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+.	SSH to one of the machines from ug51.eecg.toronto.edu to ug75.eecg.toronto.edu range.
+.	Define the necessary environment variables: "source /cad1/CUDA/cuda.csh"
+Add the above source command to your .cshrc file so it automatically takes effect at every login. The CUDA compilers and runtime need these variables defined to work properly.
+After this stage, you should see CUDA_HOME variable already defined when you run "setenv" command.
+.	Install the SDK: "sh /cad1/CUDA/InstallSDK.sh". Accept the default settings. The script executes and finally gives "* Installation Complete" message. You should see "NVIDIA_GPU_Computing_SDK" directory created in your home directory. It takes around 250 MB of your disk space.
+There should be several subdirectories under “NVIDIA_GPU_Computing_SDK”. The CUDA examples are under “NVIDIA_GPU_Computing_SDK/C”.
+.	Let’s first build a number of shared libraries.
+Change directory by executing "cd NVIDIA_GPU_Computing_SDK/shared”
+Compile: “make”
+This creates the “release” version of the libraries. Some macros default to nothing in this version. You can also build a version for debugging: “make dbg=1”.  If you want to use the emulator you can build the emulated version: “make emu=1” or “make dbg=1 emu=1”. The same options apply to all makefiles found under the CUDA SDK.
+Better compile the debug version as well: “make dbg=1”
+.	Change directory to "NVIDIA_GPU_Computing_SDK/C/common".
+Compile the libraries: “make” . Ignore the warnings.
+Compile the debug version as well: “make dbg=1”. Ignore the warnings.
+Read what the library provides: “less cutil_readme.txt”.
+.	Now let’s compile some of the examples which can be found under the “NVIDIA_GPU_Computing_SDK/C/src” directory each on its own subdirectory.
+Change directory to the bandwidth test example: “cd ../src/bandwidthTest”.
+Now you are under NVIDIA_GPU_Computing_SDK/C/src/bandwidthTest”.
+Compile the example: “make”
+The executable is installed in NVIDIA_GPU_Computing_SDK/C/bin/linux/release
+	Compile the debug version as well: “make dbg=1”
+	Run the release version. You should see something like this:
+[bandwidthTest] starting...
+bandwidthTest Starting...
+Running on...
+ Device 0: GeForce GTX 480
+ Quick Mode
+ Host to Device Bandwidth, 1 Device(s), Paged memory
+   Transfer Size (Bytes)        Bandwidth(MB/s)
+   33554432                     2228.2
+ Device to Host Bandwidth, 1 Device(s), Paged memory
+   Transfer Size (Bytes)        Bandwidth(MB/s)
+   33554432                     1564.1
+ Device to Device Bandwidth, 1 Device(s)
+   Transfer Size (Bytes)        Bandwidth(MB/s)
+   33554432                     119485.0
+[bandwidthTest] test results...
+PASSED
+Press ENTER to exit...
+-----------------------------------------------------------
+.	For creating your own new project, follow these steps:
+There is a “template” project that you can copy and modify:
+(a)	Copy the template project:
+cd  ~/NVIDIA_GPU_Computing_SDK/C/src/template
+mkdir ../myproject
+cp -r * ../myproject
+(b)	Edit the filenames of the project to suit your needs
+mv template.cu myproject.cu
+mv template_kernel.cu myproject_kernel.cu
+mv template_gold.cpp myproject_gold.cpp
+*** Alternatively, copy the files from the deviceQuery project. This example just uses a .cu file
+in which you can write C code as well.
+(c) Edit the Makefile and source files. Just search and replace all occurrences of "template" with
+"myproject". You’ll need to change the Makefile and file “myproject.cu” that includes the test kernel #include  <template_kernel.cu>.
+(d) Build the project
+make
+You can build a debug version with "make dbg=1", an emulation version with "make emu=1", and
+a debug emulation with "make dbg=1 emu=1". Similarly, you can build versions without debugging
+support using just “make”.
+(e) Run the program
+../../bin/linux32/release/myproject
+(It should print "Test PASSED")
+(f) Now modify the code to perform the computation you require.

CUDA Programming Summer 2013

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