This section discusses parallelism topics that may be useful for performance tuning and advanced use cases.

## Getting/Setting Distributed Data Directly¶

Distributed data is usually accessed and modified through high-level Pandas and Numpy APIs. However, in many cases, Bodo allows direct access to distributed data without code modification. Here are such cases that Bodo currently supports:

1. Getting values using boolean array indexing, e.g. `B = A[A > 3]`. The output can be distributed, but may be imbalanced (`bodo.rebalance()` can be used if necessary).

2. Getting values using a slice, e.g. `B = A[::2]`. The output can be distributed, but may be imbalanced (`bodo.rebalance()` can be used if necessary).

3. Getting a value using a scalar index, e.g. `a = A[m]`. The output can be replicated.

4. Setting values using boolean array indexing, e.g. `A[A > 3] = a`. Only supports setting a scalar or lower-dimension value currently.

5. Setting values using a slice, e.g. `A[::2] = a`. Only supports setting a scalar or lower-dimension value currently.

6. Setting a value using a scalar index, e.g. `A[m] = a`.

## Concatenation Reduction¶

Some algorithms require generating variable-length output data per input data element. Bodo supports parallelizing this pattern, which we refer to as concatenation reduction. For example:

``````@bodo.jit
def impl(n):
df = pd.DataFrame()
for i in bodo.prange(n):
df = df.append(pd.DataFrame({"A": np.arange(i)}))

return df
``````

A common use case is simulation applications that generate possible outcomes based on parameters. For example:

``````@bodo.jit
def impl():
params = np.array([0.1, 0.2, 0.5, 1.0, 1.2, 1.5, ..., 100])
params = bodo.scatterv(params)
df = pd.DataFrame()
for i in bodo.prange(len(params)):
df = df.append(get_result(params[i]))

return df
``````

In this example, we chose to manually parallelize the parameter array for simplicity, since the workload is compute-heavy and the parameter data is relatively small.

Some computations such as `filter`, `join` or `groupby` can result in imbalanced data chunks across cores for distributed data. This may result in some cores operating on nearly empty dataframes, and others on relatively large ones.

Bodo provides `bodo.rebalance` to allow manual load balance if necessary. For example:

``````@bodo.jit(distributed={"df"})
def rebalance_example(df):
df = df[df["A"] > 3]
df = bodo.rebalance(df)
return df.sum()
``````

In this case, we use `bodo.rebalance` to make sure the filtered dataframe has near-equal data chunk sizes across cores, which would accelerate later computations (`sum` in this case).

We can also use the `dests` keyword to specify a subset of ranks to which bodo should distribute the data from all ranks.

Example usage:

``````@bodo.jit(distributed={"df"})
def rebalance_example(df):
df = df[df["A"] > 3]
df = bodo.rebalance(df, dests=[0, 1])
return df.sum()
``````

## Explicit Parallel Loops¶

Sometimes explicit parallel loops are required since a program cannot be written in terms of data-parallel operators easily. In this case, one can use Bodo's `prange` in place of `range` to specify that a loop can be parallelized. The user is required to make sure the loop does not have cross-iteration dependencies except for supported reductions. Currently, reductions using `+=`, `*=`, `min`, and `max` operators are supported. Iterations are simply divided between processes and executed in parallel, but reductions are handled using data exchange.

The example below demonstrates a parallel loop with a reduction:

``````import bodo
from bodo import prange
import numpy as np

@bodo.jit
def prange_test(n):
A = np.random.ranf(n)
s = 0
B = np.empty(n)
for i in prange(len(A)):
bodo.parallel_print("rank", bodo.get_rank())
# A[i]: distributed data access with loop index
# s: a supported sum reduction
s += A[i]
# write array with loop index
B[i] = 2 * A[i]
return s + B.sum()

res = prange_test(10)
print(res)
``````

Output:

``````[stdout:0]
rank 0
rank 0
rank 0
13.077183553245497
[stdout:1]
rank 1
rank 1
rank 1
13.077183553245497
[stdout:2]
rank 2
rank 2
13.077183553245497
[stdout:3]
rank 3
rank 3
13.077183553245497
``````

The user is also responsible for ensuring that control flow doesn't prevent the loop from being reduced. This can occur when operations are potentially applied unevenly or when the order the operation occurs in matters. This means that mixing reductions and control flow breaks such as `break` or `raise` are not supported.

The below example shows what happens when control flow prevents a reduction from being parallelized:

``````import bodo
from bodo import prange
import numpy as np

@bodo.jit
def prange_test(n):
A = np.random.ranf(n)
s = 0
for i in prange(len(A)):
if A[i] % 2 == 0:
s *= 2
else:
s += A[i]
return s

res = prange_test(10)
print(res)
``````

Output:

``````numba.core.errors.UnsupportedRewriteError: Failed in bodo mode pipeline (step: convert to parfors)
Reduction variable s has multiple conflicting reduction operators.
``````

## Integration with non-Bodo APIs¶

There are multiple methods for integration with APIs that Bodo does not support natively: 1. Switch to python object mode inside jit functions 2. Pass data in and out of jit functions

### Passing Distributed Data¶

Bodo can receive or return chunks of distributed data to allow flexible integration with any non-Bodo Python code. The following example passes chunks of data to interpolate with Scipy, and returns interpolation results back to jit function.

``````import scipy.interpolate

@bodo.jit(distributed=["X", "Y", "X2"])
def dist_pass_test(n):
X = np.arange(n)
Y = np.exp(-X/3.0)
X2 = np.arange(0, n, 0.5)
return X, Y, X2

X, Y, X2 = dist_pass_test(100)
# clip potential out-of-range values
X2 = np.minimum(np.maximum(X2, X[0]), X[-1])
f = scipy.interpolate.interp1d(X, Y)
Y2 = f(X2)

@bodo.jit(distributed={"Y2"})
def dist_pass_res(Y2):
return Y2.sum()

res = dist_pass_res(Y2)
print(res)
``````
``````[stdout:0] 6.555500504321469
[stdout:1] 6.555500504321469
[stdout:2] 6.555500504321469
[stdout:3] 6.555500504321469
``````

## Collections of Distributed Data¶

List and dictionary collections can be used to hold distributed data structures:

``````@bodo.jit(distributed=["df"])
def f():
to_concat = []
for i in range(10):
to_concat.append(pd.DataFrame({'A': np.arange(100), 'B': np.random.random(100)}))
df = pd.concat(to_concat)
return df

f()
``````

## Run code on a single rank¶

In cases where some code needs to be run on a single MPI rank, you can do so in a python script as follows:

``````if bodo.get_rank() == 0:
# Remove directory
import os, shutil
if os.path.exists("data/data.pq"):
shutil.rmtree("data/data.pq")

# To synchronize all ranks before proceeding
bodo.barrier()
``````

When running code on an IPyParallel cluster using the `%%px` magic, you can do this instead:

``````%%px --targets 0
# Install package
``````

An alias can be defined for convenience:

``````%alias_magic p0 px -p "--targets 0"
``````

This can be used as any other magic:

``````%%p0
# Install package
``````

## Run code once on each node¶

In cases where some code needs to be run once on each node in a multi-node cluster, such as a file system operation, installing packages, etc., it can be done as follows:

``````if bodo.get_rank() in bodo.get_nodes_first_ranks():
# Remove directory on all nodes
import os, shutil
if os.path.exists("data/data.pq"):
shutil.rmtree("data/data.pq")

# To synchronize all ranks before proceeding
bodo.barrier()
``````

The same can be done when running on an IPyParallel cluster using the `%%px` magic:

``````%%px
if bodo.get_rank() in bodo.get_nodes_first_ranks():
# Install package on all nodes