I am installing CUDA on Ubuntu 14.04 and have a Maxwell card (GTX 9** series) and I think I have installed everything properly with the toolkit as I can compile my samples. However, I read that in places that I should install the SDK (This appears to be talked about with the sdk 4). I am not sure if the toolkit and sdk are different? As I have a later 9 series card does that mean I have CUDA 6 running? Here is my nvcc version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2014 NVIDIA Corporation
Built on Wed_Aug_27_10:36:36_CDT_2014
Cuda compilation tools, release 6.5, V6.5.16
I am following a book and I need to include <cutil.h> and I can't find that file in the includes anywhere where I installed it.
I followed this guide provided by nvidia and as I have done what they say this is why I am confused http://developer.download.nvidia.com/compute/cuda/6_5/rel/docs/CUDA_Getting_Started_Linux.pdf
Thanks for help
CUDA Toolkit is a software package that has different components. The main pieces are:
CUDA SDK (The compiler, NVCC, libraries for developing CUDA software, and CUDA samples)
GUI Tools (such as Eclipse Nsight for Linux/OS X or Visual Studio Nsight for Windows)
Nvidia Driver (system driver for driving the card)
It has also many other components such as CUDA-debugger, profiler, memory checker, etc.
The fact that you are able to compile and run samples means that you probably installed the Toolkit fully and have the SDK, the driver, and the Samples at least.
As for the cutil.h, doing a search in my CUDA 6.5 installation with find -L . -iname "cutil.h" yielded no results. Also looking at other related questions on SO, it seems like this header file does not exist in CUDA installations anymore (since CUDA 5.0). However, looking at the samples, you can find some newer utility headers such as helper_cuda.h being in use. Helpers like these should be located in somewhere like /usr/local/cuda/samples/common/inc in your OS. helper_cuda.h is a header I almost always include in my CUDA programs since I find utility functions such as checkCudaErrors() very useful.
If you are following a book, my recommendation is; try to compile the code, and whenever you get an error saying a utility function is missing, do a grep search in the header files included in samples/common/inc. You will most probably find the missing utility functions there and then you can include the necessary headers accordingly.
Related
I used below command to build binary for nvidia GPU:
clang++ -fsycl -fsycl-targets=nvptx64-nvidia-cuda simple-sycl-app.cpp -o simple-sycl-app-cuda
But got below error message:
clang++: error: cannot find 'libspirv-nvptx64--nvidiacl.bc'; provide path to libspirv library via '-fsycl-libspirv-path', or pass '-fno-sycl-libspirv' to build without linking with libspirv
I searched in both intel oneAPI installation path and cuda toolkit path, but cannot find the spirv-nvptx64-nvidiacl.bc.
Anyone knows where to find libspirv-nvptx64—nvidiacl.bc?
It looks like you are trying to compile using the DPC++ compiler for Nvidia GPUs.
This option is not included in the oneAPI release installations from the Intel website. At the moment you will need to compile the DPC++ LLVM project with this enabled to be able to use the appropriate flag to target Nvidia devices.
You can follow the instructions on this page to compile the project and then it explains how to use the ptx target. In the future Codeplay, the company I work for, intends to publish release binaries that include the ptx compiler option.
I'm compiling the source code by using pgf95 (Fortran compiler).
If I use cuda 10.0, it successfully compiles the source code.
However, If I use cuda 10.1, it fails showing that 'cannot find libcublasLt.so'.
When I scan the directory cuda-10.0/lib64, cuda-10.1/lib64, both do not have the file starting with 'libcublasLt'.
How can I solve this issue?
libcublasLt.so is the library that provides the implementation for the cublasLt API which is defined here. It just happens to be a separate shared object from libcublas.so
In the past (e.g. CUDA 10.0 and prior), most CUDA libraries were installed in /usr/local/cuda/lib64 (or similar) by default (on linux). At about the CUDA 10.1 timeframe, it was decided that some libraries would be installed in different places. CUDA 10.1 is also where the cublasLt API and library were introduced. This affected some cublas libraries and is discussed in the CUDA 10.1 release notes here (both the introduction of the cublasLt library, as well as the change in library locations).
So there are 2 possibilities here (for CUDA 10.1, CUDA 10.2):
libcublasLt.so is on your machine, but it is simply not where you were expecting to find it.
libcublasLt.so is not on your machine. This means you are working with CUDA version prior to the introduction of the cublasLt API (i.e. 10.0 or prior), or you have a broken install.
So, assuming you are working with CUDA 10.1 or CUDA 10.2, the first step is to locate/determine whether libcublasLt.so is on your machine or not. You can use a linux utility like find or locate to accomplish that. They should have man pages available for you.
If you can find it, then you need to provide the path to it, via a linker spec (e.g. -L/path/to/libcublasLt.so/
If you can't find it, then either you are working with an older version of CUDA (10.0 or prior), or you need to reinstall CUDA.
I believe by the time you get to CUDA 11.0, the CUDA packages put the cublas libraries back in /usr/local/cuda/lib64 with the other libraries. YMMV.
I just upgraded from CUDA 4.2 to CUDA 5.0. Not surprisingly, the library that used to be named libcudart.so.4 is now called libcudart.so.5.0. After recompiling my code with nvcc 5.0, and attempting to running the code, I got this message:
./main: error while loading shared libraries: libcudart.so.4: cannot open shared object file: No such file or directory
Yeah, you stupid system, I know there's no libcudart.so.4. That's because it's now called libcudart.so.5.0. Why is it looking for libcudart.so.4 instead of libcudart.so.5.0, and how can I fix it?
What I've tried so far:
I've checked that all my paths are in order. These environment variables are set:
export PATH=$PATH:/usr/local/cuda/bin:/usr/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib:/usr/local/cuda/lib64:/lib
#note: /usr/local/cuda is symlinked to /usr/local/cuda-5.0
I've verified that libcudart.so.5.0 can be found in one of the LD_LIBRARY_PATH directories.
I recompiled my CUDA application with the the CUDA 5.0 version of nvcc. I successfully compiled and ran my application on an other machine with CUDA 4.2, and on an other machine with CUDA 4.0.
I confirmed that nvcc is really on version 5.0:
user#host$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2012 NVIDIA Corporation
Built on Fri_Sep_21_17:28:58_PDT_2012
Cuda compilation tools, release 5.0, V0.2.122
I'd like to get this question off the unanswered list, and I don't think #Jared Hoberock will mind, so I'm going to post his comment as an answer. If there's a concern and Jared or solvingPuzzles posts an answer, I'll delete mine (assuming it's not accepted -- I can't delete an accepted answere AFAIK).
nvcc seems to be statically linking against libcudart.a version 4.
Somewhere in your lib path, it seems that nvcc is finding an old libcudart.a, which needs to be removed.
For other readers, it's probably just sufficient to find all instances of libcudart.* on the system and delete any that don't match your desired CUDA version (assuming you're not trying to run a machine with multiple CUDA versions available -- in that case, the library paths for both compiling and running have to be managed appropriately)
See the image below of an Nvidia Nsight 2.2 profiling session (Win7, MSVC++ 10 Pro, CUDA 4.2, GTX 670).
On the first host thread (26.8%) I get the function call names from the CUDA API. Is it possible to get the function call names from the user defined functions being executed by the second thread (13.6%) in the host process? If so, how?
Paul, this is not supported by default.
Using the NVIDIA Tools Extension (NVTX) library you can manually instrument your code. This library is installed in the directory C:\Program Files\NVIDIA GPU Computing Toolkit\nvToolsExt with Nsight Visual Studio Edition (all versions) or CUDA Toolkit 5.0 RC. NVTX is supported by Visual Profiler in 5.0 RC.
The library comes with two samples to show you how to use the library. The NvtxMultithread.cpp sample provides a helper library. The functions of interest are
nvtxRangePushA
nvtxRangePop
or if you are using C++ the helper library has the scoped helper that can be used as
nvtx::ScopedRange range(__FUNCTION__)
at the top of each function.
It is possible to automate this by using the cl.exe options /Gh and /GH but this requires writing assembly.
This presentation might help. Parallel Nsight: Analyzing and Optimizing Massively Parallel Applications.
I'm trying to do some CUDA development on a PC without CUDA-capable GPU via emulation mode. The OS is Linux Mint Debian (can be considered Debian testing for all practical purposes) 32bit (2.6.32-5-686 kernel). Here's what I did so far:
Grabbed the CUDA Toolkit 32 bit and SDK for Ubuntu from http://developer.nvidia.com/cuda-toolkit-40
Installed the CUDA Toolkit in /usr/local/cuda/lib
Added the paths to bashrc
echo "# CUDA stuff
PATH=\$PATH:/usr/local/cuda/bin
LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/cuda/lib
export PATH
export LD_LIBRARY_PATH" >> ~/.bashrc
Added the path to /etc/ld.so.conf.d/cuda.conf:
/usr/local/cuda/lib
Executed "sudo ldconfig"
Restarted the session
Then installed the SDK in /home/user/NVIDIA_GPU_Computing_SDK folder
When I got to NVIDIA_GPU_Computing_SDK/C and type "make emu=1" to compile the examples I get:
nvcc warning : option 'device-emulation' has been deprecated and is ignored
/usr/bin/ld: cannot find -lcudartemu
/usr/bin/ld: cannot find -lcudartemu
collect2: ld returned 1 exit status
Seems like a library missing (rt = runtime ?). There is libcudart3 in the package manager, but wants a whole bunch of nvidia stuff as a dependency, including drivers and I don't even have an NVIDIA card on this machine. Also apparently the GPU emulation is now deprecated... Does anybody have some experience with CUDA emulation?
There is no emulation in CUDA any more. It was deprecated and removed during the 3.x release cycle. There is no emulation support beyond CUDA 3.1 IIRC. Certainly there is nothing you can do in CUDA 4.0.
On Linux, your best bet is to try gpuocelot, which provides a PTX level emulation on x86 processors and a reimplementation of the CUDA APIs.
Although I agree with the suggestion to try Ocelot, when I was in the same boat I found it easiest to go on eBay and get a cheap CUDA capable card to use for testing (I think I paid < $40). If you have the ability to open the hardware (I realize this isn't an option for some people) and to install drivers, that's what I'd suggest.