I have written a converter that takes openstreetmap xml files and converts them to a binary runtime rendering format that is typically about 10% of the original size. Input file sizes are typically 3gb and larger. The input files are not loaded into memory all at once, but streamed as points and polys are collected, then a bsp is run on them and the file is output. Recently on larger files it runs out of memory and dies (the one in question has 14million points and 1million polygons). Typically my program is using about 1gb to 1.2 gb of ram when this happens. I've tried increasing virtual memory from 2 to 8gb (on XP) but this change made no effect. Also, since this code is open-source I would like to have it work regardless of the available ram (albeit slower), it runs on Windows, Linux and Mac.
What techniques can I use to avoid having it run out of memory? Processing the data in smaller sub-sets and then merging the final results? Using my own virtual memory type of handler? Any other ideas?
Close Unnecessary Programs and Applications However, the high memory usage problem is mainly due to the overcrowding of many internal processes. Therefore, it helps to stop the unnecessary programs and applications that are running. Open the Task Manager and check any extra programs you aren't using.
They can be hard to reproduce, hard to debug, and potentially expensive to correct as well. Applications that have memory errors can experience major problems. For example, memory leaks can cause an application to run out of memory resulting in the termination of the application, gracefully or otherwise.
First, on a 32-bit system, you will always be limited to 4 GB of memory, no matter pagefile settings. (And of those, only 2GB will be available to your process on Windows. On Linux, you'll typically have around 3GB available)
So the first obvious solution is to switch to a 64-bit OS, and compile your application for 64-bit. That gives you a huge virtual memory space to use, and the OS will swap data in and out of the pagefile as necessary to keep things working.
Second, allocating smaller chunks of memory at a time may help. It's often easier to find 4 256MB chunks of free memory than one 1GB chunk.
Third, split up the problem. Don't process the entire dataset at once, but try to load and process only a small section at a time.
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