I need to find for each point of the data set all its nearest neighbors. The data set contains approx. 10 million 2D points. The data are close to the grid, but do not form a precise grid...
This is probably a common situation, but I couldn\'t find a specific answer on SO or Google. I have a large table (>10 million rows) of friend relationships on a MySQL database that is very important
I have a web page where you can customize your game character. In order to speed up browsing (gems) I load entire gems database (600 entries, 247KB) as a separate .js file, so it can be cached and I d
There\'s this script called svnmerge.py that I\'m trying to tweak and optimize a bit. I\'m completely new to Python though, so it\'s not ea开发者_StackOverflow中文版sy.
I need to fill in a large (maybe not so much - several thousands of entries) dataset to a Gtk::TreeModelColumn. How do I do that without locking up the application. Is it safe to put the processing in
This solution works but performance is lower than expected.A query returning 200K rows takes several minutes and pegs the CPU on my dev box. Running the same* query in query analyzer returns all resul
I have a huge table in a database and I want to split that into several parts physically, maintaining the database scheme.
I have an extremely large 2D bytearray in memory, byte MyBA = new byte[int.MaxValue][10]; Is there any way (probably unsafe) that I can fool C# into thinking this is one huge continuous byte array?
When displaying an inline for a model, if there\'s a large number of inlines, the change page then loads slowly, and can be hard to navigate through all of them. I\'m already using an inline-collapsin
I am dealing with large amounts of scientific data that are stored in tab separated .tsv files. The typical operations to be performed are reading several large files, filtering out only certain colum
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