When an ordinary Python function is invoked in a Python program, the Python interpreter waits for the function to complete execution before proceeding to the next statement. But if a function is expected to execute for a long period of time, it may be preferable not to wait for its completion but instead to proceed immediately with executing subsequent statements. The function can then execute concurrently with that other computation.

Concurrency can be used to enhance performance when independent activities can execute on different cores or nodes in parallel. The following code fragment demonstrates this idea, showing that overall execution time may be reduced if the two function calls are executed concurrently.

v1 = expensive_function(1)
v2 = expensive_function(2)
result = v1 + v2

However, concurrency also introduces a need for synchronization. In the example, it is not possible to compute the sum of v1 and v2 until both function calls have completed. Synchronization provides a way of blocking execution of one activity (here, the statement result = v1 + v2) until other activities (here, the two calls to expensive_function()) have completed.

Parsl supports concurrency and synchronization as follows. Whenever a Parsl program calls a Parsl app (a function annotated with a Parsl app decorator, see Apps), Parsl will create a new task and immediately return a future in lieu of that function’s result(s). The program will then continue immediately to the next statement in the program. At some point, for example when the task’s dependencies are met and there is available computing capacity, Parsl will execute the task. Upon completion, Parsl will set the value of the future to contain the task’s output.

A future can be used to track the status of an asynchronous task. For example, after creation, the future may be interrogated to determine the task’s status (e.g., running, failed, completed), access results, and capture exceptions. Further, futures may be used for synchronization, enabling the calling Python program to block until the future has completed execution.

Parsl provides two types of futures: AppFuture and DataFuture. While related, they enable subtly different parallel patterns.


AppFutures are the basic building block upon which Parsl programs are built. Every invocation of a Parsl app returns an AppFuture that may be used to monitor and manage the task’s execution. AppFutures are inherited from Python’s concurrent library. They provide three key capabilities:

1. An AppFuture’s result() function can be used to wait for an app to complete, and then access any result(s). This function is blocking: it returns only when the app completes or fails. The following code fragment implements an example similar to the expensive_function() example above. Here, the sleep_double app simply doubles the input value. The program invokes the sleep_double app twice, and returns futures in place of results. The example shows how the future’s result() function can be used to wait for the results from the two sleep_double app invocations to be computed.

def sleep_double(x):
    import time
    time.sleep(2)   # Sleep for 2 seconds
    return x*2

# Start two concurrent sleep_double apps. doubled_x1 and doubled_x2 are AppFutures
doubled_x1 = sleep_double(10)
doubled_x2 = sleep_double(5)

# The result() function will block until each of the corresponding app calls have completed
print(doubled_x1.result() + doubled_x2.result())

2. An AppFuture’s done() function can be used to check the status of an app, without blocking. The following example shows that calling the future’s done() function will not stop execution of the main Python program.

def double(x):
    return x*2

# doubled_x is an AppFuture
doubled_x = double(10)

 # Check status of doubled_x, this will print True if the result is available, else False

3. An AppFuture provides a safe way to handle exceptions and errors while asynchronously executing apps. The example shows how exceptions can be captured in the same way as a standard Python program when calling the future’s result() function.

def bad_divide(x):
    return 6/x

# Call bad divide with 0, to cause a divide by zero exception
doubled_x = bad_divide(0)

# Catch and handle the exception.
except ZeroDivisionError as ze:
    print('Oops! You tried to divide by 0')
except Exception as e:
    print('Oops! Something really bad happened')

In addition to being able to capture exceptions raised by a specific app, Parsl also raises DependencyErrors when apps are unable to execute due to failures in prior dependent apps. That is, an app that is dependent upon the successful completion of another app will fail with a dependency error if any of the apps on which it depends fail.


While an AppFuture represents the execution of an asynchronous app, a DataFuture represents a file to be produced by that app. Parsl’s dataflow model requires such a construct so that it can determine when dependent apps, apps that that are to consume a file produced by another app, can start execution.

When calling an app that produces files as outputs, Parsl requires that a list of output files be specified (as a list of File objects passed in via the outputs keyword argument). Parsl will return a DataFuture for each output file as part AppFuture when the app is executed. These DataFutures are accessible in the AppFuture’s outputs attribute.

Each DataFuture will complete when the App has finished executing, and the corresponding file has been created (and if specified, staged out).

When a DataFuture is passed as an argument to a subsequent app invocation, that subsequent app will not begin execution until the DataFuture is completed. The input argument will then be replaced with an appropriate File object.

The following code snippet shows how DataFutures are used. In this example, the call to the echo Bash app specifies that the results should be written to an output file (“hello1.txt”). The main program inspects the status of the output file (via the future’s outputs attribute) and then blocks waiting for the file to be created (hello.outputs[0].result()).

# This app echoes the input string to the first file specified in the
# outputs list
def echo(message, outputs=[]):
    return 'echo {} &> {}'.format(message, outputs[0])

# Call echo specifying the output file
hello = echo('Hello World!', outputs=[File('hello1.txt')])

# The AppFuture's outputs attribute is a list of DataFutures

# Print the contents of the output DataFuture when complete
with open(hello.outputs[0].result().filepath, 'r') as f:


Adding .filepath is only needed on Python 3.5. With Python >= 3.6 the resulting file can be passed to open directly.