Skip to content

Bodo Parallel APIs

bodo.allgatherv

bodo.allgatherv(data, warn_if_rep=True)


Gather data from all ranks and send to all, effectively replicating the data.

Arguments

  • data: data to gather.
  • warn_if_rep: prints a BodoWarning if data to gather is replicated.

Example Usage

import bodo
import pandas as pd

@bodo.jit
def mean_power():
    df = pd.read_parquet("data/cycling_dataset.pq")
    return bodo.allgatherv(df)

df = mean_power()
print(df)

Save code in test_allgatherv.py file and run with mpiexec.

mpiexec -n 4 python test_allgatherv.py

Output:

[stdout:0]
      Unnamed: 0    altitude  cadence  ...  power  speed                time
0              0  185.800003       51  ...     45  3.459 2016-10-20 22:01:26
1              1  185.800003       68  ...      0  3.710 2016-10-20 22:01:27
2              2  186.399994       38  ...     42  3.874 2016-10-20 22:01:28
3              3  186.800003       38  ...      5  4.135 2016-10-20 22:01:29
4              4  186.600006       38  ...      1  4.250 2016-10-20 22:01:30
...          ...         ...      ...  ...    ...    ...                 ...
3897        1127  178.199997        0  ...      0  3.497 2016-10-20 23:14:31
3898        1128  178.199997        0  ...      0  3.289 2016-10-20 23:14:32
3899        1129  178.199997        0  ...      0  2.969 2016-10-20 23:14:33
3900        1130  178.399994        0  ...      0  2.969 2016-10-20 23:14:34
3901        1131  178.399994        0  ...      0  2.853 2016-10-20 23:14:35

[3902 rows x 10 columns]
[stdout:1]
      Unnamed: 0    altitude  cadence  ...  power  speed                time
0              0  185.800003       51  ...     45  3.459 2016-10-20 22:01:26
1              1  185.800003       68  ...      0  3.710 2016-10-20 22:01:27
2              2  186.399994       38  ...     42  3.874 2016-10-20 22:01:28
3              3  186.800003       38  ...      5  4.135 2016-10-20 22:01:29
4              4  186.600006       38  ...      1  4.250 2016-10-20 22:01:30
...          ...         ...      ...  ...    ...    ...                 ...
3897        1127  178.199997        0  ...      0  3.497 2016-10-20 23:14:31
3898        1128  178.199997        0  ...      0  3.289 2016-10-20 23:14:32
3899        1129  178.199997        0  ...      0  2.969 2016-10-20 23:14:33
3900        1130  178.399994        0  ...      0  2.969 2016-10-20 23:14:34
3901        1131  178.399994        0  ...      0  2.853 2016-10-20 23:14:35

[3902 rows x 10 columns]
[stdout:2]
      Unnamed: 0    altitude  cadence  ...  power  speed                time
0              0  185.800003       51  ...     45  3.459 2016-10-20 22:01:26
1              1  185.800003       68  ...      0  3.710 2016-10-20 22:01:27
2              2  186.399994       38  ...     42  3.874 2016-10-20 22:01:28
3              3  186.800003       38  ...      5  4.135 2016-10-20 22:01:29
4              4  186.600006       38  ...      1  4.250 2016-10-20 22:01:30
...          ...         ...      ...  ...    ...    ...                 ...
3897        1127  178.199997        0  ...      0  3.497 2016-10-20 23:14:31
3898        1128  178.199997        0  ...      0  3.289 2016-10-20 23:14:32
3899        1129  178.199997        0  ...      0  2.969 2016-10-20 23:14:33
3900        1130  178.399994        0  ...      0  2.969 2016-10-20 23:14:34
3901        1131  178.399994        0  ...      0  2.853 2016-10-20 23:14:35

[3902 rows x 10 columns]
[stdout:3]
      Unnamed: 0    altitude  cadence  ...  power  speed                time
0              0  185.800003       51  ...     45  3.459 2016-10-20 22:01:26
1              1  185.800003       68  ...      0  3.710 2016-10-20 22:01:27
2              2  186.399994       38  ...     42  3.874 2016-10-20 22:01:28
3              3  186.800003       38  ...      5  4.135 2016-10-20 22:01:29
4              4  186.600006       38  ...      1  4.250 2016-10-20 22:01:30
...          ...         ...      ...  ...    ...    ...                 ...
3897        1127  178.199997        0  ...      0  3.497 2016-10-20 23:14:31
3898        1128  178.199997        0  ...      0  3.289 2016-10-20 23:14:32
3899        1129  178.199997        0  ...      0  2.969 2016-10-20 23:14:33
3900        1130  178.399994        0  ...      0  2.969 2016-10-20 23:14:34
3901        1131  178.399994        0  ...      0  2.853 2016-10-20 23:14:35

[3902 rows x 10 columns]

bodo.barrier

bodo.barrier()



Synchronize all processes. Block process from proceeding until all processes reach this point.

Example Usage

A typical example is to make sure all processes see side effects simultaneously. For example, a process can delete files from storage while others wait before writing to file:

import shutil, os
import numpy as np

# remove file if exists
if bodo.get_rank() == 0:
    if os.path.exists("data/data.pq"):
        shutil.rmtree("data/data.pq")

# make sure all processes are synchronized
# (e.g. all processes need to see effect of rank 0's work)
bodo.barrier()


@bodo.jit
def f(n):
    df = pd.DataFrame({"A": np.arange(n)})
    df.to_parquet("data/data.pq")


f(10)

The following figure illustrates what happens when processes call bodo.barrier(). When barrier is called, a process pauses and waits until all other processes have reached the barrier:

Process synchronization with Barrier

Danger

The example above shows that it is possible to have each process follow a different control flow, but all processes must always call the same Bodo functions in the same order.

bodo.gatherv

bodo.gatherv(data, allgather=False, warn_if_rep=True, root=0)



Collect distributed data manually by gathering them into a single rank.

Arguments

  • data: data to gather.
  • root: specify rank to collect the data. Default: rank 0.
  • warn_if_rep: prints a BodoWarning if data to gather is replicated.
  • allgather: send gathered data to all ranks. Default: False. Same behavior as bodo.allgatherv.

Example Usage

import bodo
import pandas as pd

@bodo.jit
def mean_power():
    df = pd.read_parquet("data/cycling_dataset.pq")
    return bodo.gatherv(df, root=1)

df = mean_power()
print(df)
Save code in test_gatherv.py file and run with mpiexec.

mpiexec -n 4 python test_gatherv.py

Output:

[stdout:1]
      Unnamed: 0    altitude  cadence  ...  power  speed                time
0              0  185.800003       51  ...     45  3.459 2016-10-20 22:01:26
1              1  185.800003       68  ...      0  3.710 2016-10-20 22:01:27
2              2  186.399994       38  ...     42  3.874 2016-10-20 22:01:28
3              3  186.800003       38  ...      5  4.135 2016-10-20 22:01:29
4              4  186.600006       38  ...      1  4.250 2016-10-20 22:01:30
...          ...         ...      ...  ...    ...    ...                 ...
3897        1127  178.199997        0  ...      0  3.497 2016-10-20 23:14:31
3898        1128  178.199997        0  ...      0  3.289 2016-10-20 23:14:32
3899        1129  178.199997        0  ...      0  2.969 2016-10-20 23:14:33
3900        1130  178.399994        0  ...      0  2.969 2016-10-20 23:14:34
3901        1131  178.399994        0  ...      0  2.853 2016-10-20 23:14:35

[3902 rows x 10 columns]
[stdout:0]
Empty DataFrame
Columns: [Unnamed: 0, altitude, cadence, distance, hr, latitude, longitude, power, speed, time]
Index: []

[0 rows x 10 columns]
[stdout:2]
Empty DataFrame
Columns: [Unnamed: 0, altitude, cadence, distance, hr, latitude, longitude, power, speed, time]
Index: []

[0 rows x 10 columns]
[stdout:3]
Empty DataFrame
Columns: [Unnamed: 0, altitude, cadence, distance, hr, latitude, longitude, power, speed, time]
Index: []

[0 rows x 10 columns]

bodo.get_rank

bodo.get_rank()


Get the process number from Bodo (called rank in MPI terminology).

Example Usage

Save following code in get_rank.py file and run with mpiexec.

import bodo
# some work only on rank 0
if bodo.get_rank() == 0:
    print("rank 0 done")

# some work on every process
print("rank", bodo.get_rank(), "here")
mpiexec -n 4 python get_rank.py

Output

rank 0 done
rank 0 here
rank 1 here
rank 2 here
rank 3 here

bodo.get_size

bodo.get_size()


Get the total number of processes.

Example Usage

Save following code in get_rank_size.py file and run with mpiexec.

import bodo
# some work only on rank 0
if bodo.get_rank() == 0:
    print("rank 0 done")

# some work on every process
print("rank", bodo.get_rank(), "here")
print("total ranks:", bodo.get_size())
mpiexec -n 4 python get_rank_size.py

Output

rank 0 done
rank 0 here
total ranks: 4
rank 1 here
total ranks: 4
rank 2 here
total ranks: 4
rank 3 here
total ranks: 4

bodo.random_shuffle

bodo.random_shuffle(data, seed=None, dests=None, parallel=False)


Manually shuffle data evenly across selected ranks.

Arguments

  • data: data to shuffle.
  • seed: number to initialze random number generator.
  • dests: selected ranks to distribute shuffled data to. By default, distribution includes all ranks.
  • parallel: flag to indicate whether data is distributed. Default: False. Inside JIT default value depends on Bodo's distribution analysis algorithm for the data passed (For more information, see Data Distribution section below).

Example Usage

import bodo
import pandas as pd

@bodo.jit
def test_random_shuffle():
    df = pd.DataFrame({"A": range(100)})
    return df

df = test_random_shuffle()
print(df.head())
df = bodo.random_shuffle(res, parallel=True)
print(df.head())

Save code in test_random_shuffle.py file and run with mpiexec.

mpiexec -n 4 python test_random_shuffle.py

Output:

[stdout:1]
    A
0  25
1  26
2  27
3  28
4  29
    A
19  19
10  10
17  42
9    9
17  17
[stdout:3]
    A
0  75
1  76
2  77
3  78
4  79
    A
6   31
0   25
24  49
22  22
5   30
[stdout:2]
    A
0  50
1  51
2  52
3  53
4  54
    A
11  36
24  24
15  65
14  14
10  35
[stdout:0]
    A
0  0
1  1
2  2
3  3
4  4
    A
4   29
18  18
8   58
15  15
3   28

bodo.rebalance

bodo.rebalance(data, dests=None, random=False, random_seed=None, parallel=False)


Manually redistribute data evenly across [selected] ranks.

Arguments

  • data: data to rebalance.
  • dests: selected ranks to distribute data to. By default, distribution includes all ranks.
  • random: flag to randomize order of the rows of the data. Default: False.
  • random_seed: number to initialize random number generator.
  • parallel: flag to indicate whether data is distributed. Default: False. Inside JIT default value depends on Bodo's distribution analysis algorithm for the data passed (For more information, see Data Distribution section below).

Example Usage

  • Example with just the parallel flag set to True:

    import bodo
    import pandas as pd
    
    @bodo.jit
    def mean_power():
        df = pd.read_parquet("data/cycling_dataset.pq")
        df = df.sort_values("power")[df["power"] > 400]
        return df
    
    df = mean_power()
    print(df.shape)
    df = bodo.rebalance(df, parallel=True)
    print("After rebalance: ", df.shape)
    

    Save code in test_rebalance.py file and run with mpiexec.

    mpiexec -n 4 python test_rebalance.py
    
    [stdout:0]
    (5, 10)
    After rebalance: (33, 10)
    [stdout:1]
    (18, 10)
    After rebalance: (33, 10)
    [stdout:2]
    (82, 10)
    After rebalance: (33, 10)
    [stdout:3]
    (26, 10)
    After rebalance: (32, 10)
    
  • Example to distribute the data from all ranks to subset of ranks using dests argument.

    import bodo
    import pandas as pd
    
    @bodo.jit
    def mean_power():
        df = pd.read_parquet("data/cycling_dataset.pq")
        df = df.sort_values("power")[df["power"] > 400]
        return df
    
    df = mean_power()
    print(df.shape)
    df = bodo.rebalance(df, dests=[1,3], parallel=True)
    print("After rebalance: ", df.shape)
    
    Save code in test_rebalance.py file and run with mpiexec.

    mpiexec -n 4 python test_rebalance.py
    

    Output:

    [stdout:0]
    (5, 10)
    After rebalance: (0, 10)
    [stdout:1]
    (18, 10)
    After rebalance: (66, 10)
    [stdout:2]
    (82, 10)
    After rebalance: (0, 10)
    [stdout:3]
    (26, 10)
    After rebalance: (65, 10)
    

bodo.scatterv

bodo.scatterv(data, warn_if_dist=True)



Distribute data manually by scattering data from one process to all processes.

Arguments

  • data: data to distribute.
  • warn_if_dist: flag to print a BodoWarning if data is already distributed.

Note

Currently, bodo.scatterv only supports scattering from rank 0.

Example Usage

  • When used outside of JIT code, we recommend that the argument be set to None for all ranks except rank 0. For example:
import bodo
import pandas as pd


@bodo.jit(distributed=["df"])
def mean_power(df):
    x = df.power.mean()
    return x

df = None
# only rank 0 reads the data
if bodo.get_rank() == 0:
    df = pd.read_parquet("data/cycling_dataset.pq")

df = bodo.scatterv(df)
res = mean_power(df)
print(res)

Save the code in test_scatterv.py file and run with mpiexec.

mpiexec -n 4 python test_scatterv.py

Output:

[stdout:0] 102.07842132239877
[stdout:1] 102.07842132239877
[stdout:2] 102.07842132239877
[stdout:3] 102.07842132239877

Note

data/cycling_dataset.pq is located in the Bodo tutorial repo.

  • This is not a strict requirement. However, since this might be bad practice in certain situations, Bodo will throw a warning if the data is not None on other ranks.
import bodo
import pandas as pd

df = pd.read_parquet("data/cycling_dataset.pq")
df = bodo.scatterv(df)
res = mean_power(df)
print(res)

Save code in test_scatterv.py file and run with mpiexec.

mpiexec -n 4 python test_scatterv.py

Output:

BodoWarning: bodo.scatterv(): A non-None value for 'data' was found on a rank other than the root. This data won't be sent to any other ranks and will be overwritten with data from rank 0.

[stdout:0] 102.07842132239877
[stdout:1] 102.07842132239877
[stdout:2] 102.07842132239877
[stdout:3] 102.07842132239877
  • When using scatterv inside of JIT code, the argument must have the same type on each rank due to Bodo's typing constraints. All inputs except for rank 0 are ignored.
import bodo
import pandas as pd

@bodo.jit()
def impl():
    if bodo.get_rank() == 0:
        df = pd.DataFrame({"A": [1,2,3,4,5,6,7,8]})
    else:
        df = pd.DataFrame({"A": [-1]*8})
    return bodo.scatterv(df)
print(impl())

Save code in test_scatterv.py file and run with mpiexec.

mpiexec -n 8 python test_scatterv.py

Output:

[stdout:6]
      A
6     7
[stdout:0]
      A
0     1
[stdout:1]
      A
1     2
[stdout:4]
      A
4     5
[stdout:7]
      A
7     8
[stdout:3]
      A
3     4
[stdout:2]
      A
2     3
[stdout:5]
      A
5     6

Note

scatterv, gatherv, allgatherv, rebalance, and random_shuffle work with all distributable data types. This includes:

  • All supported numpy array types.
  • All supported pandas array types (with the exception of Interval Arrays).
  • All supported pandas Series types.
  • All supported DataFrame types.
  • All supported Index types (with the exception of Interval Index).
  • Tuples of the above types.
Back to top