Memory mapped file for numpy arrays

Question

I need to read in parts of a huge numpy array stored in a memory mapped file, process the data and repeat for another part of the array. The whole numpy array takes up around 50 GB and my machine has 8 GB of RAM.

I initially created the memory mapped file using numpy.memmap by reading in a lot of smaller files and processing their data and then writing the processed data to the memmap file. During the creation of the memmap file, I had no memory issues (I was using memmap.flush() periodically). Here's how I create the memory mapped file:

mmapData = np.memmap(mmapFile,mode='w+', shape=(large_no1,large_no2))
for i1 in np.arange(numFiles):
   auxData = load_data_from(file[i1])
   mmapData[i1,:] = auxData
   mmapData.flush() % Do this every 10 iterations or so

However, when I try to access small portions (<10 MB) of the memmap file, it floods my whole ram when the memmap object is created. The machine slows down drastically and I can't do anything. Here's how I try to read in the data from the memory mapped file:

mmapData = np.memmap(mmapFile, mode='r',shape=(large_no1,large_no2))
aux1 = mmapData[5,1:1e7]

I thought using mmap or numpy.memmap should allow me to access parts of massive arrays without trying to load the whole thing to memory. What am I missing?

Am I using the wrong tool to access parts of a large numpy array (> 20 GB) stored in disk?

Answer 1

Could it be that you're looking at virtual, rather than physical memory consumption, and the slowdown is coming from something else?