Sun’s Fortress Language: Parallelism by Default

By Michael Feldman

July 16, 2008

If anyone knows how to introduce a new programming language, it’s Sun Microsystems. The company’s highly successful Java language, which was introduced in 1991, has become ubiquitous in network-centric and embedded computing. Today, there’s a whole research team at Sun Labs devoted to programming languages, and the big project there in recent years has been the development of the Fortress programming language. The end game is to “do for Fortran what Java did for C.”

Unlike Java though, Fortress is geared for HPC applications, with programmability as a major design goal. The language maintains a high level of abstraction for the developer, allowing the focus to be on the algorithm rather than the underlying hardware. And even though Fortress specifically targets high-end technical computing, it is also applicable to large-scale parallel applications of almost any type. “We were looking for a language that was good for multicore, for supercomputing, and for everything in between,” explains Eric Allen, principal investigator of the programming languages research group at Sun Labs.

The project began in 2003 and was originally funded out of DARPA’s High Productivity Computing System (HPCS) program. When Sun was dropped from HPCS in Phase III of the program, Sun Labs took over the Fortress R&D completely. But since Sun has made Fortress an open source project, the company has received a lot of outside help from universities and other researchers that have contributed to the design and implementation of the language. The University of Tokyo, the University of Virginia, and University of Aarhus in Denmark are all developing new Fortress libraries, while Rice University has been working on compiler optimizations.

Although the basic foundation is now fairly stable, the language specification is not written in stone. Version 1.0 of the compiler and runtime was launched in April of this year and represents a prototype for users who would like to kick the tires and offer some feedback. According to Allen, Sun is updating the spec as new features are added or current ones are refined and is incorporating the changes into the language as appropriate. The intention is to release new distributions every few months. Allen says a production version of the compiler is expected in 2010, or thereabouts.

The current prototype runs on top of a standard Java Virtual Machine (JVM), so just about anyone with a computer can give Fortress a whirl. Sun offers the latest distribution free on their Project Fortress site. For performance reasons, Allen expects that at some point more of the runtime will be statically compiled rather than interpreted, but right now the convenience of the JVM is enabling widespread experimentation. He says they’ve already received a lot of good suggestions, especially from the academic community.

Allen himself teaches a programming course using Fortress at UT Austin. According to him, the kids there are enthusiastic about writing code with it and are amazed at how concise Fortress programs are compared to other languages they’ve used.

The language itself supports both task and data parallelism. Most of the constructs assume concurrency unless the programmer explicitly specifies sequential execution. So parallel computation is automatically performed underneath the covers as a result of standard source code execution (assuming the underlying platform has more than a single core). For example, basic operations like for-loops are parallelized by default. Even computing arguments that are to be passed to a function are performed in parallel. “In fact, everywhere where we could possibly add parallelism into the language, we added it,” says Allen.

The runtime implicitly farms out computations to the available processor cores using a fine-grained threading model. As cores becomes idle, the runtime will transparently steal work from overloaded parts of the system and move those computations to the unused cores. The language also provides for explicit threading under the control of the programmer. Atomic operations are executed using a transactional memory scheme instead of the old-style locks.

For clusters, where the locality of the computation becomes an issue, the language has both implicit and explicit methods of distributing data. By default, Fortress arrays are spread across a system with the default arrangement determined by the Fortress libraries. This allows the implementation to use target-specific libraries for machines with similar locality characteristics. Fortress also has the notion of a “distribution,” which permits the programmer to explicitly specify both distribution of data and locality information for scheduling.

Probably the most distinguishing feature of Fortress is its support for mathematical notation. The goal here is to make the step from algorithm specification to source code as short as possible. To do this, the language supports 16-bit Unicode characters and specifies ASCII keyboard sequences that are rendered into mathematical notation. The current Fortress distribution includes an extension to the Emacs text editor that will convert these keyboard sequences as they are typed. The language designers’ devotion to this type of notation created some challenges for the compiler’s parser. For example, the use of whitespace between two operands to indicate multiplication (e.g., x y) requires some natural language smarts to determine the intention of the programmer.

Below is an example of Fortress code using some math notation. It’s the NAS (NASA Advanced Supercomputing) Conjugate Gradient Parallel function, a well-known HPC benchmark:

Fortress also allows for the creation of new grammars, so many types of domain-specific formulations are possible. For example, the molecular dynamics community could conceivably create a customized syntax for applications under its domain. The language enables these new grammars to be incorporated via library additions.

But with any new language, even a technically superior one, widespread adoption is elusive. Sun believes that maintaining Fortress as an open source project will go a long way toward attracting a larger audience. Allen says they are taking negative feedback seriously and are committed to letting the outside community help shape the design. The company hopes that giving people a stake in the language’s evolution will help drive a sense of ownership.

The notion of allowing the language to evolve is one of the central themes of the Fortress designers. Wherever possible, language features have been implemented in libraries rather than in the compiler proper to allow for alternative implementations and a more flexible upgrade path. “Fortran has been around for about 50 years,” says Allen. “I think it’s incumbent upon any design team for a new language to have that sort of timescale in mind when thinking about how their design is going to weather with time.”

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire