The directories AES, BFS, CP, LPS, LIB, MUM, NN, NQU, RAY, STO, and WP contain
benchmarks used in the ISPASS 2009 paper on GPGPU-Sim.  Their original sources
are listed below.

The instructions below describe how to build and run the benchmarks assuming
you are using GPGPU-Sim v3.x.  This series of instructions is essentially the
procedure we follow when using GPGPU-Sim v3.x in our research at UBC.

Assuming all dependencies required by the benchmarks are satisifed, you can
build/run them by doing the following:
   
1. Install the NVIDIA CUDA Toolkit and then install/build the NVIDIA CUDA SDK
   benchmarks. Note the CUDA Toolkit needs to be installed before you can build 
   the CUDA SDK.  Building the CUDA SDK is required since some of the ISPASS 2009
   benchmarks use a library created when building the CUDA SDK.

2. Open Makefile.ispass-2009 in your favorite text editor and set the
   variables at the top of the file to suit your environment. 

3. Define the following environment variables:

	CUDA_INSTALL_PATH
	NVIDIA_COMPUTE_SDK_LOCATION

   The first, CUDA_INSTALL_PATH, should point to the directory you installed
   the NVIDIA CUDA Toolkit (e.g., /usr/local/cuda).

   The second, NVIDIA_COMPUTE_SDK_LOCATION, should point to the directory you
   installed the NVIDIA GPU Computing SDK (e.g., ~/NVIDIA_GPU_Computing_SDK)

   You must also ensure your PATH includes $CUDA_INSTALL_PATH/bin.	

4. run "make -f Makefile.ispass-2009" in this directory. You should NOT need
   to copy or move any files for this to work.

5. Verify binaries were generated for the benchmarks in ../bin/release/
   (relative to the directory this file located in)

6. Type "source setup_environment" in the v3.x directory.  This modifies your
   LD_LIBRARY_PATH to use GPGPU-Sim for CUDA/OpenCL applications. If you have 
   not already done so, build GPGPU-Sim now.

7. Place a link to the configuration files (gpgpusim.config and the
   interconnect configuration file) in the simulation run directory.  You can 
   do this using the script "setup_config.sh" you will find in this directory, 
   which creates symbolic links. For example type:

	./setup_config.sh GTX480

   Aside: If later you want to change the configuration, you need to first run

        ./setup_config.sh --cleanup

   then run ./setup_config.sh again with the configuration you want.

8. Run one of the applications by typing the command line in the
   README.GPGPU-Sim in the benchmark directory. For example,

	cd AES
	sh README.GPGPU-Sim

9. When debugging the simulator, you should build GPGPU-Sim in debug mode: 

	cd $GPGPUSIM_ROOT
	source setup_environment debug
	make clean
	make

   Example of running the simulator in gdb (starting from this directory):

	cd AES
	gdb --args `cat README.GPGPU-Sim` # different steps required for WP
	

###############################################################################
#                             References                                      #
###############################################################################

AES

S. A. Manavski. CUDA compatible GPU as an efficient hardware
accelerator for AES cryptography. In ICSPC 2007: Proc. of IEEE Int’l
Conf. on Signal Processing and Communication, pages 65–68, 2007.

BFS:

P. Harish and P. J. Narayanan. Accelerating Large Graph Algorithms
on the GPU Using CUDA. In HiPC, pages 197–208, 2007.

CP:

http://www.crhc.uiuc.edu/IMPACT/parboil.php. 

DG:
T. C. Warburton. Mini Discontinuous Galerkin Solvers. 
http://www.caam.rice.edu/˜timwar/RMMC/MIDG.html.

LPS:

M. Giles. Jacobi iteration for a Laplace discretisation on a 3D structured
grid. http://people.maths.ox.ac.uk/˜gilesm/hpc/NVIDIA/laplace3d.pdf

LIB:

M. Giles and S. Xiaoke. Notes on using the NVIDIA 8800 GTX graphics
card. http://people.maths.ox.ac.uk/˜gilesm/hpc/

MUM:

M. Schatz, C. Trapnell, A. Delcher, and A. Varshney. High-throughput
sequence alignment using Graphics Processing Units. BMC Bioinformatics,
 8(1):474, 2007.

NN:

Billconan and Kavinguy. A Neural Network on GPU.
http://www.codeproject.com/KB/graphics/GPUNN.aspx. 

NQU:

Pcchen. N-Queens Solver. 
http://forums.nvidia.com/index.php?showtopic=76893

RAY:

Maxime. Ray tracing. http://www.nvidia.com/cuda.

STO:

S. Al-Kiswany, A. Gharaibeh, E. Santos-Neto, G. Yuan, and M. Ripeanu.
StoreGPU: exploiting graphics processing units to accelerate distributed
storage systems. In Proc. 17th Int’l Symp. on High Performance
Distributed Computing, pages 165–174, 2008. 

WP:

J. Michalakes and M. Vachharajani. GPU acceleration of numerical 
weather prediction. IPDPS 2008: IEEE Int’l Symp. on Parallel and
Distributed Processing, pages 1–7, April 2008.