At present the framework uses the same data structures, caches, etc. for the calculation of the generated normalization integral matrix as the accepted. In cases where the acceptance is small O(1%) this places an unnecessary burden on memory. The generated NI's never need to be recomputed throughout a fit, so there is no need for the caching mechanisms that are in place for the accepted NI's (which are recomputed if the amplitudes contains free parameters). Maybe there is a way to reduce memory usage for the generated NI's, e.g., compute in blocks of events and sum?
At present the framework uses the same data structures, caches, etc. for the calculation of the generated normalization integral matrix as the accepted. In cases where the acceptance is small O(1%) this places an unnecessary burden on memory. The generated NI's never need to be recomputed throughout a fit, so there is no need for the caching mechanisms that are in place for the accepted NI's (which are recomputed if the amplitudes contains free parameters). Maybe there is a way to reduce memory usage for the generated NI's, e.g., compute in blocks of events and sum?