I'm implementing an inference algorithm and am running into memory allocation issues that are slowing it down. I created a minimal example that resembles my algorithm and see that the problem persists.
The issue is that Julia is allocating a lot of extra memory when adding matrices together. This happens regardless of whether or not I preallocate the output matrix. Minimal example: https://gist.github.com/colincsl/ab44884c5542539f813d Memory output of minimal example (using julia --track-allocation=user): https://gist.github.com/colincsl/c9c9dd86fca277705873 Am I misunderstanding something? Should I be performing the operation differently? One thing I've played with is the matrix C. The indices are a sliding window (e.g. use C[t-10:t] for all t). When I remove C from the equation the performance increases by a factor of 2.5. However, it still uses more memory than expected. Could this be the primary issue?
