Petascale Era Will Force Software Rethink

By Jim Sexton

July 20, 2007

As we enter the petascale era, there will be a number of challenges to overcome before applications can truly take advantage of the enormous computational power that is coming available. One of the most pressing of these challenges will be to design software programs that map well to petascale architectures to allow the community to solve previously unattainable scientific and business problems.

For the last 20 years, performance improvements have been delivered by increasing processor frequencies. In the petascale era, processor frequencies will no longer increase due to fundamental atomic limits in our ability to shrink features on Silicon. Moore’s Law will continue, but performance increases will now come through parallelism and petascale systems will deliver performance by deploying hundreds of thousands of individual processor cores. Multiple cores will be assembled into individual chips and tens of thousands of chips will then be assembled to deliver the petascale performance which Moore’s law predicts to arrive in the next few years.

Programming approaches for multicore chips and parallel multicore systems are well understood. The programming challenge which arises however is very complex. When developing code for a single processor, a programmer is able to focus on the algorithms, and can, to first approximation, ignore the system architecture issues during program design. Compilers for single processor programming are well developed and mature and do a very good job at mapping a program properly to the system architecture on which that program is designed to run.

When programming for a parallel multicore process architecture, a programmer is forced to manage algorithmic and systems architectures together. The parallel system architecture requires that a programmer decide how to distribute data and work among the parallel processing elements in the architecture, at that same time as the algorithm is being designed. The parallel programmer needs to make many critical decisions which have huge impact on program performance and capability all through the design process. These decisions include items such as how many chips and cores will be required, how will data be distributed and moved across these elements, and how will work be distributed. On parallel systems, programming has changed from being a routine technical effort to being a creative art form.

The opportunity provided by leveraging these big parallel machines is enormous. It will be possible to answer some really hard questions in complex systems in all spheres of human activities. Examples include a better understanding of the processes that drive global warming, insight into how the world wide economy functions, and a full understanding of the chemical and biological processes that occur within the human body. Right now, we have the computing power to address these questions. We just don’t have programs because they are so complex and so difficult to develop, test and validate.

On average, it takes two to four years to develop a programming code to simulate just one human protein. The challenge the scientific community now faces is finding the people who understand how to write complex programs for petascale architectures. There is an obvious Catch-22 involved: Until more of these programs start running on parallel machines and show results, it will be hard to justify the investment needed to fund the building of a whole infrastructure from scratch. This may include PhD programs at universities, recruitment of specialists, and the build-up of resources.

Although a major shift to parallelism is beginning, there is a high cost of entry. Right now, parallelism is in the early adopter phase. Before it shifts to the mainstream/commercial phase, the community will need to see a clear cost/benefit before it brings everyone along. In order to advance this effort in the U.S., the Scientific Discovery Advanced Computing Discovery (SciDAC) program is establishing nine Centers for Enabling Technologies to focus on specific challenges in petascale computing. These multidisciplinary teams are led by national laboratories and universities and focus on meeting the specific needs of SciDAC applications for researchers as they move toward petascale computing. These centers will specialize in applied mathematics, computer science, distributed computing and visualization, and will be closely tied to specific science application teams.

In addition to scientific questions, industry applications could help drive the development of the code and lead to mainstream adoption. One example is the energy and oil/petroleum industry. petascale computing may improve petroleum reserve management, nuclear reactor design, and nuclear fuel reprocessing. Another is the weather. As we need more precise, short-term weather prediction, microclimate modeling comes into play.

In the past, the computer science community tended to focus on the hardware and system software, but left the development of applications to others. The trend now is that programmers need to develop applications so that they are tightly coupled to the systems they will run on. One needs to design the program for the system. That’s been the anathema for many years.

—–

About the Author

Jim Sexton is the lead for Blue Gene Applications at IBM’s IBM T. J. Watson Research Center in Yorktown Heights, NY. He received his Ph.D. in theoretical physics from Columbia University. He was a Research Fellow at Fermi National Accelerator Laboratory, then at the Institute for Advanced Study at Princeton University. Before joining the staff at the T. J. Watson Research Center, the was a professor at Trinity College in Dublin. His areas of interest include high performance computing, systems architectures, HPC systems software, theoretical physics and high energy theoretical physics.

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