Introduction to Dragon

Wondering where to start with Dragon ? This page is for you. We’ll walk through the select patterns and abstractions and have you quickly programming at scale.

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Python multiprocessing

Dragon was initially designed to allow developers to program to the standard Python multiprocessing API and scale their application to an entire supercomputer. The team took what they learned developing high-performance, scalable software for Cray, Inc. and applied it to the standard API for parallel Python. We won’t duplicate multiprocessing documentation here, but it is worth reviewing some of the key interfaces and their typical use cases.

Pool

The multiprocessing documentation introduces the API with this basic example using Pool.map().

from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    with Pool(5) as p:
        print(p.map(f, [1, 2, 3]))

This code starts up an additional 5 processes and executes the function f() across each item in the list [1,2,3]. When your application has a pattern like this, Pool.map() is a great tool for getting true parallel performance as each process is its own interpreter and GIL. This is especially important when the list of items is very large. The Dragon version of this code looks like the following:

import dragon
from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    mp.set_start_method("dragon")
    with Pool(5) as p:
        print(p.map(f, [1, 2, 3]))

We needed to add a single import dragon and tell multiprocessing to use the dragon start method. That’s it. If this code then called libraries underneath that also use multiprocessing, those two steps enables Dragon for the libraries as well. But what do I get from this? The true power of Dragon comes when you have a very large working set and can scale across an entire cluster. As of the Dragon v0.9 release, we regularily test mp.Pool() with over 50,000 workers on hundreds of nodes on a Cray EX supercomputer.

import dragon
from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    mp.set_start_method("dragon")
    with Pool(50000) as p:
        print(p.map(f, range(50000))

You can also manage multiple mp.Pool() instances at once and have them come and go at different times. This is great for use-cases where the nature of computation changes over time. For example, imagine we had to process two types of files, type A and type B. Let’s say type A takes twice as much time to process a single file as type B. One approach to balance the processing could look like this:

import dragon
from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    mp.set_start_method("dragon")

    typeAfiles = # some long list
    typeBfiles = # some other long list

    poola = Pool(2000)
    poolb = Pool(1000)

    resultsa = poola.map_async(f, typeAfiles)
    resultsb = poolb.map_async(f, typeBfiles)
    for result in resultsa.get()
        # do something
    for result in resultsb.get()
        # do something

    poola.close()
    poolb.close()
    poola.join()
    poolb.join()

Since we can manage the life-cycle of mp.Pool() explicitly, we can have them close and bring up new ones as our computational load changes. For Dragon users, they can start to view their set of nodes as a single collection of resources and program different elements of their application to use different amounts of resources over time. It’s kind of cloud-like.

In addition to scaling mp.Pool() to supercomputer scales, Dragon also lets users do something base multiprocessing doesn’t let you do. You can nest mp.Pool() inside of one another. Pools that use Pool?! Why might you want that? There are a lot of use-cases for this. Imagine your use-case is to process different types of data as they land in a filesystem. Imagine that each file has many components that themselves require Pool.map()-like operations. Something like this:

import dragon
from multiprocessing import Pool

def proc_data(d):
    # do some work

def f(workfile):
    with open(workfile, "rb") as f:
        data = f.read()
        with Pool(128) as p:
            results = p.map(proc_data, list(data))
    return results

if __name__ == '__main__':
    mp.set_start_method("dragon")

    files = # some long list
    with Pool(128) as p:
        all_results = p.map(f, files)

There is much more you can do with mp.Pool yet, especially when an entire supercomputer’s resources are at your command. The key thing is that with Dragon’s implementation you can scale out, get great performance on the internal communication that happens in mp.Pool(), and it integrates with the rest of the Python ecosystem as it should.

Queue

The other interface we typically highlight from multiprocessing is mp.Queue . We often use it any time there are multiple readers and/or writers needing to communicate. It’s a FIFO-style queue, and with Dragon’s implementation, processes can transparently access it from any node in a supercomputer the Dragon runtime is deployed to. mp.Queue is used internally in mp.Pool for both the input of items to process and the results that come back to the calling process. Since we test Dragon’s mp.Pool implementation on hundreds of nodes, we know our mp.Queue scales well. We do have designs for even better scaling, but that’s for a different document. Here’s how to use mp.Queue in combination with another idiom from multiprocessing called mp.Process.

import dragon
from multiprocessing import Process, Queue

def compute_it(f):
    # do something

def work(f, resultq):
    resultq.put(compute_it(f))

 if __name__ == '__main__':
    mp.set_start_method("dragon")

    q = Queue()
    somedata = # some data
    p = Process(target=work, args=(somedata, q,))
    p.start()

    result = q.get()
    p.join()

mp.Queue is a “pickleable” object, which means you can pass it as an argument to an entirely different Python process, as done in this example. The same is true for all the other communication and collective primitives in multiprocessing. Dragon’s implementation relies on our high-performance (Shared memory+RDMA-capable) communication layer, called Channels .

Data

The Python dict is one of the most fundamental and useful abstractions in the language, in our opinon. What if we had a dict that scaled to hundreds or thousands of nodes and could be accessed by thousands of processes at the same time? Dragon has this feature. With the Dragon distributed dict, DDict , you can easily manage data exchange at-scale between process with great performance. Like everything communication related in Dragon, it uses our Channels layer for high-performance communication. It behaves with the same semantics as the normal dict and how they are accessed from multiple threads at the same time. The only difference with the DDict is it works across multiple processes.

Using the DDict looks like the following:

import dragon
from multiprocessing import Pool
from dragon.data.ddict import DDict

dist_dict = None  # this is scope only in the current process, not across processes
                  # this lets us access the variable across functions below using "global"
                  # there are other ways to do this, but this one is pretty short

def setup(the_ddict):
    global dist_dict
    dist_dict = the_ddict

def assign(x):
    global dist_dict
    key = # some object, like a string or int
    dist_dict[key] = x

if __name__ == '__main__':
    mp.set_start_method("dragon")

    dist_dict = DDict(managers_per_node=1, num_nodes=1, total_mem=1024**3)

    with Pool(5, initializer=setup, initargs=(dist_dict,)) as p:
        print(p.map(assign, [1, 2, 3]))

    for k in dist_dict.keys():
        print(f"{k} = {dist_dict[k]}", flush=True)

You can start to think of the DDict almost like a co-located object store that scales with your application. For example, you might read in a large quanitity of data from a filesystem and store them into the DDict with keys mimicing file paths. If you don’t have a great parallel filesystem, this lets you read the data once, cache it in the memory of your nodes, and leverage your network’s performance (and shared memory) for subsequent accesses. You can use it instead of storing intermediate results to a filesystem. If your workload consists of stages of Python processes in a pipeline, DDict is a very convenient way to manage data exchange without any system-specific code, such as file paths.

How well does DDict perform though? We improve Dragon performance with each release, but this is where we are at with Dragon v0.10. With this gups_ddict.py, inspired by the classic GUPS benchmark, some large number of processes will write a unique set of key/value pairs into the DDict. The keys are always 128 bytes in size, but the values vary in length per outer loop of the benchmark. The plot below shows the aggregate bandwidth measured across the clients for writing key/value pairs with a DDict sharded across 128 nodes on a Cray EX system. The equivalent benchmark written for something like SHMEM may be faster, but for the largest value sizes DDict is approaching 30% of the achievable bandwidth. We have seen high-end filesystems do worse. We should note, the DDict is not persistent between executions of Dragon. We are working on that feature.

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