have the option to learn OpenCL, which more widely supported, including AMD, Intel, Nvidia etc... E.g. Intel has an OpenCL SDK (the target is the CPU then, but I guess is irrelevant for you).
After learning the basics of either CUDA or OpenCL, the other will be easy to learn. Neither the syntax nor the semantics are the same, but it is easy step forward as the concepts are the same.
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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.
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
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
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.
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.
https://github.com/hughperkins/cuda-on-cl lets you run NVIDIA® CUDA™ programs on OpenCL 1.2 GPUs (full disclosure: I'm the author)
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.
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.