Combinatorial BLAS Library (MPI reference implementation)

1.0

Authors:
Aydin Buluc, John R. Gilbert
This material is based upon work supported by the National Science Foundation under Grant No. 0709385. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF)

Introduction

Download

Requirements: You need a recent C++ compiler (g++ version 4.2 or higher - and compatible), a compliant MPI-2 implementation, and a TR1 library (libstdc++ that comes with g++ has them). If not, you can use the boost library and pass the -DNOTR1 option to the compiler (cmake will automatically do it for you); it will work if you just add boost's path to $INCADD in the makefile. The recommended tarball uses the CMake build system, but only to build the documentation and unit-tests, and to automate installation. The chances are that you're not going to use any of our sample applications "as-is", so you can just modify them or imitate their structure to write your own application by just using the header files. There are very few binary libraries to link to, and no configured header files. Like many high-performance C++ libraries, the Combinatorial BLAS is mostly templated.

Documentation: This is a reference implementation of the Combinatorial BLAS Library in C++/MPI. It is purposefully designed for distributed memory platforms though it also runs in uniprocessor and shared-memory (such as multicores) platforms. It contains efficient implementations of novel data structures/algorithms as well as reimplementations of some previously known data structures/algorithms for convenience. More details can be found in Chapter 4 of my thesis [1].

The main data structure is a distributed sparse matrix ( SpParMat <IT,NT,DER> ) which HAS-A sequential sparse matrix ( SpMat <IT,NT> ) that can be implemented in various ways as long as it supports the interface of the base class (currently: SpTuples, SpCCols, SpDCCols).

For example, the standard way to declare a parallel sparse matrix A that uses 32-bit integers for indices, floats for numerical values (nonzeros), SpDCCols <int,float> for the underlying sequential matrix operations is:

The repetitions of int and float types inside the SpDCCols< > is a direct consequence of the static typing of C++ and is akin to some STL constructs such as vector<int, SomeAllocator<int> >

The supported operations (a growing list) are:

All the binary operations can be performed on matrices with different numerical value representations. The type-traits mechanism will take care of the automatic type promotion. Of course, you have to declare the return value type appropriately (until C++0x is out, which has auto )

Some features it uses:

Sequential classes:

Parallel classes:

Applications implemented using Combinatorial BLAS:

Performance results of both applications can be found in Chapter 5 of my thesis [1].

Test programs demonstrating how to use the library:

Citation: Please cite my thesis [1] if you end up using the Combinatorial BLAS in your research.

counter