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Octocat-spinner-32 examples
Octocat-spinner-32 include
Octocat-spinner-32 numpush
Octocat-spinner-32 tests
Octocat-spinner-32 .gitignore
Octocat-spinner-32 LICENSE
Octocat-spinner-32 Makefile
Octocat-spinner-32 NOTICE
Octocat-spinner-32 README.md
Octocat-spinner-32 requirements.txt
Octocat-spinner-32 setup.py
README.md

Notice

Deprecated project. I am abandoning the Numpush project in favor of working on Blaze at @ContinuumIO. Blaze is the future of shared memory and distributed array computations. The project will be public in November. See:

numpush

Efficient distribution for scientific Python data structures and code.

Numpush is not a clustering or map-reduce solution, it is merely a library of common functions that are often needed when building distributed scientific Python applications.

Target Infastructure

  • Libraries
    • numpy
    • pandas
    • numba
    • bitey
    • llvm-py
    • theano
    • numexpr
    • hdf5
    • carray
  • Datastore Backends
    • MooseFS
    • Redis
  • Network transports
    • TIPC
    • ZeroMQ
    • TCP
  • IO Operations
    • sendfile
    • splice
    • mmap
    • read/write

Note: This is indication of the direction of the project, quite a bit of it is incomplete at this time.

Data

The core idea is based on the observation that most scientific Python data structures can be represented by two parts:

  1. Large block of indexable memory
  2. Small bits of metadata

(1) is a heavy object to move around, so we use fast transports (i.e. ZeroMQ, TIPC ) that all act on a universal memoryview interface doing low-level POSIX IO calls ( mmap, splice, sendfile ) that don't require needless copying in userspace to send data and can delegate scheduling to the Linux kernel.

(2) are trivial to move around, these are pickled or msgpacked and moved around easily since they are most often PyObject's or can be encoded as PyObjects.

For example a Pandas dataframe is a Numpy array with some extra metadata and logical methods. The logic exists wherever the library is installed, to the move the dataframe we need then only encode the metadata and buffer the internal array.

>>> df = DateFrame({'a': [1,2,3], 'b': [4,5,6]})
   a  b
0  1  4
1  2  5
2  3  6

>>> index = df.index.tolist()
>>> columns = df.columns.tolist()
>>> dtype = df.values.dtype
>>> shape = df.values.shape

>>> nd = buffer(df.values)

... sent over the wire ..

>>> nd2 = np.frombuffer(nd, dtype=dtype).reshape(shape)

>>> df2 = DataFrame(nd2, index=index, columns=columns)

Code

The code scenario is slightly different, but usually the idea is that we have

  1. Chunk of bytecode/bitcode that is moveable between homogeneous computational architectures.
  2. Some associated "state" that is needed to bootstrap the code

(1) is most often simply a string buffer and moving them around the network is trivial. Quite often (2) does not ever occur since we are just moving pure objects around.

For example a slug of Numba LLVM that does some simple numerical operations, like:

define i32 @myfunc(i32, i32, i32) {
        %3 = add i32 %0, %1
        %4 = mul i32 %3, %2
        ret i32 %4
}

Can easily be serialized over the network, recompile at the target and translated into a Numpy ufunc by the Numba infrastructure.

'BC\xc0\xde!\x0c\x00\x00z\x00\x00\x00\x01\x10\x00\x00\x12\x00\x00\x00\x07\x81#\x91A'

Shared Memory

Once data is at a remote target one often wants to farm it out to as many cores (i.e. through multiprocessing ) as possible all with the same shared memory source. For numpy derivative data structures this is not difficult ( though very undocumented! ). To that end there are some simple shared memory methods included for doing simple shared memory array operations with mutex.

For example one might want a shared memory-backed DataFrame and to work on it across 8 cores.

This could be done with ctypes hacks on multiprocessing.RawArray, but they were ugly hacks. The numpush.shmem module has a cleaner implementation.

from multiprocessing import Pool
from numpush.shmem import SDataFrame

df = SDataFrame(.. your enormous dataframe .. )

def f(df):
    ... modify shared memory dataframe ...

pool = Pool(processes=8)
pool.apply(f, df)
pool.join()

Data Stores

Not going to try and solve the problem of distributed fault-tolerant datastores, both MooseFS and Redis work nearly out of the box and can address most use cases. The goal is simply to make them interface with scientific Python libraries more easily.

MooseFS is distributed, fault-tolerant and quite easy to bootstrap on small-medium size clusters, especially on EC2. Works well with data that can be manipulated as file-like objects.

Redis is local, fast, simple, and with hiredis can work efficiently with large buffers. Works well for small data simple data that fits into key-value model.

Required

  • libzmq
  • Python 2.7.x ( memoryview is used copiously )
  • pyzmq 2.1.11
  • msgpack
  • Cython 0.16

Optional

License

Released under the MIT license.

Copyright (c) 2012 Stephen Diehl, <stephen.m.diehl@gmail.com>
See LICENSE and NOTICE for details.
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