OpenMP 4.0 Specifications Released

The OpenMP 4.0 API Specification is released with Significant New Standard Features

The OpenMP 4.0 API supports the programming of accelerators, SIMD programming, and better optimization using thread affinity

The OpenMP Consortium has released OpenMP API 4.0, a major upgrade of the OpenMP API standard language specifications. Besides several major enhancements, this release provides a new mechanism to describe regions of code where data and/or computation should be moved to another computing device.

Bronis R. de Supinski, Chair of the OpenMP Language Committee, stated that “OpenMP 4.0 API is a major advance that adds two new forms of parallelism in the form of device constructs and SIMD constructs. It also includes several significant extensions for the loop-based and task-based forms of parallelism already supported in the OpenMP 3.1 API.

The 4.0 specification is now available on the 

Standard for parallel programming extends its reach

With this release, the OpenMP API specifications, the de-facto standard for parallel programming on shared memory systems, continues to extend its reach beyond pure HPC to include DSPs, real time systems, and accelerators. The OpenMP API aims to provide high-level parallel language support for a wide range of applications, from automotive and aeronautics to biotech, automation, robotics and financial analysis.

New features in the OpenMP 4.0 API include:

· Support for accelerators. The OpenMP 4.0 API specification effort included significant participation by all the major vendors in order to support a wide variety of compute devices. OpenMP API provides mechanisms to describe regions of code where data and/or computation should be moved to another computing device. Several prototypes for the accelerator proposal have already been implemented.

· SIMD constructs to vectorize both serial as well as parallelized loops. With the advent of SIMD units in all major processor chips, portable support for accessing them is essential. OpenMP 4.0 API provides mechanisms to describe when multiple iterations of the loop can be executed concurrently using SIMD instructions and to describe how to create versions of functions that can be invoked across SIMD lanes.

· Error handling. OpenMP 4.0 API defines error handling capabilities to improve the resiliency and stability of OpenMP applications in the presence of system-level, runtime-level, and user-defined errors. Features to abort parallel OpenMP execution cleanly have been defined, based on conditional cancellation and user-defined cancellation points.

· Thread affinity. OpenMP 4.0 API provides mechanisms to define where to execute OpenMP threads. Platform-specific data and algorithm-specific properties are separated, offering a deterministic behavior and simplicity in use. The advantages for the user are better locality, less false sharing and more memory bandwidth.

· Tasking extensions. OpenMP 4.0 API provides several extensions to its task-based parallelism support. Tasks can be grouped to support deep task synchronization and task groups can be aborted to reflect completion of cooperative tasking activities such as search. Task-to-task synchronization is now supported through the specification of task dependency.

· Support for Fortran 2003. The Fortran 2003 standard adds many modern computer language features. Having these features in the specification allows users to parallelize Fortran 2003 compliant programs. This includes interoperability of Fortran and C, which is one of the most popular features in Fortran 2003.

· User-defined reductions. Previously, OpenMP API only supported reductions with base language operators and intrinsic procedures. With OpenMP 4.0 API, user-defined reductions are now also supported.

· Sequentially consistent atomics. A clause has been added to allow a programmer to enforce sequential consistency when a specific storage location is accessed atomically.

This represents collaborative work by many of the brightest in industry, research, and academia, building on the consensus of 26 members. We strive to deliver high-level parallelism that is portable across 3 widely-implemented common General Purpose languages, productive for HPC and consumers, and delivers highly competitive performance. I want to congratulate all the members for coming together to create such a momentous advancement in parallel programming, under such tight constraints and industry challenges.
With this release, the OpenMP API will move immediately forward to the next release to bring even more usable parallelism to everyone.
 – Michael Wong, CEO OpenMP ARB.

Hunger Games themed semi-iterated prisoner’s dilemma tournament

With all the talk surrounding it, crowdsourcing science might seem like a new concept and it might be true for citizen science efforts, but it is definitely an old trick to source your research to other researchers. In fact, evolutionary game theory was born (or at least popularized) by one such crowdsourcing exercise; in 1980, Robert Axelrod wanted to find out the best strategy for iterated prisoner’s dilemma and reached out to prominent researchers for strategy submissions to around-robin tournmanet. Tit-for-tat was the winning strategy, but the real victor was Axelrod. His 1981 paper with Hamilton analyzing the result went on to become a standard reference in applications of game theory to social questions (at least outside of economics), agent-based modeling, and — of course — evolutionary game theory. Of Axelrod’s sizeable 47,222 (at time of writing) citations, almost half (23,370) come from this single paper. The tradition of tournaments continues among researchers, I’ve even discussed an imitation tournament on imitation previously.

The cynical moral of the tale: if you want to be noticed then run a game theory tournament. The folks at Brilliant.org— a website offering weekly olympiad-style challange problems in math and physics — took this message to heart, coupled it to the tried-and-true marketing technique of linking to a popular movie/book franchise, and decided to run a Hunger Games themed semi-iterated Prisoner’s dillema tournament. Submit a quick explanation of your strategy and Python script to play the game, and you could be one of the 5 winners of the $1,000 grand prize. Hooray! The submission deadline is August 18th, 2013 and all you need is a Brilliant account and it seems that these are free. If you are a reader of TheEGG blog then I recommend submitting a strategy, and discussing it in the comments (either before or after the deadline); I am interested to see what you come up with.

I will present the rules in m

Why designed a front-end programming language from scratch

Today’s programming languages have traditionally been created by the tech giants. These languages are made up of millions of lines of code, so the tech giants only invest in incremental, non-breaking changes that address their business concerns. This is why innovation in popular languages like C, Java, and JavaScript is depressingly slow.

Open-source languages like Python and Ruby gained widespread industrial use by solving backend problems at startup scale. Without the constraints of legacy code and committee politics, language designers are free to explore meaningful language innovation. And with compile-to-VM languages, it has become cheap enough for individuals and startups to create the future of programming languages themselves.

Open-source language innovation has not yet disrupted front-end programming. We still use the same object-oriented model that took over the industry in the 1980s. The tech giants are heavily committed to this approach, but open-source has made it possible to pursue drastically different methods.

Two years ago, I began to rethink front-end programming from scratch. I quickly found myself refining a then-obscure academic idea called Functional Reactive Programming. This developed into Elm, a language that compiles to JavaScript and makes it much easier to create highly interactive programs.

Since the advent of Elm, a lively and friendly community has sprung up, made up of everyone from professional developers to academics to beginners who have never tried functional programming before. This diversity of voices and experiences has been a huge help in guiding Elm towards viability as a production-ready language.

The community has already created a bunch of high quality contributions that are shaping the future of Elm and are aiming to shape the future of front-end programming.

Dev tools

Early on, I made it a priority to let people write, compile, and use Elm programs directly from their browser. No install, no downloads. This interactive editor made it easy for beginners and experts alike to learn Elm and start using it immediately.

In-browser compilation triggered lots of discussion, ideas, and ultimately contributions. Mads Flensted-Urech added in-line documentation for all standard libraries. Put your cursor over a function, and you get the type, prose explanation, and link to the library it comes from. Laszlo Pandy took charge of debugging tools. He is focusing on visualizing the state of an Elm program as time passes, even going so far as pausing, rewinding, and replaying events.

Runtime

I designed Elm to work nicely with concurrency. Unfortunately, JavaScript’s concurrency support is quite poor with questionable prospects for improvement. I decided to save the apparent implementation quagmire for later, but John P. Mayer decided to make it happen. He now has a version of the runtime that can automatically multiplex tasks across many threads, all implemented in JavaScript.

Common to all of these cases are driven individuals who knew they could do it better. This is how Elm got started and how it caught the attention of Prezi, a company also not content to accept JavaScript as the one and only answer for front-end development. I have since joined the company for the express purpose of furthering work on Elm.

We do not need to sit and hope that the tech giants will someday do an okay job. We can create the future of front-end programming ourselves, and we can do it now.