path: root/README

GPGPU-Sim Simulator Version 3.0.1 build $Change$

This version of GPGPU-Sim works with CUDA version 3.1 and some earlier
versions.  It does not (yet) work with CUDA version 4.x. 

Please see the copyright notice in the file COPYRIGHT distributed with this
release in the same directory as this file.  

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), but note
that use of this simulator does not imply any level of support.  Questions
answered on a best effort basis.

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

1. CONTRIBUTIONS and HISTORY

GPGPU-Sim was created by Tor Aamodt's research group at the University of
British Columbia.  Many students have contributed including: Wilson W.L. Fung,
Ali Bakhoda, George Yuan, Ivan Sham, Henry Wong, Henry Tran, Andrew Turner,
Aaron Ariel, Inderpret Singh, Tim Rogers, Jimmy Kwa, 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.  We subsequently added support for
OpenCL and removed all SimpleScalar code.

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

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 Ubuntu 10.04.3 LTS (32 bits).

Step 1: Dependencies
====================

Download and install the CUDA Toolkit.  To use ptxplus (native hardware
instructions) you must currently use the CUDA Toolkit version 2.3. Otherwise
use CUDA 3.1.

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 NVIDIA install
scripts. Note that you will need to use the lib64 directory if you are using a
64bit machine.

Ensure you have gcc, g++, make, makedepend, zlib, bison and flex installed on
your system.  For CUDA 3.1 we used gcc/g++ version 4.5.1, 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 2.5.33.  

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"

gpgpu-sim documentation:
"sudo apt-get install doxygen graphviz"

cuda sdk dependencies:
"sudo apt-get install libxi-dev libxmu-dev libglut3-dev"


Step 2: Build
=============

Read the file setup_environment and modify CUDA_INSTALL_PATH to match the
location of the CUDA toolkit compute SDK 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 

	source setup_environment debug

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.

Step 3: Run
============

Copy gpgpusim.conf and icnt_config_quadro_islip.txt from
gpgpu-sim/v3.x/configs/QuadroFX5800/ to your application's working directory.
These files configure the microarchitecture model of GPGPU-Sim to resemble a
Quadro FX 5800 (GT 200).

To use ptxplus (native ISA) uncomment the following two lines in
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.

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...).

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.