It is a very performance-critical part of a bigger project, so I would be grateful if you could give me some pointers. In other words I expect that at least for. input output and my expectation is that whenever an array value changed, it doesnt match the previous value either. I am fairly new to Julia, and don’t know if it would be possible to further improve the performance of this piece of code (that is, reduce its execution time and allocated memory). Like so: const input 1, 2, 3 const output input.map(value > value 2) There is an input value ( 1, 2, 3 ), map does something with it, and returns an entirely new value. Does anyone know why this is not the case? Further improving performance
The mapreduce() documentation suggests that it should be faster than reduce(map()) because of the lack of intermediate allocation of the map. Whereas with mapreduce, the time and allocated memory are much larger. Consider the same N and elems as above, then the performance I get for the two variants is begin The performance of the variant shown above is unexpectedly much better than that using mapreduce(). J = reduce(vcat, map(e -> e], elems)) įor this simplified example (in reality elems may not have this simple structure), the output should be elems =, ,. I want to create an array of indices (for creation of a sparse matrix in a later stage) based on a list of element connectivity as follows: N = 10000 # Number of elementsĮlems = collect( for i in 1:N) # List of element connectivity
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