New Directions for Computational Science Education

By Thomas Murphy, Dr. Paul Gray, Charlie Peck, Dr. David Joiner

August 26, 2005

We could dedicate pages of text to a precise meaning of “High Performance Computing Education at the Undergraduate Level”. In the end, just one word suffices a complete description of its current state: Broken. The June 2005 President's Information Technology Advisory Committee report “Computational Science: Ensuring America's Competitiveness” states “only a small fraction of the potential of computational science is being realized”, and later in the report “The diverse technical skills and technologies underlying software, computing systems, and networks themselves constitute a critical U.S. infrastructure that we underappreciate and undervalue at our peril. Computational science is a foundation of that infrastructure.” In another word: Broken.

Phenomenal recent advances in science rely on high performance computing (HPC) platforms and computational methods, yet the calculation of pi is still the canonical example motivating use of parallel computing techniques. Examples like this lack the relevance and luster necessary to connect theory, real-world science, and most of all learning. How can we teach the nuances of parallel matrix-matrix multiplication to undergraduate students who have at best only solved linear systems with their computational mathematics package (e.g. Octave, Maple, or Mathematica) or at worst, have never been confronted with the need to solve a linear system that couldn't be solved in an instant using the computational capabilities of today's handheld devices. This fundamental disconnect first affects our ability to attract students to computational science, and later our ability to adequately keep and prepare students for graduate school or professional work in HPC.

The increasing capabilities of commodity desktop systems, the wide availability of powerful scientific software packages, and the lack of access to High Performance Computational environments in the classroom weave a nearly impenetrable mesh masking the underlying science, preventing HPC education from being realized. Instead of being impediments to HPC education, these three aspects should be embraced and integrated into the core-teaching model for parallel computing: architectures, languages, environments, and applications. HPC Education needs a big facelift. Specifically, HPC Education needs:

  • To be more visual
    • A picture is worth four-thousand bytes or a thousand words (depending on the underlying architecture).
  • To show relevant parallel speedup
    • Students need to see parallel codes actually speed up.
    • An animation sequence should be rendered in minutes, rather than in a few hours or even overnight.
  • To provide immediate feedback
    • Of actual scientific models rather than animations presenting previously computed results
    • The impact of a 3-fold speedup in an algorithm is lost when the application first sits in a queue for an hour.
  • To be relevant and authentic
    • No algorithm should be presented with the caveat “but you would never do it this way in practice.” Calculation of PI comes to mind, as do many other traditional numerical examples.
    • This does not preclude showing a progression of functionally identical algorithms as a way to motivate algorithmic development, mathematical intuition, and creativity.
  • To be reproducible on multiple systems
    • Students need to experience the variance of results produced using different computational resources. They need to gain the mathematical and scientific intuition to judge what is acceptable and what points to algorithmic or representational errors. They must gain the means to correct these errors.
  • To be resonant
    • What students are taught in their undergraduate HPC courses should be universally applicable, regardless of their subsequent paths to national laboratories, graduate programs, or the corporate world.
  • To be progressively open-ended
    • Study after study has shown that open-ended questions elicit more involvement and learning than closed ones. Our teaching, our labs and our student exercises must have room for inquiry. They must challenge students with teacher directed areas of investigation and student initiated explorations. Note this is not a proscription for graduate level study, but for age appropriate investigation and exploration from middle school through their undergraduate education.
  • To be accessible
    • Students need to have on-demand access to HPC resources inside the classroom, outside the classroom, at the local Internet Cafe, etc.
    • Attempting to teach HPC in a void of HPC resources is not really teaching HPC.
  • To be structured
    • HPC education is missing the “peeling-the-onion” approach to HPC investigations.
    • For instance, an educational exploration of the widely used Message Passing Interface (MPI), with its 120+ commands and its many message formats, structures, and communication modes, warrants a progressive sequence of lessons and exercises each building on the other starting from the six fundamental commands (MPI_Init, MPI_Finalize, MPI_Comm_Rank, MPI_Comm_Size, MPI_Send, and MPI_Recv), which alone suffice for many exercises and many real-world applications.
  • To be interdisciplinary
    • Computational Science education is currently that opaque space betwixt and between Mathematics, Computer Science, Physics, Biology, Chemistry, and the other sciences.
    • A degree of sophistication in all these disparate disciplines is necessary to grasp Computational Science and be capable as a practitioner.

Addressing the above issues is quite a tough challenge. It involves curricular changes, which take moderate to extensive time on the part of the teacher to correctly weave into an existing program. Teachers typically teach using the framework used to teach them. We must help teachers modify and enhance their frameworks based on the most current software, hardware, and algorithmic advances. Preparation time for a teacher is a particularly rare commodity. It is quite practical to foster a community of teaching practitioners to develop a body of materials for use by other teachers.

Several groups have made significant strides to address these issues. Bob Panoff and the National Computational Science Institute (NCSI) (http://www.computationalscience.org), created the Computational Science Education Reference Desk (CSERD) (http://www.shodor.org/refdesk/). He continues to lead the effort in extending itas a pathway project of the National Science Digital Library (NSDL) effort. David Joiner has created a series of example programs for CSERD designed to be an intermediate step between “hello world” style examples for parallel computing and full blown scientific models. Each of these example programs are designed to show speed-up with typical resources available in student labs and include real time visualization, so that students can “see” speedup happen. These resources give our science students potent tools and our teachers useful curricular examples. NCSI also runs a broad spectrum of week-long workshops for all flavors of college and university science teachers, covering a range of computational methods and levels of experience. Carnegie Mellon University has created Alice (http://www.alice.org/) a free 3D interactive modeling environment which its' authors have used to introduce object oriented programming to middle school, high school, and community college students. It incorporates a very natural approach to parallel programming. Drs. Shiflet, Landau, Swanson, Helland, Yasar, and others are certainly making significant contributions to Computational Science at their institutions.

The authors of this article are seeking to make an impact on Computational Science education by introducing appropriate models at the middle and high school levels. The earlier we reach students, the greater the life-long impact.

Our goals are quixotic, but simple. We are not looking to a future computational infrastructure; we seek to exploit the hidden, existing, computational infrastructure. Moore's Law and the success of the PC manufacturers amply point to an abundance of computer cycles surrounding us. We want to give access to middle school and high school students, access to the scientific tools currently used in colleges and universities. It has to be sufficiently flashy to compete with the diversions of the video game culture. The students also need to learn the science involved and the associated operational skills. The really big goal is to fully engage reasoning skills in a progressively complex open-ended self-directed exploration.

As an example of how the existing computational infrastructure can be leveraged for HPC education, consider GROMACS (http://www.gromacs.org), a molecular dynamics software package primarily designed for bimolecular systems such as proteins and lipids. It supports simulations that help researchers discover how a molecule, for example a string of amino acids, might fold upon itself as it becomes a protein, the building blocks of the human body. As the simulation progresses it shows how different parts of the biomolecule might interact with each other.

GROMACS is a complex fully-featured tool. We are working on some recipes for both teachers and students that will help them have successful guided explorations. We are motivating the use of GROMACS by noting the relative simplicity of RNA and DNA molecules which are “just” composed of chains of four bases. Identifying their interaction sites in 3 dimensions is a daunting computational task. Proteins, composed of chains of the twenty amino acids, are the most structurally complex macromolecules known. For instance there are 1.6 x 10^60 different DNA chains of length 100. For every one of these DNA chains there are 7.9×10^69 different protein chains. There are 1.3×10^130 different protein chains of length 100. Given the limitations of the human lifespan, statistical methods are needed to identify stable configurations in a timely fashion.

Teachers and students need immediate access to HPC environments for exploration of GROMACS and other computational science applications, yet apparently few have HPC resources close at hand. We have figured-out several ways to bring relatively high performance computational resources to students. Paul Gray has previously created the Bootable Cluster CD (BCCD) as a way to transform a run-of-the-mill Windows or Macintosh lab into a high speed computational cluster in a matter of minutes. See http://bccd.cs.uni.edu/ for more information. One reason we chose GROMACS is it is designed to run on clusters. We are working on a GROMACS curriculum module for the BCCD that will have the tools and educational guidance to make it accessible at middle school, high school, and at the undergraduate level.

But this is not all. Tom Murphy created two clusters at Contra Costa College to support his high performance computing technician training program (http://contracosta.edu/hpc/). He is interfacing GROMACS to these clusters so students can access them via the web if they have a hankering to do some protein folding but don't happen to have a spare Windows or Macintosh lab available. The authors are working with Dr. Henry Neeman at the University of Oklahoma to boost this computational capacity with Condor clusters via the NSF CI-TEAM grant proposal “Scalable Cyberinfrastructure for Bioinformatics and Beyond.” These Condor clusters harness spare cycles in places like under-utilized computer labs, and so we return full circle to the wonderful world of expanding computational infrastructure by creatively exploring existing computational infrastructure.

We have also created Little-Fe, a portable, educational eight node PC cluster (http://contracosta.edu/hpc/resources/Little_Fe/). It is used at schools and conferences to stir visible interest in Computational Science with meaningfully flashing lights and colorful, appropriate, scientific visualizations. We are just finishing the third version of Little-Fe and will be publishing a recipe for others to construct their own. It is expected to cost about $2,000 in parts. Next steps are a common operating system built on the BCCD, including semi-automatic update with the latest computational science and scientific visualization tools, applications, and models.

We are working to address the needed changes we have outlined through teacher workshops, curricular models, textbooks, and lab workbooks. We are looking to press drug discovery, disease origin, genome mapping, and weather modeling into service as scientific arenas for exploration with the BCCD, Little-Fe, and other HPC computing resources. Stay tuned for more adventures in weaving computational science education into the lives of the everyday students with simple low-cost solutions.


Tom Murphy

[email protected]

Tom Murphy is professor of Computer Science at Contra Costa College, a community college straddling San Pablo and Richmond, California. He is also program chair for Computer Science and director of the NSF sponsored Contra Costa College High Performance Computing Regional Education and Training Center which trains PC cluster administrators. He is also West County Robotics Coach.

Dr. Paul Gray
[email protected]

Paul Gray is an Associate Professor of Computer Science at the University of Northern Iowa. He leads the Bootable Cluster CD project (http://bccd.cs.uni.edu) and provides instructional support for the National Computational Sciences Institute summer workshops on Cluster and Parallel Computing.

Charlie Peck
[email protected]

Charlie Peck is an Assistant Professor of Computer Science at Earlham College in Richmond, IN. In addition to teaching a broad range of classes he is the nominal leader of Earlham's Cluster Computing Group (http://cluster.earlham.edu). Charlie and his students are active in the BCCD and Little-Fe projects, in addition to having a strong interest in curriculum design and development for computational science.

Dr. David Joiner
[email protected]

David Joiner is an assistant professor of computational mathematics with the New Jersey Center for Science and Technology Education at Kean University. He is a Co-PI of the Computational Science Education Reference Desk (CSERD), a pathway project of the National Science Foundation's National Science, Technology, Engineering, and Mathematics Digital Library, and has developed curricular materials for parallel computing for CSERD and for the Cluster and Parallel Computing workshops of the National Computational Science Institute.

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!

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…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a 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…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi 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