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Our team at HPE is seeking scientists who are using Python in their work, yet wishing for more speed or scalability from their computations. Specifically, we are interested in pair-programming to see whether we can help make your computations go faster and/or handle larger problem sizes.

Here are four quick questions to gauge whether such a collaboration might be of interest to you:

If you answered “yes” to these questions, we’d be interested in hearing from you, to see whether we can help you run at faster speeds or larger scales. To register your interest, see the link at the end of this article.

But first, let’s address some questions you might have:

Wait… who are you?

We are a team of computer scientists and software engineers at Hewlett Packard Enterprise with strong backgrounds in performance-oriented scientific computing. In addition to our day-to-day technical work, we are interested (and experienced) in helping scientists in diverse fields achieve greater performance and scalability in their computations.

Due to our personal interest in benefiting the planet, combined with HPE’s culture of seeking out creative ways to do so, we are particularly interested in working with computational scientists whose code has the potential to run faster, but who may not have the experience or background to achieve that on their own.

So, how can you make my computations faster?

Our team develops an open-source programming language named Chapel that is often compared favorably to Python in terms of the ease of reading and writing code. However, where native Python code has inherent barriers to performance and scalability, Chapel was designed with such concerns in mind from the outset.

We also contribute to a software framework called Arkouda, which permits Chapel computations to be invoked from Python code, optionally running in Jupyter notebooks. This means that while Chapel might be the means by which your computations run faster, you can still interact with them in a familiar setting.

Chapel and Arkouda focus on leveraging parallel hardware effectively. This can mean targeting the multicore processors and/or GPUs available on a laptop, a departmental cluster, a cloud instance, or the world’s largest supercomputers. In practice, some scientists have been happy reaping Chapel’s benefits on their laptops, while others have scaled to thousands or even millions of processors.

What are you proposing?

We understand that learning a new programming language or tuning for performance can be daunting, particularly when you’re trying to get your science done. And we also know that our team is unlikely to be able to learn enough about other scientific fields to make a meaningful contribution on our own. So we’re proposing meeting in the middle through a virtual [note: Or a small-group programming session if you're part of a team.].

You bring your knowledge of your field and computation, while we’ll bring our expertise in performance-oriented parallel computing and Chapel. We’ll use the session to hear about your computation, sketch out some code, introduce you to Chapel, and see how far we can get applying it to your computation.

If that initial session seems productive and promising, we can consider doing more. If it isn’t, at least we’ll each have learned a bit more about the other’s field, while likely gaining a new perspective on our own.

So… What’s in it for the Chapel team?

Beyond the potential to help scientists solve problems faster or larger than they might otherwise be able to, our motive is to grow the Chapel community. Larger user communities make new languages healthier and more successful. Our hope, of course, is that you love Chapel and become part of the community going forward. Yet even if that doesn’t happen, we’ll hopefully gain insights into how Chapel could improve and evolve to help users in your field in the future.

Have other groups benefited from Chapel?

We obviously think Chapel is great, and happily, other groups who’ve used it in their respective fields agree. The following comments are from scientists in the community describing Chapel’s impact on their work:

Chapel is fast. Parallelization is really easy! I didn’t know I could get so much from my desktop until I used Chapel.

— Nelson Luís Diaz
Professor of Environmental Engineering, Federal University of Paraná (Brazil)

With Chapel, I found myself seamlessly transitioning every potentially parallelizable aspect of my work from sequential to parallel execution. For me, who had worked with Spark and Hadoop before, discovering that implementing parallelism among CPUs and computers was as easy as defining a variable, was a big deal and so exciting.

— Marjan Asgari
Physical Scientist at Natural Resources Canada
(on her experience using Chapel as a PhD student at the University of Guelph)

I have now written three major programs for my work using Chapel, and each time I was able to significantly increase performance and achieve excellent parallelism with a low barrier to entry. Chapel is my go-to language if I need to stand up a highly performant software stack quickly, and I have truly enjoyed working with members of the Chapel team if the technical requirements of my work went beyond what I could program on my own.

— Scott Bachman
Scientist II, National Center for Atmospheric Research
Technical Modeling Lead, [C]Worthy, LLC

Chapel allowed us to train highly qualified personnel on the development of an efficient parallel fluid dynamics simulation software in a short time without the hurdle of conventional parallel programming languages.

— Simon Bourgault-Côté
(on his use of Chapel as a Research Associate, Polytechnique Montreal)

Sounds great, what’s next?

If this article’s proposal intrigues you, let us know about your project and interest using this online form. As responses come in, we will set up virtual meetings with as many users as we’re able to.

In the meantime, if you’d like to learn more about Chapel, please see:

Finally, if you know of colleagues or other groups that you think would benefit from Chapel, please share this post with them as well!