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| -rw-r--r-- | README | 114 |
1 files changed, 77 insertions, 37 deletions
@@ -50,8 +50,10 @@ See file CHANGES for updates in this and earlier versions. 2. INSTALLING, BUILDING and RUNNING GPGPU-Sim -GPGPU-Sim was developed on Linux SuSe. This release was tested with SuSe -version 11.1 and Ubuntu 10.04.3 LTS (32 bits). +GPGPU-Sim was developed on SUSE Linux(this release was tested with SUSE +version 11.3) and has been used on several other Linux platforms (both 32-bit +and 64-bit systems). In principle, GPGPU-Sim should work with any linux +distribution as long as the following software dependencies are satisfied. Step 1: Dependencies ==================== @@ -72,8 +74,23 @@ have tested OpenCL on GPGPU-Sim using NVIDIA driver version 256.40 Note the most recent version of the NVIDIA driver produces PTX that is incompatible with this version of GPGPU-Sim. -Ensure you have gcc, g++, make, makedepend, zlib, bison and flex installed on -your system. For CUDA 3.1 we used gcc/g++ version 4.3.2 (if using the CUDA 3.1 +GPGPU-Sim dependencies: +* gcc +* g++ +* make +* makedepend +* xutils +* bison +* flex +* zlib +* libboost +* cuda toolkit + +GPGPU-Sim documentation dependencies: +* doxygen +* graphviz + +For CUDA 3.1 we used gcc/g++ version 4.3.2 (if using the CUDA 3.1 SDK) or 4.5.1 (if not using the CUDA SDK), for CUDA 2.3 we used gcc/g++ version 4.3.2, for CUDA 1.1 we used gcc/g++ version 4.1.3. This version of GPGPU-Sim does not yet work with CUDA 4.x; We used bison version 2.3, and flex version @@ -83,7 +100,8 @@ If you are using Ubuntu, the following commands will install all required dependencies besides the CUDA Toolkit. gpgpu-sim dependencies: -"sudo apt-get install build-essentials xutils-dev bison zlib1g-dev flex libboost-all-dev libglu1-mesa-dev" +"sudo apt-get install build-essentials xutils-dev bison zlib1g-dev flex +libboost-all-dev libglu1-mesa-dev" gpgpu-sim documentation: "sudo apt-get install doxygen graphviz" @@ -95,24 +113,46 @@ cuda sdk dependencies: Step 2: Build ============= -Read the file setup_environment and modify CUDA_INSTALL_PATH to match the -location of the CUDA toolkit on your system. Then, from a bash shell, type the -following in this directory: - - source setup_environment - -Type "make" in this directory. This will build the simulator with optimizations -enabled so the simulator runs faster. If you want to run the simulator in gdb -to debug it, then run +To build the simulator, you first need to configure how you want it to be +built. From the root directory if the simulator, do the following: + +cd distribution + +then open the file 'setup_environment' with your favorite text editor. Read +the file carefully and modify the environment variables in that file to your +environment specific paths. In particular, you need to set CUDA_INSTALL_PATH +correctly. If you set CUDA_INSTALL_PATH in your .bashrc file as per the +instructions in the cuda toolkit installation, setup_environment will detect +that automatically, in which case, you don't need to change it in +setup_environment. The setup_environment script is engineered to work with a +default system setup, so in the general case you will not need to modify it, +however, you should still read it carefully to figure out of something specific +to your system needs to be changed. After you have edited that file, save it +and run + +source setup_environment <build_type> + +replace <build_type> with debug or release. Use release if you need faster +simulation and debug if you need to run the simulator in gdb. - source setup_environment debug +Now you are ready to build the simulator, just run + +make + +After make is done, the simulator would be ready to use. To clean the build, +run + +make clean + +To build the doxygen generated documentations, run + +make docs + +to clean the docs run + +make cleandocs -then "make" again. - -[Optional]: Type "make docs" in this directory to build the doxygen -documentation. You need to have doxygen and graphviz installed for this to -work. "make cleandocs" will remove the generated documentation. The -documentation resides at doc/doxygen/html. +The documentation resides at doc/doxygen/html. Step 3: Run ============ @@ -128,23 +168,23 @@ gpgpusim.config (again, note this requires CUDA toolkit 2.3): #-gpgpu_ptx_convert_to_ptxplus 1 #-gpgpu_ptx_save_converted_ptxplus 1 -Now run your unmodified CUDA or OpenCL application. It will automatically -execute kernels on GPGPU-Sim. - -If you have not done so you need to build a CUDA appliction (or an OpenCL -application). Note that you no longer need to recompile your application to run -on GPGPU-Sim. GPU kernels will automatically run on the simulator instead of -your graphics card since the setup_environment script modifies your -LD_LIBRARY_PATH to point to $GPGPUSIM_ROOT/lib. To be able to run the -application on your graphics card again, remove $GPGPUSIM_ROOT/lib from -LD_LIBRARY_PATH. +Now To run a CUDA application on the simulator, simply execute + +source setup_environment <built_type>. + +and just launch the executable as you would if it was to run on the hardware. +To revert back to running on the hardware, remove GPGPU-Sim from your +LD_LIBRARY_PATH environment variable. -Note that for OpenCL applications the NVIDIA driver is required to convert -OpenCL ".cl" files to PTX (this in turn may require you have a graphics card, -but to run CUDA applications on the simulator a graphics card is not -necessary). The resulting PTX can be saved to disk by adding --save_embedded_ptx to your gpgpusim.config file (embedded PTX files with be -saved as _0.ptx, _1.ptx, etc...). +Running OpenCL applications is identical to running CUDA applications. However, +OpenCL applications need to communicate with the NVIDIA driver in order to +build OpenCL at runtime. GPGPU-Sim supports offloading this compilation to a +remote machine. The hostname of this machine can be specified using the +environment variable OPENCL_REMOTE_GPU_HOST. This variable should also be set +through the setup_environment script. If you are offloading to a remote machine, +you might want to setup passwordless ssh login to that machine in order to +avoid having too retype your password for every execution of an OpenCL +application. If you need to run the set of applications in the NVIDIA CUDA SDK code samples then you will need to download, install and build the SDK. |