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I'm currently working on a project for medical image processing, that needs a huge amount of memory. Is there anything I can do to avoid heap fragmentation and to speed up access of image data that has already been loaded into memory?

The application has been written in C++ and runs on Windows XP.

EDIT: The application does some preprocessing with the image data, like reformatting, calculating look-up-tables, extracting sub images of interest ... The application needs about 2 GB RAM during processing, of which about 1,5 GB may be used for the image data.

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If you are doing medical image processing it is likely that you are allocating big blocks at a time (512x512, 2-byte per pixel images). Fragmentation will bite you if you allocate smaller objects between the allocations of image buffers.

Writing a custom allocator is not necessarily hard for this particular use-case. You can use the standard C++ allocator for your Image object, but for the pixel buffer you can use custom allocation that is all managed within your Image object. Here's a quick and dirty outline:

  • Use a static array of structs, each struct has:
    • A solid chunk of memory that can hold N images -- the chunking will help control fragmentation -- try an initial N of 5 or so
    • A parallel array of bools indicating whether the corresponding image is in use
  • To allocate, search the array for an empty buffer and set its flag
    • If none found, append a new struct to the end of the array
  • To deallocate, find the corresponding buffer in the array(s) and clear the boolean flag

This is just one simple idea with lots of room for variation. The main trick is to avoid freeing and reallocating the image pixel buffers.


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