One of the challenges to finding optimal solutions is the coupling of variables. If we could change one variable at a time, the search process would be so much easier. But in most problems the variables are strongly coupled, so the best value for one variable depends on the values of many other variables.
Often, we have no control over this variable coupling, since it is inherent in the physics model that defines our objectives and constraints. In these cases, we need a powerful optimization search strategy like SHERPA to figure out the complexities of the design landscape and to produce an optimized solution.
But in some cases we make this task harder than it needs to be because of how we represent the problem. That is, sometimes the way we define the problem creates unnecessary coupling or increases the complexity of existing coupling among variables. This makes the optimization search harder, and may decrease the chance of finding the optimal solution within our limited optimization search budget. The good news is that we can often alleviate this situation with a different representation. Continue reading
This new feature provides the ability to treat multiple computer resources as a single resource, without a job queuing system, to parallelize your HEEDS study. It allows for tremendous flexibility in the use and management of disparate computer resources.
As the name suggests, a compute resource set is a set of previously defined compute resources in HEEDS. The set allows you to define a pool of hardware resources that can be used to parallelize one or more analyses in the HEEDS study. The definition is not limited to homogeneous resources but can include any type of compute resource available. For example, a resource set could be a set of Windows workstations (as shown in the example image below), a set of Windows and Linux workstations, some workstations along with a cluster, several different clusters, etc. During the run, HEEDS will manage the job submissions to the resources defined in the set to make sure they are used effectively. Continue reading
The analysis definition process has been upgraded to streamline the definition. The new design facilitates data reuse, simplifies the definition steps and results in a reduced setup time. All the information related to communication with different compute resources has been removed from the analysis setup and moved into its own section.