A simple scalability test for MIMD code

Code scalability, which is crucial in any parallel system, determines how well parallel code avoids becoming a bottleneck as its host computer is made larger. The scalability of computer code can be estimated by statistically designed experiments that empirically approximate a multivariate Taylor expansion of the code´s execution response function. Each suspected code bottleneck corresponds to a first-order term in the expansion, the coefficient for that term indicating how sensitive execution is to changes in the suspect location. However, it is the coefficients for second-order interactions between code segments and the number of processors that are fundamental in discovering which program elements limit parallel speedup. Extending an earlier formulation, a new unified view via these second-order terms yields an informal scaling test of high utility in code development