Exascale Programming: Adapting What We Have Can (and Must) Work

By Michael A. Heroux, Sandia National Laboratories

January 14, 2016

The demands of massive concurrency and increased resilience required for effective exascale computing have led to claims that our existing approach to application programming must be replaced. In this article I argue that disruption is inevitable but will not require new languages or programming environments. Instead disruption will be in application design, introducing new control layers that will provide the concurrency, adaptability and resilience features we need in order to achieve effective exascale performance levels.

Before starting a discussion of parallel programming futures, we should state some concepts that are often vaguely or multiply defined. First, a model is an abstract system for reasoning about design and implementation, so programming models help us reason about programming, execution models help us reason about how a program will run, particularly when thinking about parallel execution. Programming and execution environments are concrete toolsets we use to implement and run a program. Finally, a computer language and its companion libraries (Fortran, C++, MPI, etc.) can be used to encode many programming models and can be compiled to run on many execution environments.

In this article I argue that programming and execution environments are changing to meet the future needs of exascale systems. Libraries (especially MPI) and OpenMP features are evolving to provide credible exascale programming environments for C++, C and Fortran, that are also portable and sustainable. In addition, C++ is evolving to make embedded domain specific language (EDSL) enhancements convenient to develop and use. EDSLs provide programming model and environment extensions to support parallel programming while still being portable, since the extensions are written in standard C++ syntax. EDSLs can also be targeted for further optimizations by the execution environment. EDSLs provide the most credible path to language support for new parallel programming models, syntax and execution.

Adapting What We Have Can (and Must) Work
C++ (with C as an important subset, and with OpenMP extensions) has become an essential element of scientific computing. C++ provides suitable abstractions and extensibility for defining EDSLs, while at the same time permitting explicit data references for efficient execution and interoperability with Fortran. It also has large user communities and an active standards committee whose interests align with high performance scientific computing. New features in the 2011, 2014 and 2017 standards position C++ to be even more effective for parallel computing and for extending EDSL capabilities. With the 2020 standard, C++ should contain features for the most common types of on-node parallelism including hierarchical and task-graph. Furthermore, the emergence of the modular, open compilation tools LLVM and Clang give research and vendor communities a rich platform for new R&D in production-quality compiler environments, enabling development of future language features, pluggable tools and custom optimization passes.

OpenMP is evolving to address tasking requirements, complemented by the OpenACC fork (whose capabilities are being integrated into OpenMP) to address accelerators. OpenMP has a committed two-year release cycle, with approved features published in alternate years. Simple sprinkling of OpenMP/OpenACC directives into an existing code has given these tools a bad reputation, but for fundamentally refactored applications, performance can be very good. OpenMP also promises better sustainability, especially compared to explicit pthread, CUDA or OpenCL programming approaches, which require retuning with each new generation of hardware.

ExascaleEditionThumbMPI is evolving as well and has always been more a portability layer than a programming API. For well-designed applications, explicit MPI calls are encapsulated in abstraction layers, e.g., exchangeGhostValues(), which most application developers call instead of MPI functions. Inclusion of asynchronous global and neighborhood collectives enables implementation of latency hiding algorithms, and MPI shared memory features enable use of shared memory between ranks, and thread-like shared memory programming.

Fortran is essential for exascale programming. The explicit policy of non-overlapping arrays (enabling compilers to more easily vectorize and parallelize automatically), simple loop syntax, longstanding support for robust real and complex arithmetic, along with the vast quantity of encoded scientific knowledge mean Fortran will remain the primary development language for many domain scientists who write software. Even as clean-slate Fortran development efforts decline, the value of the legacy Fortran software base and its ongoing refactoring and extension will be essential for decades to come.

Execution environments must undergo more substantial changes than programming languages and environments. Current runtime systems are very good at concurrent execution, but are not designed for lightweight threading (except GPUs) or locality-aware task mapping. Thread-scalable computing will require better, more transparent on-node thread parallel environments. Fortunately, we see much progress lately, underneath existing programming languages and environments.

Adding Tasking is Critical
Although programming and execution environments are evolving smoothly toward exascale capabilities, we do have disruptive changes ahead. Most scalable parallel applications today have simple data and work decompositions: Each MPI rank owns a static portion of large data objects, e.g., a subdomain of a large distributed global domain, and each rank executes its code sequentially (potentially vectorizing), or with modest thread-parallel capabilities. This approach works on existing NUMA multicore systems by assigning multiple MPI ranks to a node and using OpenMP across a handful of cores, but performance using this approach is not sustainable as core counts continue to increase.

Tasking, with work granularities sufficiently large to make effective use of one or a few cores, must be added to most applications in order to sustain performance improvement as concurrency demands increase. Specifically, tasking requires one or more levels of additional decomposition (at least logically) of data objects, e.g., create multiple patches or tiles from each MPI subdomain, and assign tasks to execute concurrently on these patches. Within a single shared memory node, tasks can in principle cooperate closely, executing dataflow patterns, sharing data and otherwise collaborating in lightweight parallel computation.

A tasking layer in an application enables portability across GPU (where the GPU gets a big patch and handles its task concurrency itself), multicore and manycore devices; and works with heterogeneous device combinations if task-executed code is written using OpenMP/OpenACC or uses compile-time abstraction layers such as the Kokkos library to compile to each specific device type. This tasking layer can also be implemented using a second layer of message passing. Furthermore, a tasking layer permits exploitation of new resources of parallelism. Fine grain functional parallelism, and pipeline, wavefront, and parallel-prefix execution patterns are feasible because of shared memory and lightweight control transfer. These new parallelism resources are essential as we exhaust traditional sources such as SPMD data parallelism and ensembles. It is worth noting that tasking designs within application codes do not impose use of particular parallel programming languages or environments and can in principle permit combinations of several approaches in a single executable program.

Tasking also supports new models and strategies for high bandwidth memory, resilience and load balance. Task work and data can be scoped to fit into a particular memory space. Also, since parent tasks have all necessary state to re-spawn child tasks, they can establish pre and post conditions on state data for child tasks, and timeout conditions, or simply re-spawn and re-queue for better execution flow. These attributes can protect against many failure sources, including reducing silent data corruption failures, and can improve execution time. All of the innovation required to support this kind of programming is already underway in the C++ language, programming and execution environments. No replacement is required.

The final important aspect of a tasking layer is that task-executed code is encapsulated within the tasking framework and itself has only modest parallel execution requirements: It should vectorize if possible to be able to execute efficiently on a small number of shared-cache cores. As a result, task-executed code can be written in any common HPC language, including Fortran, thus preserving our Fortran code base. In most instances, the task management layer is most effectively written in C++, but a well-designed application can insulate domain scientists from the details of task management and permit them to write new functionality at the task level in much the same way as they write code for MPI-based applications today. The only major added concern is how to encode inter-task dependencies. I think training programmers about futures concepts, which can be used to encapsulate control transfer logic, is perhaps the best way to portably provide this encoding.

The Reality of Starting Anew
With the explicit challenge of reaching multi-billion-way concurrency in order to reach exascale performance levels and beyond, many people have argued that we need a clean break from incrementally improving on our existing approaches to parallel programming, and such arguments have spurred development and exploration of new parallel programming languages. While such efforts are certainly interesting research, there is no evidence that the HPC community can bring a new language to market in a portable, sustainable way. The lack of traction gained by Chapel, X-10 and Fortress, the HPCS languages introduced more than a decade ago are one indication, but for an even more telling example, we simply have to look at the current state of Fortran to reach this conclusion.

trilinosWhile Fortran remains an important language for scientific computing, and new lines of Fortran code are still being written, the adoption of new Fortran features is very slow. In 2009 and 2010, the C++ based Trilinos project developed Fortran interface capabilities, called ForTrilinos. As an object-oriented (OO) collection of libraries, we assumed that the OO features of Fortran 2003 would provide us with natural mappings of Trilinos classes into Fortran equivalents. Over the two-year span of the ForTrilinos effort, we discovered that compiler support for 2003 features was very immature. ForTrilinos developers quickly came to know the handful of compiler developers who worked on these features and, despite close collaboration with them to complete and stabilize the implementation of Fortran 2003 features (in 2010), ForTrilinos stalled and is no longer developed.

The Fortran 2008 standard has similar issues. Co-arrays, an elegant approach for supporting SPMD parallel programming, first developed in the early 1990s, is part of the 2008 standard, but any application developer interested in portability cannot use them. In contrast, the C++ standards community is committed to producing a revised standard every three years, and features for the coming standard often appear in vendor compilers before or simultaneously with ratification of the standard.

Yes, Fortran is an important programming language for scientific computing, and it is a language our community owns, but the reality is that the use of new standard Fortran features is very restricted, if portability is paramount. Furthermore, it is the feature set of Fortran 95 that is most valuable to scientific computing. The recent announcement of a revitalized effort to produce a Fortran equivalent to Clang is exciting, especially if the resulting Fortran 95 features are solid and compiled code vectorizes well. However, the anemic adoption of new Fortran standards should serve as strong evidence that any prospects of a new scientific computing language are very slim. Also, the likelihood that the broader computing community will cooperate with us in establishing a new language is low. C++ is the broader community’s answer for high-performance concurrency.

My pessimism about new languages does not mean novelty is impossible. It means that novelty must be introduced as modest extensions to existing capabilities. CUDA took this route and other task-based embedded C++ DSLs show promise.

The exascale performance milestone is approaching, but reaching it requires disruptive changes at all levels of the computing ecosystem, driven by needs for massive concurrency. Scientific application design will require disruptive changes in software architecture, in the form of tasking, in order to address increasing hierarchies, take advantage of memory hierarchies, the commodity performance curves of thread-state count and vectorization, and address resilience. However, the practical realities of our production environments make the possibilities for brand-new software eco-systems extremely unlikely. We only have to look at the reality of our Fortran environments to see this.

Fortunately, our existing languages and environments are adapting to support the programming and execution models necessary for exascale performance. The disruption on the path to exascale is being contained to adapting our existing languages and environments, not replacing them.

Author Bio
Michael A. Heroux is a Distinguished Member of the Technical Staff at Sandia National Laboratories, working on new algorithm development, and robust parallel implementation of solver components for problems of interest to Sandia and the broader scientific and engineering community. He leads development of the Trilinos Project, an effort to provide state of the art solution methods in a state of the art software framework. Trilinos is an award-winning product, freely available as Open Source and actively developed by dozens of researchers. Dr. Heroux is also the lead developer and architect of the HPCG benchmark, intended as an alternative ranking for the TOP 500 computer systems. For more: http://www.sandia.gov/~maherou/biography.html

Subscribe to HPCwire's Weekly Update!

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

U.S. CTO Michael Kratsios Adds DoD Research & Engineering Title

July 13, 2020

Michael Kratsios, the U.S. Chief Technology Officer, has been appointed acting Undersecretary of Defense for research and engineering. He replaces Mike Griffin, who along with his deputy Lis Porter, stepped down last wee Read more…

By John Russell

Supercomputer Research Reveals Star Cluster Born Outside Our Galaxy

July 11, 2020

The Milky Way is our galactic home, containing our solar system and continuing into a giant band of densely packed stars that stretches across clear night skies around the world – but, it turns out, not all of those st Read more…

By Oliver Peckham

Max Planck Society Begins Installation of Liquid-Cooled Supercomputer from Lenovo

July 9, 2020

Lenovo announced today that it is supplying a new high performance computer to the Max Planck Society, one of Germany's premier research organizations. Comprised of Intel Xeon processors and Nvidia A100 GPUs, and featuri Read more…

By Tiffany Trader

Xilinx Announces First Adaptive Computing Challenge

July 9, 2020

A new contest is challenging the computing world. Xilinx has announced the first Xilinx Adaptive Computing Challenge, a competition that will task developers and startups with finding creative workload acceleration solutions. Xilinx is running the Adaptive Computing Challenge in partnership with Hackster.io, a developing community... Read more…

By Staff report

Reviving Moore’s Law? LBNL Researchers See Promise in Heterostructure Oxides

July 9, 2020

The reality of Moore’s law’s decline is no longer doubted for good empirical reasons. That said, never say never. Recent work by Lawrence Berkeley National Laboratory researchers suggests heterostructure oxides may b Read more…

By John Russell

AWS Solution Channel

Best Practices for Running Computational Fluid Dynamics (CFD) Workloads on AWS

The scalable nature and variable demand of CFD workloads makes them well-suited for a cloud computing environment. Many of the AWS instance types, such as the compute family instance types, are designed to include support for this type of workload.  Read more…

Intel® HPC + AI Pavilion

Supercomputing the Pandemic: Scientific Community Tackles COVID-19 from Multiple Perspectives

Since their inception, supercomputers have taken on the biggest, most complex, and most data-intensive computing challenges—from confirming Einstein’s theories about gravitational waves to predicting the impacts of climate change. Read more…

President’s Council Targets AI, Quantum, STEM; Recommends Spending Growth

July 9, 2020

Last week the President Council of Advisors on Science and Technology (PCAST) met (webinar) to review policy recommendations around three sub-committee reports: 1) Industries of the Future (IotF), chaired be Dario Gil (d Read more…

By John Russell

Max Planck Society Begins Installation of Liquid-Cooled Supercomputer from Lenovo

July 9, 2020

Lenovo announced today that it is supplying a new high performance computer to the Max Planck Society, one of Germany's premier research organizations. Comprise Read more…

By Tiffany Trader

President’s Council Targets AI, Quantum, STEM; Recommends Spending Growth

July 9, 2020

Last week the President Council of Advisors on Science and Technology (PCAST) met (webinar) to review policy recommendations around three sub-committee reports: Read more…

By John Russell

Google Cloud Debuts 16-GPU Ampere A100 Instances

July 7, 2020

On the heels of the Nvidia’s Ampere A100 GPU launch in May, Google Cloud is announcing alpha availability of the A100 “Accelerator Optimized” VM A2 instance family on Google Compute Engine. The instances are powered by the HGX A100 16-GPU platform, which combines two HGX A100 8-GPU baseboards using... Read more…

By Tiffany Trader

Q&A: HLRS’s Bastian Koller Tackles HPC and Industry in Germany and Europe

July 6, 2020

In this exclusive interview for HPCwire – sadly not face to face – Steve Conway, senior advisor for Hyperion Research, talks with Dr.-Ing Bastian Koller about the state of HPC and its collaboration with Industry in Europe. Koller is a familiar figure in HPC. He is the managing director at High Performance Computing Center Stuttgart (HLRS) and also serves... Read more…

By Steve Conway, Hyperion

OpenPOWER Reboot – New Director, New Silicon Partners, Leveraging Linux Foundation Connections

July 2, 2020

Earlier this week the OpenPOWER Foundation announced the contribution of IBM’s A21 Power processor core design to the open source community. Roughly this time Read more…

By John Russell

Hyperion Forecast – Headwinds in 2020 Won’t Stifle Cloud HPC Adoption or Arm’s Rise

June 30, 2020

The semiannual taking of HPC’s pulse by Hyperion Research – late fall at SC and early summer at ISC – is a much-watched indicator of things come. This yea Read more…

By John Russell

Racism and HPC: a Special Podcast

June 29, 2020

Promoting greater diversity in HPC is a much-discussed goal and ostensibly a long-sought goal in HPC. Yet it seems clear HPC is far from achieving this goal. Re Read more…

Top500 Trends: Movement on Top, but Record Low Turnover

June 25, 2020

The 55th installment of the Top500 list saw strong activity in the leadership segment with four new systems in the top ten and a crowning achievement from the f Read more…

By Tiffany Trader

Supercomputer Modeling Tests How COVID-19 Spreads in Grocery Stores

April 8, 2020

In the COVID-19 era, many people are treating simple activities like getting gas or groceries with caution as they try to heed social distancing mandates and protect their own health. Still, significant uncertainty surrounds the relative risk of different activities, and conflicting information is prevalent. A team of Finnish researchers set out to address some of these uncertainties by... Read more…

By Oliver Peckham

[email protected] Turns Its Massive Crowdsourced Computer Network Against COVID-19

March 16, 2020

For gamers, fighting against a global crisis is usually pure fantasy – but now, it’s looking more like a reality. As supercomputers around the world spin up Read more…

By Oliver Peckham

[email protected] Rallies a Legion of Computers Against the Coronavirus

March 24, 2020

Last week, we highlighted [email protected], a massive, crowdsourced computer network that has turned its resources against the coronavirus pandemic sweeping the globe – but [email protected] isn’t the only game in town. The internet is buzzing with crowdsourced computing... Read more…

By Oliver Peckham

Supercomputer Simulations Reveal the Fate of the Neanderthals

May 25, 2020

For hundreds of thousands of years, neanderthals roamed the planet, eventually (almost 50,000 years ago) giving way to homo sapiens, which quickly became the do Read more…

By Oliver Peckham

DoE Expands on Role of COVID-19 Supercomputing Consortium

March 25, 2020

After announcing the launch of the COVID-19 High Performance Computing Consortium on Sunday, the Department of Energy yesterday provided more details on its sco Read more…

By John Russell

Neocortex Will Be First-of-Its-Kind 800,000-Core AI Supercomputer

June 9, 2020

Pittsburgh Supercomputing Center (PSC - a joint research organization of Carnegie Mellon University and the University of Pittsburgh) has won a $5 million award Read more…

By Tiffany Trader

Honeywell’s Big Bet on Trapped Ion Quantum Computing

April 7, 2020

Honeywell doesn’t spring to mind when thinking of quantum computing pioneers, but a decade ago the high-tech conglomerate better known for its control systems waded deliberately into the then calmer quantum computing (QC) waters. Fast forward to March when Honeywell announced plans to introduce an ion trap-based quantum computer whose ‘performance’ would... Read more…

By John Russell

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

By Doug Black

Leading Solution Providers


Nvidia’s Ampere A100 GPU: Up to 2.5X the HPC, 20X the AI

May 14, 2020

Nvidia's first Ampere-based graphics card, the A100 GPU, packs a whopping 54 billion transistors on 826mm2 of silicon, making it the world's largest seven-nanom Read more…

By Tiffany Trader

‘Billion Molecules Against COVID-19’ Challenge to Launch with Massive Supercomputing Support

April 22, 2020

Around the world, supercomputing centers have spun up and opened their doors for COVID-19 research in what may be the most unified supercomputing effort in hist Read more…

By Oliver Peckham

Australian Researchers Break All-Time Internet Speed Record

May 26, 2020

If you’ve been stuck at home for the last few months, you’ve probably become more attuned to the quality (or lack thereof) of your internet connection. Even Read more…

By Oliver Peckham

15 Slides on Programming Aurora and Exascale Systems

May 7, 2020

Sometime in 2021, Aurora, the first planned U.S. exascale system, is scheduled to be fired up at Argonne National Laboratory. Cray (now HPE) and Intel are the k Read more…

By John Russell

Global Supercomputing Is Mobilizing Against COVID-19

March 12, 2020

Tech has been taking some heavy losses from the coronavirus pandemic. Global supply chains have been disrupted, virtually every major tech conference taking place over the next few months has been canceled... Read more…

By Oliver Peckham

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

TACC Supercomputers Run Simulations Illuminating COVID-19, DNA Replication

March 19, 2020

As supercomputers around the world spin up to combat the coronavirus, the Texas Advanced Computing Center (TACC) is announcing results that may help to illumina Read more…

By Staff report

$100B Plan Submitted for Massive Remake and Expansion of NSF

May 27, 2020

Legislation to reshape, expand - and rename - the National Science Foundation has been submitted in both the U.S. House and Senate. The proposal, which seems to Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This