# Example parallel patterns

Parsl can be used to implement a wide range of parallel programming patterns, from bag of tasks through to nested workflows. Parsl implicitly assembles a dataflow dependency graph based on the data shared between apps. The flexibility of this model allows for the implementation of a wide range of parallel programming and workflow patterns.

Parsl is also designed to address broad execution requirements, from programs that run many short tasks to those that run a few long tasks.

Below we illustrate a range of parallel programming and workflow patterns. It is important to note that this set of examples is by no means comprehensive.

## Bag of Tasks

Parsl can be used to execute a large bag of tasks. In this case, Parsl assembles the set of tasks (represented as Parsl apps) and manages their concurrent execution on available resources.

    from parsl import python_app

    parsl.load()

    # Map function that returns double the input integer
    @python_app
    def app_random():
        import random
        return random.random()

    results =  []
    for i in range(0, 10):
        x = app_random()
        results.append(x)

    for r in results:
        print(r.result())

## Sequential workflows

Sequential workflows can be created by passing an AppFuture from one task to another. For example, in the following program the `generate` app (a Python app) generates a random number that is consumed by the `save` app (a Bash app), which writes it to a file. Because `save` cannot execute until it receives the `message` produced by `generate`, the two apps execute in sequence.

    from parsl import python_app

    parsl.load()

    # Generate a random number
    @python_app
    def generate(limit):
          from random import randint
          """Generate a random integer and return it"""
          return randint(1, limit)

    # Write a message to a file
    @bash_app
    def save(message, outputs=()):
          return 'echo {} &> {}'.format(message, outputs[0])

    message = generate(10)

    saved = save(message, outputs=['output.txt'])

    with open(saved.outputs[0].result(), 'r') as f:
          print(f.read())

## Parallel workflows

Parallel execution occurs automatically in Parsl, respecting dependencies among app executions. In the following example, three instances of the `wait_sleep_double` app are created. The first two execute concurrently, as they have no dependencies; the third must wait until the first two complete and thus the `doubled_x` and `doubled_y` futures have values. Note that this sequencing occurs even though `wait_sleep_double` does not in fact use its second and third arguments.

    from parsl import python_app

    parsl.load()

    @python_app
    def wait_sleep_double(x, foo_1, foo_2):
         import time
         time.sleep(2)   # Sleep for 2 seconds
         return x*2

    # Launch two apps, which will execute in parallel, since they do not have to
    # wait on any futures
    doubled_x = wait_sleep_double(10, None, None)
    doubled_y = wait_sleep_double(10, None, None)

    # The third app depends on the first two:
    #    doubled_x   doubled_y     (2 s)
    #           \     /
    #           doublex_z          (2 s)
    doubled_z = wait_sleep_double(10, doubled_x, doubled_y)

    # doubled_z will be done in ~4s
    print(doubled_z.result())

## Parallel workflows with loops

A common approach to executing Parsl apps in parallel is via loops. The following example uses a loop to create many random numbers in parallel.

    from parsl import python_app

    parsl.load()

    @python_app
    def generate(limit):
        """Generate a random integer and return it"""
        from random import randint
        return randint(1, limit)

    rand_nums = []
    for i in range(1,5):
        rand_nums.append(generate(i))

    # Wait for all apps to finish and collect the results
    outputs = [r.result() for r in rand_nums]

The `ParslPoolExecutor` simplifies this pattern using the same interface as Python’s native Executors.

    from parsl.concurrent import ParslPoolExecutor
    from parsl.configs.htex_local import config

    # NOTE: Functions used by the ParslPoolExecutor do _not_ use decorators
    def generate(limit):
        """Generate a random integer and return it"""
        from random import randint
        return randint(1, limit)

    with ParslPoolExecutor(config) as exec:
        outputs = pool.map(generate, range(1, 5))

In the preceding example, the execution of different tasks is coordinated by passing Python objects from producers to consumers. In other cases, it can be convenient to pass data in files, as in the following reformulation. Here, a set of files, each with a random number, is created by the `generate` app. These files are then concatenated into a single file, which is subsequently used to compute the sum of all numbers.

    from parsl import python_app, bash_app

    parsl.load()

    @bash_app
    def generate(outputs=()):
        return 'echo $(( RANDOM % (10 - 5 + 1 ) + 5 )) &> {}'.format(outputs[0])

    @bash_app
    def concat(inputs=(), outputs=(), stdout='stdout.txt', stderr='stderr.txt'):
        return 'cat {0} >> {1}'.format(' '.join(inputs), outputs[0])

    @python_app
    def total(inputs=()):
        total = 0
        with open(inputs[0].filepath, 'r') as f:
            for l in f:
                total += int(l)
        return total

    # Create 5 files with random numbers
    output_files = []
    for i in range (5):
         output_files.append(generate(outputs=['random-%s.txt' % i]))

    # Concatenate the files into a single file
    cc = concat(inputs=[i.outputs[0] for i in output_files], outputs=['all.txt'])

    # Calculate the average of the random numbers
    totals = total(inputs=[cc.outputs[0]])

    print(totals.result())

## MapReduce

MapReduce is a common pattern used in data analytics. It is composed of a map phase that filters values and a reduce phase that aggregates values. The following example demonstrates how Parsl can be used to specify a MapReduce computation in which the map phase doubles a set of input integers and the reduce phase computes the sum of those results.

    from parsl import python_app

    parsl.load()

    # Map function that returns double the input integer
    @python_app
    def app_double(x):
        return x*2

    # Reduce function that returns the sum of a list
    @python_app
    def app_sum(inputs=()):
        return sum(inputs)

    # Create a list of integers
    items = range(0,4)

    # Map phase: apply the double *app* function to each item in list
    mapped_results = []
    for i in items:
        x = app_double(i)
        mapped_results.append(x)

    # Reduce phase: apply the sum *app* function to the set of results
    total = app_sum(inputs=mapped_results)

    print(total.result())

The program first defines two Parsl apps, `app_double` and `app_sum`. It then makes calls to the `app_double` app with a set of input values. It then passes the results from `app_double` to the `app_sum` app to aggregate values into a single result. These tasks execute concurrently, synchronized by the `mapped_results` variable. The following figure shows the resulting task graph.

## Caching expensive initialisation between tasks

Many tasks in workflows require a expensive “initialization” steps that, once performed, can be used across successive invocations for that task. For example, you may want to reuse a machine learning model for multiple interface tasks and avoid loading it onto GPUs more than once.

This ExaWorks tutorial gives examples of how to do this.