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Parallel minimization via Geneva, ScaLAPACK and MPI on the GSI cluster

The theory group in collaboration with the scientific computing group at GSI is preparing a general framework to predict and analyze hadronic final-state interactions. This is of crucial importance for a successful interpretation of data to be taken by the PANDA collaboration at FAIR. In 2012’s Annual Report [1], we discussed our motivation in depth and presented our choices of software for the underlying needs of our framework. In this report, we want to explore the framework’s near final form and the workflow we are using for code developement. We use Mathematica as the primary physic based developement environment, to ensure easy mathematical correctness and consistency by using Mathematica’s internal checks. MathCode C++ is then able to compile Mathematica’s code into native C++ code. We further extended this code with our own functions and classes, to enable the usage of ScaLAPACK’s scalable solving of linear systems in Mathematica. The desired use case requires to minimize a function with respect to a specific parameter set. The problem we needed to solve, was to create that function by MathCode C++, which needs ScaLAPACK’s cluster wide scale. Geneva is a C++ library for clustered problem solving by using evolutionary algorithms. In Geneva, in order to minimize our function, a broker sends singular genotypes to clients (in our usecase: varying parameters of our function) to let the clients evaluate their phenotype’s fitness (in our usecase: the chisquare of that set of parameters). After many iterations over a well-sized population a good quality minimum will be reached. In combination, we can