Modified the README according to the information that was in the wiki. Now everything related to building and setting up the simulator should be in the README and the wiki should only refer to that

[git-p4: depot-paths = "//depot/gpgpu_sim_research/fermi/distribution/": change = 11467]

1 files changed, 77 insertions, 37 deletions

diff --git a/README b/README
index 40a4898..6f33fe3 100644
--- a/README
+++ b/README
@@ -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.