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1 Introduction
================================
The scc-par parallel strongly connected components (SCC) algorithm [1] computes
all strongly connected components on a given input graph. It is optimized for 
small-world graphs, which are characterized by a low diameter and a large 
strongly-connected component on the order of the number of nodes. 

The C++ codes in scc-par assume the following libraries:

  * g++ (with builtin atomic functions)
  * g++ (with OpenMP support)
  * a custom graph library and runtime (gm_graph) 

The first two are supported by any recent g++ distributions (version 4.2 or higher); 
the third one is included in this source package.

2 Source Package
================================
The source code of scc-par is available as a zip file from:

    http://www.stanford.edu/~nrodia

It includes three main directories:

  * gm_graph -- the custom graph library and runtime
  * src -- the parallel SCC algorithm source files
  * tools -- the graph dataset format conversion tool

3 Compiling and Executing
================================
Compile the gm_graph runtime library and the scc binary by running make in the 
top-level directory. 

To execute scc-par, in the top-level directory, run:

./scc <graph_name> <num_threads> <method> {-d|-a|-p}

Calling ./scc with no input parameters will print the help, which describes
all of the options. 

Also included is a graph conversion utility to convert adjacency list or edge list
graph files into the binary format used by scc-par. To use this utility, you
will first need to download and install Green-Marl, available at:
    http://github.com/stanford-ppl/Green-Marl/

In the Makefile in the tools directory, set GM_TOP to the top-level Green-Marl
directory. To compile and print the help 
message for the conversion program, in the tools directory:

make
./convert -?

4 License
================================
Copyright (c) 2013 Stanford University, unless otherwise specified.
All rights reserved.

This software was developed by the Pervasive Parallelism Laboratory of
Stanford University, California, USA.

Permission to use, copy, modify, and distribute this software in source
or binary form for any purpose with or without fee is hereby granted,
provided that the following conditions are met:

   1. Redistributions of source code must retain the above copyright
      notice, this list of conditions and the following disclaimer.

   2. Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.

   3. Neither the name of Stanford University nor the names of its
      contributors may be used to endorse or promote products derived
      from this software without specific prior written permission.


THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
SUCH DAMAGE.

5 Reference
================================
Please cite the following paper when referencing this code.

[1] S. Hong, N. C. Rodia, and K. Oluktoun, "On fast parallel detection of strongly
connected components (scc) in small-world graphs," SC 2013.