- The SSCA#2 spec (Ver 1.1).
- High Performance Graph Algorithms from Parallel Sparse Matrices

SSCA#2 is the graph analysis benchmark in the Scalable Synthetic Compact Application Benchmarks. Its purpose is to stress memory access, using mainly integer (and optionally, character) operations on extremely large graphs. It is a compute-intensive benchmark, which is hard to parallelize. One of the important figures of merit for this benchmark are the largest problem size that can be solved on a given computer.

Large-scale computation on graphs and other discrete structures is becoming increasingly important in many applications, including computational biology, web search, and knowledge discovery. High-performance combinatorial computing is an infant field, in sharp contrast with numerical scientific computing.

We argue that many of the tools of high-performance numerical computing - in particular, parallel algorithms and data structures for computation with sparse matrices - can form the nucleus of a robust infrastructure for parallel computing on graphs. We demonstrate this with a graph analysis benchmark (SSCA#2) using the sparse matrix infrastructure in StarP, our parallel dialect of the matlab programming language.