GPGPU-Sim simulator modification to support fast distributed mutual exclusion
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README

GPGPU-Sim Simulator version 2.1.1b (beta)

See doc/GPGPU-Sim_Manual.html for more documentation.

Please see the copyright notice in the file COPYRIGHT distributed with this
release in the same directory as this file.  This version of GPGPU-Sim is
for non-commercial use only.

If you use this simulator in your research please cite:

Ali Bakhoda, George Yuan, Wilson W. L. Fung, Henry Wong, Tor M. Aamodt,
Analyzing CUDA Workloads Using a Detailed GPU Simulator, in IEEE International
Symposium on Performance Analysis of Systems and Software (ISPASS), Boston, MA,
April 19-21, 2009.

Please sign up for the google groups page for Q&A (see gpgpu-sim.org).

See Section 2 "INSTALLING, BUILDING and RUNNING GPGPU-Sim" below to get started.

1. CONTRIBUTIONS and HISTORY

GPGPU-Sim was created at the University of British Columbia by Tor M. Aamodt,
Wilson W.  L. Fung, Ali Bakhoda, George Yuan along with contributions by Ivan
Sham, Henry Wong, Henry Tran, and others.  

GPGPU-Sim models the features of a modern graphics processor that are relevant
to non-graphics applications.  The first version of GPGPU-Sim was used in a
MICRO'07 paper and follow-on ACM TACO paper on dynamic warp formation. That
version of GPGPU-Sim used the SimpleScalar PISA instruction set for functional
simulation, and various configuration files to provide a programming model
close to CUDA.  Creating benchmarks for the original GPGPU-Sim simulator was a
very time consuming process.  This motivated the development an interface for
directly running CUDA applications to leverage the growing number of
applications being developed to use CUDA.

The interconnection network is simulated using the booksim simulator developed
by Bill Dally's research group at Stanford.

The current version of GPGPU-Sim still uses a few portions of SimpleScalar
functional simulation code: support for memory spaces and command line option
processing (but not for any timing model purposes).  SimpleScalar code has very
strict restrictions on non-academic use (these portions may be removed in a
future version of GPGPU-Sim).

To produce output that is compatible with the output from running the same CUDA
program on the GPU, we have implemented several PTX instructions using the CUDA
Math library (part of the CUDA toolkit). Code to interface with the CUDA Math
library is contained in cuda-math.h, which also includes several structures
derived from vector_types.h (one of the CUDA header files).

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 has been used on several other Linux platforms.  

Step 1: Ensure you have gcc, make, zlib, bison and flex installed on your
system.  For CUDA 2.x we used gcc version 4.3.2, for CUDA 1.1 we used gcc 
version 4.1.3.  We used bison version 2.3, and flex version 2.5.33.  

Step 2: Download and install the CUDA Toolkit and CUDA SDK code samples from
NVIDIA's website: <http://www.nvidia.com/cuda>.  If you want to run OpenCL on
the simulator, download and install NVIDIA's OpenCL driver from
<http://developer.nvidia.com/object/opencl-download.html>. Update your PATH and
LD_LIBRARY_PATH as indicated by the install scripts.

Step 3: Build libcutil.a. The install script for the CUDA SDK does not do this
step automatically. If you installed the CUDA Toolkit in a nonstandard location
you will first need to set CUDA_INSTALL_PATH to the location you installed the
CUDA toolkit (including the trailing "/cuda").  Then, change to the C/common
subdirectory of your CUDA SDK installation (or common subdirectory on older
CUDA SDK versions) and type "make".

Step 4: Set environment variables (e.g., your .bashrc file if you use bash as
your shell). 
 
    (a) Set GPGPUSIM_ROOT to point to the directory containing this README file. 
    (b) Set CUDAHOME to point to your CUDA installation directory
    (c) Set NVIDIA_CUDA_SDK_LOCATION to point to the location of the CUDA SDK
    (d) Add $CUDAHOME/bin and $GPGPUSIM_ROOT/bin to your PATH 
    (e) Add $GPGPUSIM_ROOT/lib/ to your LD_LIBRARY_PATH and remove $CUDAHOME/lib
        or $CUDAHOME/lib64 from LD_LIBRARY_PATH
    (f) If using OpenCL, set NVOPENCL_LIBDIR to the installation directory of 
        libOpenCL.so distributed with the NVIDIA OpenCL driver.
        On SuSe 11.1 64-bit NVIDIA's libOpenCL.so is installed in /usr/lib64/.  

Step 5: 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 build it using "make DEBUG=1" instead.

Step 6: Run a CUDA built with a recent version of CUDA (or an OpenCL
application) and the device code should now run on the simulator instead of
your graphics card.  To be able to run the application on your graphics card
again, remove $GPGPUSIM_ROOT/lib from your LD_LIBRARY_PATH.  There is also a
"static" build setup used for some of the examples in the benchmarks directory
(more information on this is available in doc/GPGPU-Sim_Manual.html)

By default, this version of GPGPU-Sim uses the ptx source embedded within the
binary. To use the .ptx files in the current directory, type:

   export PTX_SIM_USE_PTX_FILE=1

Note that for OpenCL applications the NVIDIA driver is required to convert
OpenCL ".cl" files to PTX.  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...).

3. USING THE SIMULATOR

For guidelines on using and configuring the simulator, please see
doc/GPGPU-Sim_Manual.html