distributed_do.py 14.6 KB
Newer Older
Martin Reinecke's avatar
Martin Reinecke committed
1
from __future__ import print_function
2
3
4
5
import numpy as np
from .random import Random
from mpi4py import MPI

Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
6
7
8
_comm = MPI.COMM_WORLD
ntask = _comm.Get_size()
rank = _comm.Get_rank()
Martin Reinecke's avatar
Martin Reinecke committed
9
master = (rank == 0)
10
11


Martin Reinecke's avatar
Martin Reinecke committed
12
13
14
15
16
def mprint(*args):
    if master:
        print(*args)


Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
17
def _shareSize(nwork, nshares, myshare):
Martin Reinecke's avatar
Martin Reinecke committed
18
    return (nwork//nshares) + int(myshare < nwork % nshares)
Martin Reinecke's avatar
Martin Reinecke committed
19

Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
20
21

def _shareRange(nwork, nshares, myshare):
Martin Reinecke's avatar
Martin Reinecke committed
22
23
    nbase = nwork//nshares
    additional = nwork % nshares
Martin Reinecke's avatar
Martin Reinecke committed
24
    lo = myshare*nbase + min(myshare, additional)
Martin Reinecke's avatar
Martin Reinecke committed
25
    hi = lo + nbase + int(myshare < additional)
Martin Reinecke's avatar
Martin Reinecke committed
26
27
    return lo, hi

28

Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
29
def local_shape(shape, distaxis):
Martin Reinecke's avatar
Martin Reinecke committed
30
    if len(shape) == 0 or distaxis == -1:
31
        return shape
Martin Reinecke's avatar
Martin Reinecke committed
32
33
    shape2 = list(shape)
    shape2[distaxis] = _shareSize(shape[distaxis], ntask, rank)
34
35
    return tuple(shape2)

Martin Reinecke's avatar
Martin Reinecke committed
36

37
38
class data_object(object):
    def __init__(self, shape, data, distaxis):
Martin Reinecke's avatar
Martin Reinecke committed
39
        self._shape = tuple(shape)
Martin Reinecke's avatar
Martin Reinecke committed
40
        if len(self._shape) == 0:
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
41
            distaxis = -1
42
43
44
        self._distaxis = distaxis
        self._data = data

Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
45
    def _sanity_checks(self):
46
        # check whether the distaxis is consistent
Martin Reinecke's avatar
Martin Reinecke committed
47
        if self._distaxis < -1 or self._distaxis >= len(self._shape):
48
            raise ValueError
Martin Reinecke's avatar
Martin Reinecke committed
49
50
51
52
        itmp = np.array(self._distaxis)
        otmp = np.empty(ntask, dtype=np.int)
        _comm.Allgather(itmp, otmp)
        if np.any(otmp != self._distaxis):
53
54
            raise ValueError
        # check whether the global shape is consistent
Martin Reinecke's avatar
Martin Reinecke committed
55
56
57
        itmp = np.array(self._shape)
        otmp = np.empty((ntask, len(self._shape)), dtype=np.int)
        _comm.Allgather(itmp, otmp)
58
        for i in range(ntask):
Martin Reinecke's avatar
Martin Reinecke committed
59
            if np.any(otmp[i, :] != self._shape):
60
61
                raise ValueError
        # check shape of local data
Martin Reinecke's avatar
Martin Reinecke committed
62
63
        if self._distaxis < 0:
            if self._data.shape != self._shape:
64
65
                raise ValueError
        else:
Martin Reinecke's avatar
Martin Reinecke committed
66
67
68
69
            itmp = np.array(self._shape)
            itmp[self._distaxis] = _shareSize(self._shape[self._distaxis],
                                              ntask, rank)
            if np.any(self._data.shape != itmp):
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
                raise ValueError

    @property
    def dtype(self):
        return self._data.dtype

    @property
    def shape(self):
        return self._shape

    @property
    def size(self):
        return np.prod(self._shape)

    @property
    def real(self):
Martin Reinecke's avatar
Martin Reinecke committed
86
        return data_object(self._shape, self._data.real, self._distaxis)
87
88
89

    @property
    def imag(self):
Martin Reinecke's avatar
Martin Reinecke committed
90
        return data_object(self._shape, self._data.imag, self._distaxis)
91

Martin Reinecke's avatar
Martin Reinecke committed
92
93
94
95
96
97
    def conj(self):
        return data_object(self._shape, self._data.conj(), self._distaxis)

    def conjugate(self):
        return data_object(self._shape, self._data.conjugate(), self._distaxis)

Martin Reinecke's avatar
Martin Reinecke committed
98
    def _contraction_helper(self, op, mpiop, axis):
99
        if axis is not None:
Martin Reinecke's avatar
Martin Reinecke committed
100
            if len(axis) == len(self._data.shape):
101
102
                axis = None
        if axis is None:
Martin Reinecke's avatar
Martin Reinecke committed
103
            res = np.array(getattr(self._data, op)())
Martin Reinecke's avatar
Martin Reinecke committed
104
            if (self._distaxis == -1):
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
105
                return res[()]
Martin Reinecke's avatar
Martin Reinecke committed
106
107
            res2 = np.empty((), dtype=res.dtype)
            _comm.Allreduce(res, res2, mpiop)
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
108
            return res2[()]
109
110

        if self._distaxis in axis:
Martin Reinecke's avatar
Martin Reinecke committed
111
112
            res = getattr(self._data, op)(axis=axis)
            res2 = np.empty_like(res)
Martin Reinecke's avatar
Martin Reinecke committed
113
            _comm.Allreduce(res, res2, mpiop)
Martin Reinecke's avatar
Martin Reinecke committed
114
            return from_global_data(res2, distaxis=0)
115
        else:
Martin Reinecke's avatar
Martin Reinecke committed
116
            # perform the contraction on the local data
Martin Reinecke's avatar
Martin Reinecke committed
117
118
            res = getattr(self._data, op)(axis=axis)
            if self._distaxis == -1:
Martin Reinecke's avatar
Martin Reinecke committed
119
                return from_global_data(res, distaxis=0)
Martin Reinecke's avatar
Martin Reinecke committed
120
            shp = list(res.shape)
Martin Reinecke's avatar
Martin Reinecke committed
121
            shift = 0
Martin Reinecke's avatar
Martin Reinecke committed
122
            for ax in axis:
Martin Reinecke's avatar
Martin Reinecke committed
123
124
                if ax < self._distaxis:
                    shift += 1
Martin Reinecke's avatar
Martin Reinecke committed
125
126
            shp[self._distaxis-shift] = self.shape[self._distaxis]
            return from_local_data(shp, res, self._distaxis-shift)
127
128
129

    def sum(self, axis=None):
        return self._contraction_helper("sum", MPI.SUM, axis)
Martin Reinecke's avatar
Martin Reinecke committed
130

Martin Reinecke's avatar
fixes    
Martin Reinecke committed
131
132
    def min(self, axis=None):
        return self._contraction_helper("min", MPI.MIN, axis)
Martin Reinecke's avatar
Martin Reinecke committed
133

Martin Reinecke's avatar
fixes    
Martin Reinecke committed
134
135
    def max(self, axis=None):
        return self._contraction_helper("max", MPI.MAX, axis)
136

Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
137
138
    def mean(self):
        return self.sum()/self.size
Martin Reinecke's avatar
Martin Reinecke committed
139

Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
140
141
    def std(self):
        return np.sqrt(self.var())
Martin Reinecke's avatar
Martin Reinecke committed
142

Martin Reinecke's avatar
Martin Reinecke committed
143
    # FIXME: to be improved!
Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
144
145
146
    def var(self):
        return (abs(self-self.mean())**2).mean()

147
    def _binary_helper(self, other, op):
Martin Reinecke's avatar
Martin Reinecke committed
148
        a = self
149
        if isinstance(other, data_object):
Martin Reinecke's avatar
Martin Reinecke committed
150
            b = other
151
152
153
154
            if a._shape != b._shape:
                raise ValueError("shapes are incompatible.")
            if a._distaxis != b._distaxis:
                raise ValueError("distributions are incompatible.")
Martin Reinecke's avatar
Martin Reinecke committed
155
156
            a = a._data
            b = b._data
157
        else:
Martin Reinecke's avatar
Martin Reinecke committed
158
            a = a._data
159
160
161
            b = other

        tval = getattr(a, op)(b)
Martin Reinecke's avatar
Martin Reinecke committed
162
163
164
165
        if tval is a:
            return self
        else:
            return data_object(self._shape, tval, self._distaxis)
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199

    def __add__(self, other):
        return self._binary_helper(other, op='__add__')

    def __radd__(self, other):
        return self._binary_helper(other, op='__radd__')

    def __iadd__(self, other):
        return self._binary_helper(other, op='__iadd__')

    def __sub__(self, other):
        return self._binary_helper(other, op='__sub__')

    def __rsub__(self, other):
        return self._binary_helper(other, op='__rsub__')

    def __isub__(self, other):
        return self._binary_helper(other, op='__isub__')

    def __mul__(self, other):
        return self._binary_helper(other, op='__mul__')

    def __rmul__(self, other):
        return self._binary_helper(other, op='__rmul__')

    def __imul__(self, other):
        return self._binary_helper(other, op='__imul__')

    def __div__(self, other):
        return self._binary_helper(other, op='__div__')

    def __rdiv__(self, other):
        return self._binary_helper(other, op='__rdiv__')

Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
200
201
202
    def __idiv__(self, other):
        return self._binary_helper(other, op='__idiv__')

203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
    def __truediv__(self, other):
        return self._binary_helper(other, op='__truediv__')

    def __rtruediv__(self, other):
        return self._binary_helper(other, op='__rtruediv__')

    def __pow__(self, other):
        return self._binary_helper(other, op='__pow__')

    def __rpow__(self, other):
        return self._binary_helper(other, op='__rpow__')

    def __ipow__(self, other):
        return self._binary_helper(other, op='__ipow__')

    def __eq__(self, other):
        return self._binary_helper(other, op='__eq__')

    def __ne__(self, other):
        return self._binary_helper(other, op='__ne__')

    def __neg__(self):
Martin Reinecke's avatar
Martin Reinecke committed
225
        return data_object(self._shape, -self._data, self._distaxis)
226
227

    def __abs__(self):
Martin Reinecke's avatar
Martin Reinecke committed
228
        return data_object(self._shape, np.abs(self._data), self._distaxis)
229
230

    def all(self):
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
231
        return self.sum() == self.size
232
233

    def any(self):
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
234
        return self.sum() != 0
235
236


Martin Reinecke's avatar
Martin Reinecke committed
237
def full(shape, fill_value, dtype=None, distaxis=0):
Martin Reinecke's avatar
Martin Reinecke committed
238
239
    return data_object(shape, np.full(local_shape(shape, distaxis),
                                      fill_value, dtype), distaxis)
240
241


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
242
def empty(shape, dtype=None, distaxis=0):
Martin Reinecke's avatar
Martin Reinecke committed
243
244
    return data_object(shape, np.empty(local_shape(shape, distaxis),
                                       dtype), distaxis)
245
246


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
247
def zeros(shape, dtype=None, distaxis=0):
Martin Reinecke's avatar
Martin Reinecke committed
248
249
    return data_object(shape, np.zeros(local_shape(shape, distaxis), dtype),
                       distaxis)
250
251


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
252
def ones(shape, dtype=None, distaxis=0):
Martin Reinecke's avatar
Martin Reinecke committed
253
254
    return data_object(shape, np.ones(local_shape(shape, distaxis), dtype),
                       distaxis)
255
256
257
258
259
260
261


def empty_like(a, dtype=None):
    return data_object(np.empty_like(a._data, dtype))


def vdot(a, b):
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
262
    tmp = np.array(np.vdot(a._data, b._data))
Martin Reinecke's avatar
Martin Reinecke committed
263
264
    res = np.empty((), dtype=tmp.dtype)
    _comm.Allreduce(tmp, res, MPI.SUM)
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
265
    return res[()]
266
267
268
269
270
271
272


def _math_helper(x, function, out):
    if out is not None:
        function(x._data, out=out._data)
        return out
    else:
Martin Reinecke's avatar
Martin Reinecke committed
273
        return data_object(x.shape, function(x._data), x._distaxis)
274
275
276
277
278
279
280
281
282
283
284
285
286
287


def abs(a, out=None):
    return _math_helper(a, np.abs, out)


def exp(a, out=None):
    return _math_helper(a, np.exp, out)


def log(a, out=None):
    return _math_helper(a, np.log, out)


Martin Reinecke's avatar
Martin Reinecke committed
288
289
290
291
def tanh(a, out=None):
    return _math_helper(a, np.tanh, out)


292
293
294
295
296
def sqrt(a, out=None):
    return _math_helper(a, np.sqrt, out)


def from_object(object, dtype=None, copy=True):
Martin Reinecke's avatar
Martin Reinecke committed
297
298
299
    return data_object(object._shape, np.array(object._data, dtype=dtype,
                                               copy=copy),
                       distaxis=object._distaxis)
300
301


Martin Reinecke's avatar
Martin Reinecke committed
302
303
# This function draws all random numbers on all tasks, to produce the same
# array independent on the number of tasks
Martin Reinecke's avatar
Martin Reinecke committed
304
305
306
# MR FIXME: depending on what is really wanted/needed (i.e. same result
# independent of number of tasks, performance etc.) we need to adjust the
# algorithm.
Martin Reinecke's avatar
Martin Reinecke committed
307
def from_random(random_type, shape, dtype=np.float64, **kwargs):
308
    generator_function = getattr(Random, random_type)
Martin Reinecke's avatar
Martin Reinecke committed
309
310
311
312
313
314
315
    for i in range(ntask):
        lshape = list(shape)
        lshape[0] = _shareSize(shape[0], ntask, i)
        ldat = generator_function(dtype=dtype, shape=lshape, **kwargs)
        if i == rank:
            outdat = ldat
    return from_local_data(shape, outdat, distaxis=0)
316

Martin Reinecke's avatar
Martin Reinecke committed
317

Martin Reinecke's avatar
Martin Reinecke committed
318
319
320
321
def local_data(arr):
    return arr._data


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
322
323
def ibegin(arr):
    res = [0] * arr._data.ndim
Martin Reinecke's avatar
Martin Reinecke committed
324
    res[arr._distaxis] = _shareRange(arr._shape[arr._distaxis], ntask, rank)[0]
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
325
    return tuple(res)
Martin Reinecke's avatar
Martin Reinecke committed
326
327


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
328
329
def np_allreduce_sum(arr):
    res = np.empty_like(arr)
Martin Reinecke's avatar
Martin Reinecke committed
330
    _comm.Allreduce(arr, res, MPI.SUM)
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
331
    return res
Martin Reinecke's avatar
Martin Reinecke committed
332
333
334
335
336
337


def distaxis(arr):
    return arr._distaxis


Martin Reinecke's avatar
Martin Reinecke committed
338
def from_local_data(shape, arr, distaxis):
Martin Reinecke's avatar
Martin Reinecke committed
339
340
341
    return data_object(shape, arr, distaxis)


Martin Reinecke's avatar
Martin Reinecke committed
342
343
def from_global_data(arr, distaxis=0):
    if distaxis == -1:
Martin Reinecke's avatar
Martin Reinecke committed
344
        return data_object(arr.shape, arr, distaxis)
Martin Reinecke's avatar
Martin Reinecke committed
345
    lo, hi = _shareRange(arr.shape[distaxis], ntask, rank)
Martin Reinecke's avatar
Martin Reinecke committed
346
    sl = [slice(None)]*len(arr.shape)
Martin Reinecke's avatar
Martin Reinecke committed
347
    sl[distaxis] = slice(lo, hi)
Martin Reinecke's avatar
Martin Reinecke committed
348
349
350
    return data_object(arr.shape, arr[sl], distaxis)


Martin Reinecke's avatar
Martin Reinecke committed
351
352
def to_global_data(arr):
    if arr._distaxis == -1:
Martin Reinecke's avatar
fixes    
Martin Reinecke committed
353
354
355
356
357
        return arr._data
    tmp = redistribute(arr, dist=-1)
    return tmp._data


Martin Reinecke's avatar
Martin Reinecke committed
358
def redistribute(arr, dist=None, nodist=None):
Martin Reinecke's avatar
Martin Reinecke committed
359
360
361
    if dist is not None:
        if nodist is not None:
            raise ValueError
Martin Reinecke's avatar
Martin Reinecke committed
362
        if dist == arr._distaxis:
Martin Reinecke's avatar
Martin Reinecke committed
363
364
365
366
367
368
            return arr
    else:
        if nodist is None:
            raise ValueError
        if arr._distaxis not in nodist:
            return arr
Martin Reinecke's avatar
Martin Reinecke committed
369
        dist = -1
Martin Reinecke's avatar
Martin Reinecke committed
370
371
        for i in range(len(arr.shape)):
            if i not in nodist:
Martin Reinecke's avatar
Martin Reinecke committed
372
                dist = i
Martin Reinecke's avatar
Martin Reinecke committed
373
                break
Martin Reinecke's avatar
Martin Reinecke committed
374

Martin Reinecke's avatar
Martin Reinecke committed
375
    if arr._distaxis == -1:  # all data available, just pick the proper subset
Martin Reinecke's avatar
Martin Reinecke committed
376
        return from_global_data(arr._data, dist)
Martin Reinecke's avatar
Martin Reinecke committed
377
    if dist == -1:  # gather all data on all tasks
Martin Reinecke's avatar
Martin Reinecke committed
378
        tmp = np.moveaxis(arr._data, arr._distaxis, 0)
Martin Reinecke's avatar
Martin Reinecke committed
379
380
        slabsize = np.prod(tmp.shape[1:])*tmp.itemsize
        sz = np.empty(ntask, dtype=np.int)
Martin Reinecke's avatar
Martin Reinecke committed
381
        for i in range(ntask):
Martin Reinecke's avatar
Martin Reinecke committed
382
383
384
385
            sz[i] = slabsize*_shareSize(arr.shape[arr._distaxis], ntask, i)
        disp = np.empty(ntask, dtype=np.int)
        disp[0] = 0
        disp[1:] = np.cumsum(sz[:-1])
Martin Reinecke's avatar
Martin Reinecke committed
386
        tmp = np.require(tmp, requirements="C")
Martin Reinecke's avatar
Martin Reinecke committed
387
388
        out = np.empty(arr.size, dtype=arr.dtype)
        _comm.Allgatherv(tmp, [out, sz, disp, MPI.BYTE])
Martin Reinecke's avatar
Martin Reinecke committed
389
390
391
392
        shp = np.array(arr._shape)
        shp[1:arr._distaxis+1] = shp[0:arr._distaxis]
        shp[0] = arr.shape[arr._distaxis]
        out = out.reshape(shp)
Martin Reinecke's avatar
Martin Reinecke committed
393
        out = np.moveaxis(out, 0, arr._distaxis)
Martin Reinecke's avatar
Martin Reinecke committed
394
        return from_global_data(out, distaxis=-1)
Martin Reinecke's avatar
Martin Reinecke committed
395

Martin Reinecke's avatar
Martin Reinecke committed
396
    # real redistribution via Alltoallv
Martin Reinecke's avatar
Martin Reinecke committed
397
    ssz0 = arr._data.size//arr.shape[dist]
Martin Reinecke's avatar
Martin Reinecke committed
398
    ssz = np.empty(ntask, dtype=np.int)
Martin Reinecke's avatar
Martin Reinecke committed
399
400
401
    rszall = arr.size//arr.shape[dist]*_shareSize(arr.shape[dist], ntask, rank)
    rbuf = np.empty(rszall, dtype=arr.dtype)
    rsz0 = rszall//arr.shape[arr._distaxis]
Martin Reinecke's avatar
Martin Reinecke committed
402
    rsz = np.empty(ntask, dtype=np.int)
Martin Reinecke's avatar
Martin Reinecke committed
403
404
405
406
407
408
409
410
411
412
413
414
    if dist == 0:  # shortcut possible
        sbuf = np.ascontiguousarray(arr._data)
        for i in range(ntask):
            lo, hi = _shareRange(arr.shape[dist], ntask, i)
            ssz[i] = ssz0*(hi-lo)
            rsz[i] = rsz0*_shareSize(arr.shape[arr._distaxis], ntask, i)
    else:
        sbuf = np.empty(arr._data.size, dtype=arr.dtype)
        sslice = [slice(None)]*arr._data.ndim
        ofs = 0
        for i in range(ntask):
            lo, hi = _shareRange(arr.shape[dist], ntask, i)
Martin Reinecke's avatar
Martin Reinecke committed
415
            sslice[dist] = slice(lo, hi)
Martin Reinecke's avatar
Martin Reinecke committed
416
417
418
419
420
421
            ssz[i] = ssz0*(hi-lo)
            sbuf[ofs:ofs+ssz[i]] = arr._data[sslice].flat
            ofs += ssz[i]
            rsz[i] = rsz0*_shareSize(arr.shape[arr._distaxis], ntask, i)
    ssz *= arr._data.itemsize
    rsz *= arr._data.itemsize
Martin Reinecke's avatar
Martin Reinecke committed
422
423
    sdisp = np.append(0, np.cumsum(ssz[:-1]))
    rdisp = np.append(0, np.cumsum(rsz[:-1]))
Martin Reinecke's avatar
Martin Reinecke committed
424
425
    s_msg = [sbuf, (ssz, sdisp), MPI.BYTE]
    r_msg = [rbuf, (rsz, rdisp), MPI.BYTE]
Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
426
    _comm.Alltoallv(s_msg, r_msg)
Martin Reinecke's avatar
Martin Reinecke committed
427
    del sbuf  # free memory
Martin Reinecke's avatar
Martin Reinecke committed
428
429
430
431
432
433
434
435
436
    if arr._distaxis == 0:
        rbuf = rbuf.reshape(local_shape(arr.shape, dist))
        arrnew = from_local_data(arr.shape, rbuf, distaxis=dist)
    else:
        arrnew = empty(arr.shape, dtype=arr.dtype, distaxis=dist)
        rslice = [slice(None)]*arr._data.ndim
        ofs = 0
        for i in range(ntask):
            lo, hi = _shareRange(arr.shape[arr._distaxis], ntask, i)
Martin Reinecke's avatar
Martin Reinecke committed
437
            rslice[arr._distaxis] = slice(lo, hi)
Martin Reinecke's avatar
Martin Reinecke committed
438
439
440
441
            sz = rsz[i]//arr._data.itemsize
            arrnew._data[rslice].flat = rbuf[ofs:ofs+sz]
            ofs += sz
    return arrnew
Martin Reinecke's avatar
Martin Reinecke committed
442
443


Martin Reinecke's avatar
Martin Reinecke committed
444
445
def transpose(arr):
    if len(arr.shape) != 2 or arr._distaxis != 0:
Martin Reinecke's avatar
Martin Reinecke committed
446
        raise ValueError("bad input")
Martin Reinecke's avatar
Martin Reinecke committed
447
448
449
450
451
452
453
454
455
456
457
    ssz0 = arr._data.size//arr.shape[1]
    ssz = np.empty(ntask, dtype=np.int)
    rszall = arr.size//arr.shape[1]*_shareSize(arr.shape[1], ntask, rank)
    rbuf = np.empty(rszall, dtype=arr.dtype)
    rsz0 = rszall//arr.shape[0]
    rsz = np.empty(ntask, dtype=np.int)
    sbuf = np.empty(arr._data.size, dtype=arr.dtype)
    ofs = 0
    for i in range(ntask):
        lo, hi = _shareRange(arr.shape[1], ntask, i)
        ssz[i] = ssz0*(hi-lo)
Martin Reinecke's avatar
Martin Reinecke committed
458
        sbuf[ofs:ofs+ssz[i]] = arr._data[:, lo:hi].flat
Martin Reinecke's avatar
Martin Reinecke committed
459
460
461
462
463
464
465
466
467
468
469
470
        ofs += ssz[i]
        rsz[i] = rsz0*_shareSize(arr.shape[0], ntask, i)
    ssz *= arr._data.itemsize
    rsz *= arr._data.itemsize
    sdisp = np.append(0, np.cumsum(ssz[:-1]))
    rdisp = np.append(0, np.cumsum(rsz[:-1]))
    s_msg = [sbuf, (ssz, sdisp), MPI.BYTE]
    r_msg = [rbuf, (rsz, rdisp), MPI.BYTE]
    _comm.Alltoallv(s_msg, r_msg)
    del sbuf  # free memory
    arrnew = empty((arr.shape[1], arr.shape[0]), dtype=arr.dtype, distaxis=0)
    ofs = 0
Martin Reinecke's avatar
Martin Reinecke committed
471
    sz2 = _shareSize(arr.shape[1], ntask, rank)
Martin Reinecke's avatar
Martin Reinecke committed
472
473
474
    for i in range(ntask):
        lo, hi = _shareRange(arr.shape[0], ntask, i)
        sz = rsz[i]//arr._data.itemsize
Martin Reinecke's avatar
Martin Reinecke committed
475
        arrnew._data[:, lo:hi] = rbuf[ofs:ofs+sz].reshape(hi-lo, sz2).T
Martin Reinecke's avatar
Martin Reinecke committed
476
477
478
479
        ofs += sz
    return arrnew


Martin Reinecke's avatar
Martin Reinecke committed
480
481
def default_distaxis():
    return 0