is it possible to check if any CUDA devices are present before all cudaMalloc... commands are called?
im using C++ and i just want to print an error message before the program launches incase the user doesn't support cuda tech.
EDIT: if i can check it from C#, it will be even better.
thanks!
You can use cudaGetDeviceCount to get the number of cuda devices and use cuda device properties to retrieve your necessary compute capabilities.
A rather old version of the API documentation for cudaGetDeviceCount can be found here.
Related
I am planning to call a typical matrix multiply CUDA C kernel from a fortran program. I am referring the following link http://www-irma.u-strasbg.fr/irmawiki/index.php/Call_CUDA_from_Fortran . I would be glad if any resources is available on this. I intend to avoid PGI Cuda Fortran as I am not possessing the compiler. In the link above I cannot make out what should be the CUDA.F90 file. I assume the last code given in the link is that of main.F90. Kindly help.
Perhaps you need to re-read the very first line of that page you linked to. Those instructions are relying on a set of external ISO C bindings for the CUDA API. That is where the CUDA.F90 file you are asking about comes from. You will need to download and build the FortCUDA bindings to use the instructions on that wiki page.
Edited to add that given your last question was about compilation in Nsight Visual Studio Edition, it would seem that you are running on a Windows platform. You should know that you can't use gcc to build CUDA applications on Windows platforms. The supplied CUDA libraries will only work with either the Microsoft toolchain or (possibly) Intel's compilers in certain cases.
I'm new at CUDA and OpenCL.
I have translated the kernels of a program from CUDA kernels to OpenCL kernels. I'm using the same seeds for the random number generation in both versions.
While the OpenCL version gets the exact same results every run, the CUDA version gives a slight different results every run.
I'm compiling the CUDA version without -use_fast_math.
My device is 1.1 capability.
Any idea about what could be the reason?
Thanks in advance
Devices of compute capability 1.1 do not support double operations. So if you are using double they are getting demoted to float. That could possibly affect your results, although a compute capability 1.1 device cannot support double in OpenCL either, AFAIK.
My question actually is is there any CUDA compiling options that may affect the accuracy of the CUDA results.
Yes, there are a variety of options that affect CUDA's usage of floating point math
I don't know why any of this would lead to variation from one run to the next, however. It's likely that you have a bug in the code.
I found the problem. In the original code, some values were updated asynchronously and was not completely updated yet. Thanks everybody for help. And sorry for the troubles.
I am designing a multi-gpu cuda code but I still don't have the machinary to actually develop the code. So, until I do,
Do you know if there is someway to emulate a multiple gpu enviroment just by using one gpu?
I suppose that such a thing, if it exists, would be very limited but it would allow me to test my ideas until I get the hardware I want.
Thanks!
Something close can be approximated using the CUDA Driver API (cuCtxCreate, cuCtxSetCurrent). See CUDA C Programming Guide Appendix G.4 Interoperability between Runtime and Driver API. Before calling any cuda* functions use cuCtxCreate to create two contexts on the device. Use cuCtxSetCurrent in place of cudaSetDevice.
While building my CUDA project I get the following error:
cutil_inline_runtime.h(328): error: identifier "CURAND_STATUS_DOUBLE_PRECISION_REQUIRED" is undefined
So I started googling. Since I couldn't find the solution (nor did I find the actual problem) I downloaded from nVIDIA CURAND guide pdf and started reading. In that pdf it says:
Enumerator:
...
**CURAND_STATUS_DOUBLE_PRECISION_REQUIRED** GPU does not have double precision required by MRG32k3a
...
This means I can't perform operations with double data type... right? Well that seems wrong because, I assure you, couple a days ago I made a project using double data type and it worked just fine. Does anyone have a suggestion? Thank you!
EDIT Since I was unable to find the "real" solution for the problem, eventually I commented out the lines with "CURAND_STATUS_DOUBLE_PRECISION_REQUIRED" in cutil_inline_runtime.h and now it works. I know it is "ugly" but it was the only thing that helped...
I also had this problem. The issue was that I was using different versions of the cuda SDK and the cuda toolkit. I was using a newer version of the SDK which referenced the definition of CURAND_STATUS_DOUBLE_PRECISION_REQUIRED, but that definition was undefined in curand.h in the older version of the cuda toolkit that I had installed.
Try installing the version of the toolkit that matches your cuda SDK version.
Try Googling "Compute Capability", it's how nvidia defines the various CUDA capabilities. In the CUDA C Programming guide, it's mentioned a couple of times that devices with compute capability 1.2 or lower do not support double precision FP math: CUDA C Programming, pages 140, and also check table F-1, namely the row about dpfp support.
Gefore 8800 GT is a G80 architecture chip, which is compute capability 1.0. You cannot do double computations on this card. Keep in mind much example code makes use of emulation modes, and the like, which can fool you into thinking it works.
I am new to multiple GPUs. I have written a code for a single GPU and want to further speed up by use of multiple GPUs. I am working with two GTX 470 with MS VS 2008 and cuda toolkit 4.0
I am facing two problems.
First problem is my code somehow doesn't run fine with 4.0 build rules and works fine for 3.2 build rules. Also the SDK example of multiGPU doesn't build on VS2008 giving error
error C3861: 'cudaDeviceReset': identifier not found
My second problem is, if I have to work with 3.2 then according to the documentation, threads have to be launched separately and separate allocations to be made etc. What is the easiest library for launching threads for multiple gpus and can you please give some example for my setup for access to multiple GPUs.
The answer to the first question is that you are clearly linking an older version of the CUDA runtime library. cudaDeviceReset is a new addition to the API introduced in CUDA 4.0. So double check the build rules and make sure you really are pointing the linker at the CUDA 4.0 toolkit and not an earlier version
The second part of your question sounds like a "hai plz give me teh code" question, and that isn't really what this place is for. I will, however, give you a link to GPUWorker (code currently available here), which is a boost threads based multigpu framework that was originally part of the HOOMD molecular dynamics package. It should give you some hints on how to do a multithreaded, multigpu code, even if GPUWorker turns out to not be directly applicable to your needs.