Probably not, because the specifics of the workload - exact parameters, representation of data in memory, value ranges etc - lead you to highly divergent optimization strategies.
Probably not, because the specifics of the workload - exact parameters, representation of data in memory, value ranges etc - lead you to highly divergent optimization strategies.
shouldn't it be possible to be run as a mlautoresearch project? i.e. orchestrate 10 strategies to speed it up, run in paralellel, pick the winning and go from there?
You are assuming all problems in the world are solvable by one of "10 strategies".