Bodo Parallelism Basics¶
This page discusses Bodo's JIT compilation workflow and the parallelism model and APIs provided by Bodo.
JIT (Just-in-time) Compilation Workflow¶
Bodo provides a just-in-time (JIT) compilation workflow using the
@bodo.jit decorator, which replaces a Python function with a so-called
Dispatcher object. Bodo compiles the function the first time a
Dispatcher object is called and reuses the compiled version afterwards.
The function is recompiled only if the same function is called with
different argument types (not often in practice). All of this is
completely transparent to the caller, and does not affect any Python
code calling the function.
>>> import numpy as np
>>> import pandas as pd
>>> import bodo
>>> @bodo.jit
... def f(n, a):
... df = pd.DataFrame({"A": np.arange(n) + a})
... return df.head(3)
...
>>> print(f)
CPUDispatcher(<function f at 0x100bec310>)
>>> print(f(8, 1)) # compiles for (int, int) input types
A
0 1
1 2
2 3
>>> print(f(8, 2)) # same input types, no need to compile
A
0 2
1 3
2 4
>>> print(f(8, 2.2)) # compiles for (int, float) input types
A
0 2.2
1 3.2
2 4.2
Note
In many cases, the binary that Bodo generates when compiling a function can be saved to disk and reused across program executions. See caching for more information.
Parallel Execution Model¶
As we saw in the "Getting Started" tutorial, Bodo transforms functions
for parallel execution. Bodo uses Message Passing Interface
(MPI) that
follows Single Program Multiple Data
(SPMD) paradigm. In this model,
the dispatcher does not launch processes or threads on the fly. Instead,
all processes are launched at the beginning and run the same file using
mpiexec command.
Bodo parallelizes functions with the bodo.jit decorator by
distributing the data across the processes. Each rank runs the same code
on a chunk of the data, and Bodo automatically communicates the data
between the ranks (as needed).
For example, save the following code in atest_bodo.py and use
mpiexec to launch 4 processes as follows:
import numpy as np
import pandas as pd
import bodo
@bodo.jit
def f(n, a):
df = pd.DataFrame({"A": np.arange(n) + a})
return df
print(f(8, 1))
Output:
In this example, mpiexec launches 4 Python processes, each
executing the same test_bodo.py file. Since the function f is
decorated with bodo.jit and Bodo can parallelize it, each
process generates a chunk of the data in np.arange.
Note how the prints, which are regular Python code executed outside of Bodo, run for each process.
Warning
- Python codes outside of Bodo functions execute sequentially on every process.
- Bodo functions run in parallel assuming that Bodo is able to parallelize them. Otherwise, Bodo prints the following warning and runs sequentially on every process.
On Jupyter notebook, parallel execution happens in very much the same
way. We start a set of MPI engines through ipyparallel and activate a
client. See how to use bodo with jupyter notebooks for more information
and examples.
See Also
Data Distribution¶
Bodo parallelizes computation by dividing data into separate chunks across processes. However, some data handled by a Bodo function may not be divided into chunks. There are are two main data distribution schemes:
- Replicated (REP): the data associated with the variable is the same on every process.
- One-dimensional (1D): the data is divided into chunks, split along one dimension (rows of a dataframe or first dimension of an array).
Bodo determines distribution of variables automatically, using the nature of the computation that produces them. Let's see an example:
import bodo
import pandas as pd
@bodo.jit
def mean_power_speed():
df = pd.read_parquet("data/cycling_dataset.pq")
m = df[["power", "speed"]].mean()
return m
res = mean_power_speed()
print(res)
Save code in mean_power_speed.py and run it with mpiexec as follows:
[stdout:0]
power 102.078421
speed 5.656851
dtype: float64
[stdout:1]
power 102.078421
speed 5.656851
dtype: float64
[stdout:2]
power 102.078421
speed 5.656851
dtype: float64
[stdout:3]
power 102.078421
speed 5.656851
dtype: float64
In this example, df is parallelized (each process reads a different
chunk) but m is replicated, even though it is a Series. Semantically,
it makes sense for the output of mean operation to be replicated on
all processors, since it is a reduction and produces "small" data.
Distributed Diagnostics¶
The distributions found by Bodo can be printed either by setting the
environment variable BODO_DISTRIBUTED_DIAGNOSTICS=1 or calling
distributed_diagnostics() on the compiled function. Let's examine the
previous example's distributions by adding following line to
mean_power_speed script:
Distributed analysis replicated return variable $30return_value.12. Set distributed flag for the original variable if distributed partitions should be returned.
[stdout:0]
python mean_power_speed.py
power 102.078421
speed 5.656851
dtype: float64
Distributed diagnostics for function mean_power_speed, /Users/mean_power_speed.py (3)
Data distributions:
pq_table.0 1D_Block
pq_index.1 1D_Block
data_74 REP
Parfor distributions:
0 1D_Block
1 1D_Block
Distributed listing for function mean_power_speed, /Users/hadia/Bodo/testing/mean_power_speed.py (3)
---------------------------------------------------------------------| parfor_id/variable: distribution
@bodo.jit |
def mean_power_speed(): |
df = pd.read_parquet("Bodo-tutorial/data/cycling_dataset.pq")----| pq_table.0: 1D_Block, pq_index.1: 1D_Block
m = df[["power", "speed"]].mean()--------------------------------| #0: 1D_Block, #1: 1D_Block, data_74: REP
return m |
Setting distribution of variable 'impl_v48_data_74' to REP: output of np.asarray() call on non-array is REP
Bodo compiler optimizations rename the variables.
The output shows that power and speed columns of df are distributed (1D_Block), but m is replicated (REP).
This is because df is the output from read_parquet and input to mean, both of which can be distributed by Bodo.
m is the output from mean, which is replicated (available on every process).