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6

1

import numpy as np



array = np.zeros((210000, 210000)) # default numpy.float64

array.nbytes

When I run the above code on my 8GB memory MacBook with macOS, no error occurs. But running the same code on a 16GB memory PC with Windows 10, or a 12GB memory Ubuntu laptop, or even on a 128GB memory Linux supercomputer, the Python interpreter will raise a MemoryError. All the test environments have 64-bit Python 3.6 or 3.7 installed.

python macos numpy memory

share|improve this question

edited 4 mins ago

Boann

37.1k1290121

asked 5 hours ago

Blaise WangBlaise Wang

728

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  MacOS extends memory with virtual memory on your disk. Check your process details with Activity Monitor and you'll find a Virtual Memory: 332.71 GB entry. But it's all zeros, so it compresses really, really well..
  
  – Martijn Pieters♦
  5 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @MartijnPieters but Windows 10 and Linux also have similar mechanisms. Windows 10 has virtual memory and Linux have swap. Activity Monitor doesn't have VM for 332.71 GB. I use sysctl vm.swapusage to see the real VM usage and got 1200 M
  
  – Blaise Wang
  5 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  But they don't compress.
  
  – Martijn Pieters♦
  5 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @MartijnPieters The problem is that Windows 10 added support of RAM compression science build 10525. But still cannot run the above code.
  
  – Blaise Wang
  2 hours ago
  

  
  
  
  

add a comment | 

6

1

import numpy as np



array = np.zeros((210000, 210000)) # default numpy.float64

array.nbytes

When I run the above code on my 8GB memory MacBook with macOS, no error occurs. But running the same code on a 16GB memory PC with Windows 10, or a 12GB memory Ubuntu laptop, or even on a 128GB memory Linux supercomputer, the Python interpreter will raise a MemoryError. All the test environments have 64-bit Python 3.6 or 3.7 installed.

python macos numpy memory

share|improve this question

edited 4 mins ago

Boann

37.1k1290121

asked 5 hours ago

Blaise WangBlaise Wang

728

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  MacOS extends memory with virtual memory on your disk. Check your process details with Activity Monitor and you'll find a Virtual Memory: 332.71 GB entry. But it's all zeros, so it compresses really, really well..
  
  – Martijn Pieters♦
  5 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @MartijnPieters but Windows 10 and Linux also have similar mechanisms. Windows 10 has virtual memory and Linux have swap. Activity Monitor doesn't have VM for 332.71 GB. I use sysctl vm.swapusage to see the real VM usage and got 1200 M
  
  – Blaise Wang
  5 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  But they don't compress.
  
  – Martijn Pieters♦
  5 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @MartijnPieters The problem is that Windows 10 added support of RAM compression science build 10525. But still cannot run the above code.
  
  – Blaise Wang
  2 hours ago
  

  
  
  
  

add a comment | 

import numpy as np



array = np.zeros((210000, 210000)) # default numpy.float64

array.nbytes

When I run the above code on my 8GB memory MacBook with macOS, no error occurs. But running the same code on a 16GB memory PC with Windows 10, or a 12GB memory Ubuntu laptop, or even on a 128GB memory Linux supercomputer, the Python interpreter will raise a MemoryError. All the test environments have 64-bit Python 3.6 or 3.7 installed.

python macos numpy memory

share|improve this question

edited 4 mins ago

Boann

37.1k1290121

asked 5 hours ago

Blaise WangBlaise Wang

728

import numpy as np



array = np.zeros((210000, 210000)) # default numpy.float64

array.nbytes

share|improve this question

edited 4 mins ago

Boann

37.1k1290121

asked 5 hours ago

Blaise WangBlaise Wang

728

share|improve this question

edited 4 mins ago

Boann

37.1k1290121

asked 5 hours ago

Blaise WangBlaise Wang

728

edited 4 mins ago

Boann

37.1k1290121

edited 4 mins ago

Boann

37.1k1290121

asked 5 hours ago

Blaise WangBlaise Wang

728

asked 5 hours ago

Blaise WangBlaise Wang

728

1 Answer
1

active

oldest

votes

9

You are most likely using Mac OS X Mavericks or newer, so 10.9 or up. From that version onwards, MacOS uses virtual memory compression, where memory requirements that exceed your physical memory are not only redirected to memory pages on disk, but those pages are compressed to save space.

For your ndarray, you may have requested ~332GB of memory, but it's all a contiguous sequence of NUL bytes at the moment, and that compresses really, really well:

That's a screenshot from the Activity Monitor tool, with the process details of my Python process where I replicated your test (use the (I) icon on the toolbar to open it); this is from the Memory tab, where you can see that the Real Memory Size column is only 9.3 MB used, against a Virtual Memory Size of 332.71GB.

Once you start setting other values for those indices, you'll quickly see the memory stats increase to gigabytes instead of megabytes:

while True:

    index = tuple(np.random.randint(array.shape[0], size=2))

    array[index] = np.random.uniform(-10 ** -307, 10 ** 307)

or you can push the limit further by assigning to every index (in batches, so you can watch the memory grow):

array = array.reshape((-1,))

for i in range(0, array.shape[0], 10**5):

    array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

The process is eventually terminated; my Macbook Pro doesn't have enough swap space to store hard-to-compress gigabytes of random data:

>>> array = array.reshape((-1,))

>>> for i in range(0, array.shape[0], 10**5):

...     array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

...

Killed: 9

You could argue that MacOS is being too trusting, letting programs request that much memory without bounds, but with memory compression, memory limits are much more fluid. Your np.zeros() array does fit your system, after all. Even though you probably don't actually have the swap space to store the uncompressed data, compressed it all fits fine so MacOS allows it and terminates processes that then take advantage of the generosity.

If you don't want this to happen, use resource.setrlimit() to set limits on RLIMIT_STACK to, say 2 ** 14, at which point the OS will segfault Python when it exceeds the limits.

share|improve this answer

edited 1 hour ago

answered 5 hours ago

Martijn Pieters♦Martijn Pieters

715k13825002312

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Memory compression should only matter after allocation has already succeeded. The problem here is probably rather either memory limits (ulimits on linux for example) or more likely that the allocator doesn't find a 300GB sized chunk. If you split those up into 100 3GB pieces it would probably work on windows or linux (with big enough swap) as well.
  
  – inf
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @inf: I don't have 300GB free on my SSD. I do run out of memory when I start filling the array, randomly.
  
  – Martijn Pieters♦
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Define "run out of memory", do you get a MemoryError or just start filling RAM, swapping and get OOMed?
  
  – inf
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @inf: I'm a little reluctant to actually let it run.. As the memory has been allocated by the OS (tracemalloc confirms Python has been given the memory allocation), there won't be a MemoryError, so it'll start swapping and eventually OOMed. But before that point this laptop will be hard to use for a while as everything else is swapped out first.
  
  – Martijn Pieters♦
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I understand :) But that's what I mean. The allocation doesn't even succeed on ubuntu and linux and hence the MemoryError.
  
  – inf
  3 hours ago
  

  
  
  
  

add a comment | 

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9

You are most likely using Mac OS X Mavericks or newer, so 10.9 or up. From that version onwards, MacOS uses virtual memory compression, where memory requirements that exceed your physical memory are not only redirected to memory pages on disk, but those pages are compressed to save space.

For your ndarray, you may have requested ~332GB of memory, but it's all a contiguous sequence of NUL bytes at the moment, and that compresses really, really well:

That's a screenshot from the Activity Monitor tool, with the process details of my Python process where I replicated your test (use the (I) icon on the toolbar to open it); this is from the Memory tab, where you can see that the Real Memory Size column is only 9.3 MB used, against a Virtual Memory Size of 332.71GB.

Once you start setting other values for those indices, you'll quickly see the memory stats increase to gigabytes instead of megabytes:

while True:

    index = tuple(np.random.randint(array.shape[0], size=2))

    array[index] = np.random.uniform(-10 ** -307, 10 ** 307)

or you can push the limit further by assigning to every index (in batches, so you can watch the memory grow):

array = array.reshape((-1,))

for i in range(0, array.shape[0], 10**5):

    array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

The process is eventually terminated; my Macbook Pro doesn't have enough swap space to store hard-to-compress gigabytes of random data:

>>> array = array.reshape((-1,))

>>> for i in range(0, array.shape[0], 10**5):

...     array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

...

Killed: 9

You could argue that MacOS is being too trusting, letting programs request that much memory without bounds, but with memory compression, memory limits are much more fluid. Your np.zeros() array does fit your system, after all. Even though you probably don't actually have the swap space to store the uncompressed data, compressed it all fits fine so MacOS allows it and terminates processes that then take advantage of the generosity.

If you don't want this to happen, use resource.setrlimit() to set limits on RLIMIT_STACK to, say 2 ** 14, at which point the OS will segfault Python when it exceeds the limits.

share|improve this answer

edited 1 hour ago

answered 5 hours ago

Martijn Pieters♦Martijn Pieters

715k13825002312

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Memory compression should only matter after allocation has already succeeded. The problem here is probably rather either memory limits (ulimits on linux for example) or more likely that the allocator doesn't find a 300GB sized chunk. If you split those up into 100 3GB pieces it would probably work on windows or linux (with big enough swap) as well.
  
  – inf
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @inf: I don't have 300GB free on my SSD. I do run out of memory when I start filling the array, randomly.
  
  – Martijn Pieters♦
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Define "run out of memory", do you get a MemoryError or just start filling RAM, swapping and get OOMed?
  
  – inf
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @inf: I'm a little reluctant to actually let it run.. As the memory has been allocated by the OS (tracemalloc confirms Python has been given the memory allocation), there won't be a MemoryError, so it'll start swapping and eventually OOMed. But before that point this laptop will be hard to use for a while as everything else is swapped out first.
  
  – Martijn Pieters♦
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I understand :) But that's what I mean. The allocation doesn't even succeed on ubuntu and linux and hence the MemoryError.
  
  – inf
  3 hours ago
  

  
  
  
  

add a comment | 

9

You are most likely using Mac OS X Mavericks or newer, so 10.9 or up. From that version onwards, MacOS uses virtual memory compression, where memory requirements that exceed your physical memory are not only redirected to memory pages on disk, but those pages are compressed to save space.

For your ndarray, you may have requested ~332GB of memory, but it's all a contiguous sequence of NUL bytes at the moment, and that compresses really, really well:

That's a screenshot from the Activity Monitor tool, with the process details of my Python process where I replicated your test (use the (I) icon on the toolbar to open it); this is from the Memory tab, where you can see that the Real Memory Size column is only 9.3 MB used, against a Virtual Memory Size of 332.71GB.

Once you start setting other values for those indices, you'll quickly see the memory stats increase to gigabytes instead of megabytes:

while True:

    index = tuple(np.random.randint(array.shape[0], size=2))

    array[index] = np.random.uniform(-10 ** -307, 10 ** 307)

or you can push the limit further by assigning to every index (in batches, so you can watch the memory grow):

array = array.reshape((-1,))

for i in range(0, array.shape[0], 10**5):

    array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

The process is eventually terminated; my Macbook Pro doesn't have enough swap space to store hard-to-compress gigabytes of random data:

>>> array = array.reshape((-1,))

>>> for i in range(0, array.shape[0], 10**5):

...     array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

...

Killed: 9

You could argue that MacOS is being too trusting, letting programs request that much memory without bounds, but with memory compression, memory limits are much more fluid. Your np.zeros() array does fit your system, after all. Even though you probably don't actually have the swap space to store the uncompressed data, compressed it all fits fine so MacOS allows it and terminates processes that then take advantage of the generosity.

If you don't want this to happen, use resource.setrlimit() to set limits on RLIMIT_STACK to, say 2 ** 14, at which point the OS will segfault Python when it exceeds the limits.

share|improve this answer

edited 1 hour ago

answered 5 hours ago

Martijn Pieters♦Martijn Pieters

715k13825002312

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Memory compression should only matter after allocation has already succeeded. The problem here is probably rather either memory limits (ulimits on linux for example) or more likely that the allocator doesn't find a 300GB sized chunk. If you split those up into 100 3GB pieces it would probably work on windows or linux (with big enough swap) as well.
  
  – inf
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @inf: I don't have 300GB free on my SSD. I do run out of memory when I start filling the array, randomly.
  
  – Martijn Pieters♦
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Define "run out of memory", do you get a MemoryError or just start filling RAM, swapping and get OOMed?
  
  – inf
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @inf: I'm a little reluctant to actually let it run.. As the memory has been allocated by the OS (tracemalloc confirms Python has been given the memory allocation), there won't be a MemoryError, so it'll start swapping and eventually OOMed. But before that point this laptop will be hard to use for a while as everything else is swapped out first.
  
  – Martijn Pieters♦
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I understand :) But that's what I mean. The allocation doesn't even succeed on ubuntu and linux and hence the MemoryError.
  
  – inf
  3 hours ago
  

  
  
  
  

add a comment | 

You are most likely using Mac OS X Mavericks or newer, so 10.9 or up. From that version onwards, MacOS uses virtual memory compression, where memory requirements that exceed your physical memory are not only redirected to memory pages on disk, but those pages are compressed to save space.

For your ndarray, you may have requested ~332GB of memory, but it's all a contiguous sequence of NUL bytes at the moment, and that compresses really, really well:

That's a screenshot from the Activity Monitor tool, with the process details of my Python process where I replicated your test (use the (I) icon on the toolbar to open it); this is from the Memory tab, where you can see that the Real Memory Size column is only 9.3 MB used, against a Virtual Memory Size of 332.71GB.

Once you start setting other values for those indices, you'll quickly see the memory stats increase to gigabytes instead of megabytes:

while True:

    index = tuple(np.random.randint(array.shape[0], size=2))

    array[index] = np.random.uniform(-10 ** -307, 10 ** 307)

or you can push the limit further by assigning to every index (in batches, so you can watch the memory grow):

array = array.reshape((-1,))

for i in range(0, array.shape[0], 10**5):

    array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

The process is eventually terminated; my Macbook Pro doesn't have enough swap space to store hard-to-compress gigabytes of random data:

>>> array = array.reshape((-1,))

>>> for i in range(0, array.shape[0], 10**5):

...     array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

...

Killed: 9

You could argue that MacOS is being too trusting, letting programs request that much memory without bounds, but with memory compression, memory limits are much more fluid. Your np.zeros() array does fit your system, after all. Even though you probably don't actually have the swap space to store the uncompressed data, compressed it all fits fine so MacOS allows it and terminates processes that then take advantage of the generosity.

If you don't want this to happen, use resource.setrlimit() to set limits on RLIMIT_STACK to, say 2 ** 14, at which point the OS will segfault Python when it exceeds the limits.

share|improve this answer

edited 1 hour ago

answered 5 hours ago

Martijn Pieters♦Martijn Pieters

715k13825002312

while True:

    index = tuple(np.random.randint(array.shape[0], size=2))

    array[index] = np.random.uniform(-10 ** -307, 10 ** 307)

array = array.reshape((-1,))

for i in range(0, array.shape[0], 10**5):

    array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

>>> array = array.reshape((-1,))

>>> for i in range(0, array.shape[0], 10**5):

...     array[i:i + 10**5] = np.random.uniform(-10 ** -307, 10 ** 307, 10**5)

...

Killed: 9

share|improve this answer

edited 1 hour ago

answered 5 hours ago

Martijn Pieters♦Martijn Pieters

715k13825002312

answered 5 hours ago

Martijn Pieters♦Martijn Pieters

715k13825002312

answered 5 hours ago

Martijn Pieters♦Martijn Pieters

715k13825002312