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1. #CUDA EMULATOR INSTALL#
2. #CUDA EMULATOR UPDATE#
3. #CUDA EMULATOR ANDROID#
4. #CUDA EMULATOR CODE#
5. #CUDA EMULATOR SERIES#

usr/local/cuda/include/surface_functions.h:100: error: there are no arguments to ‘_surf1Dreadc1’ that depend on a template parameter, so a declaration of ‘_surf1Dreadc1’ must be available usr/local/cuda/include/surface_functions.h: In function ‘void surf1Dread(T*, surface, int, int, cudaSurfaceBoundaryMode)’: Hi, I am kind of a beginner with all of this so I followed your steps pretty much exactly, but the line “nvcc -c hello.cu -arch=sm_20” threw me a bunch of errors which are totally cryptic to me. The final output shows the result of the computation performed using GPU and CPU respectively.

#CUDA EMULATOR CODE#

This code computes the dot product of two vectors a and b using the GPU (in our case on the CPU using emulation) and stores the result in c. G++ -o hello.out hello.o `OcelotConfig -l` Now compile the code nvcc -c hello.cu -arch=sm_20 You may go through this page to understand the configuration file.Ģ. Place both these files in the hellocuda directory. Download the configuration file from here and the Hello World program from here. Navigate to your home directory and create a folder hellocuda.

#CUDA EMULATOR INSTALL#

Install by double clicking (it should open in the ubuntu software center, select install there).ĥ. I selected libboost-* in i386 (Release) and then downloaded the *.deb file below the Downloadable files section. The CUDA setup is completed now and you can check using the command nvcc -version

#CUDA EMULATOR UPDATE#

Update the sources using source ~/.bashrc Copy paste the lines below export PATH=$PATH:/usr/local/cuda/binĮxport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib:/usr/local/libĮxport CPLUS_INCLUDE_PATH=/usr/local/cuda/include In vi editor enter “G” to go to the end of file, then “i” to add data. Now you need to update the paths in your bashrc file vi ~/.bashrc Check the current version using gcc -versionģ. Now select the version 4.4 (usually item 0). install /usr/bin/gcc gcc /usr/bin/gcc-4.4 60 \ install /usr/bin/gcc gcc /usr/bin/gcc-4.6 40 \

Next install gcc and g++ v4.4 and update alternatives and priorities for gcc versions sudo apt-get install gcc-4.4 Hit enter if it asks for a change of destination directory. Change permissions for the installation file (mine was for 32bit) and install it. Now start terminal (using ctrl + alt + t) and navigate to your Downloads directory. Download the “CUDA Toolkit for Ubuntu Linux 10.10” from here (v 4.0). Here, I provide my version of the installation procedure to get a simple “Hello World” program up and running using GPGPU emulator.ġ. Finally, using few magical keywords in my Google search I located this blog entry which provided detailed installation procedure for the gpuocelot. The documentation online for installing emulators in systems which do not have a graphics card was insufficient. I spent a lot of time trying to install a GPU emulator but either I got stuck in some library dependency, or some of the downloaded packages were dependent on older gcc versions.

#CUDA EMULATOR SERIES#

Mine is an Intel Mobile 4 Series Chipset Integrated Graphics Controller, you can check yours using the command: lspci | grep VGA I wanted to use CUDA for one of my class projects but I do not have a compatible graphics card. You might want to try out the parallel computing platform CUDA to gauge what kind of speed-up you can achieve in you current code by running it on a GPU.

#CUDA EMULATOR ANDROID#