How to use the AMD Threadripper for Scientific Computations

In the last blog entry we introduced a new optimization algorithm -  GCMA-ES   - and a new method to apply Lambert transfers for trajectory optimization - the Stochastic Lambert Solver. But we applied both methods only to variants of existing problems from the ESA GTOP optimization benchmark database GTOP Benchmarks .  As I showed Ingo Althöfer these results, he had the idea to apply the methods to something new. Ingo is Team-Manager of Team Jena which participated in all GTOC competitions since GTOC 5. This team has two special properties: it is both the smallest and the least successful team in the top ten. Probably these properties correlate. Beside its manager Team Jena has only one worker, the author of this blog. GTOC 10 is near, so if my manager has a training task for me, I should start working on it. And it is getting cold in German November 2018, but not in my flat as long as my AMD TR 2990WX is running full thrust utilizing all 64 threads. Around the W...

Open source real world optimization benchmarks hard enough to drive existing methods towards their limits are extremely rare. ESA’s Messenger full mission benchmark GTOP Task Messenger Full fulfills this criteria and therefore is currently a kind of reference problem to evaluate the effectiveness of optimization methods. It resembles an accurate model of Messengers full mission trajectory, so its solution is of real value for space mission planning. Currently the playing field is dominated by stochastic methods like ACO (Ant Colony Optimization), its commercial implementation Midaco (Mixed integer distributed ant colony optimization) and the open source CMA-ES (covariance matrix adaptation evolution strategy). In MIDACO Messenger simple retry mechanisms for ACO/Midaco and CMA-ES are compared – resulting in a slight advantage for ACO/Midaco over CMA-ES. Simple retry mechanisms generally are not able to solve the Messenger Full problem. Based on ACO/Midaco a more sophisticated retr...