Distributed Sparse Matrices for Very High Level Languages

Sparse matrices are first class objects in many VHLLs (very high level languages) used for scientific computing. They are a basic building block for various numerical and combinatorial algorithms. Parallel computing is becoming ubiquitous, specifically due to the advent of multi-core architectures. As existing VHLLs are adapted to emerging architectures, and new ones are conceived, one must rethink tradeoffs in language design. We describe the design and implementation of a sparse matrix infrastructure for Star-P, a parallel implementation of the Matlab R © programming language. We demonstrate the versatility of our infrastructure by using it to implement a benchmark that creates and manipulates large graphs. Our design is by no means specific to Star-P — we hope it will influence the design of sparse matrix infrastructures in other languages.