Interview: SURF Pushes the Boundaries of Deep Learning

October 21, 2019

Valeriu Codreanu. Image courtesy of SURF.

Oct. 21, 2019 — Aad van de Wijngaart interviews SURF consultant and team Leader, Valeriu Codreanu, who specializes in deep learning technologies. In the Q&A, Codreanu highlights some of SURF’s accomplishments across academia, healthcare, and research. The full interview is below.


Deep learning is developing rapidly, partly thanks to the work of SURF. By using alternative technology and smart techniques, we regularly achieve spectacular results, says SURF consultant and team leader High Performance Machine Learning Valeriu Codreanu.

Recognizing tumors with CPUs

Sometimes it helps to do things differently from others. “Everyone uses graphical processors (GPUs) for deep learning, but we don’t,” says Codreanu. “In close cooperation with Intel, we run the models on CPUs, the central processors of any PC.” SURF is an Intel Parallel Computing Center; in this program, universities, research institutes and labs work together with Intel to optimize open-source applications for computing power.

Image courtesy of SURF.

This leads to spectacular results. With an American file of 112,000 radiographs, Codreanu and his colleagues trained a model to recognize tumors in just 8 minutes, with the same average reliability as the best existing models. But that was just the beginning. “We realized that other researchers worked with compressed X-rays because of the limited memory space of GPUs: not 1,000 x 1,000 but 256 x 256 pixels. We didn’t have that limitation. When we adapted a model and released it on the full-size photos, reliability soared.”

Recognizing 300,000 plants

But there are bigger challenges. For X-rays, the model only had to distinguish 14 types of tumors, but SURF researchers also work with Pl@ntNet. Millions of people worldwide use this app to recognize plants. At the moment, the software distinguishes around 15,000 species of flora, but there are no less than 300,000 plant species in the world.

“In addition,” Codreanu explains, “the Plantnet database contains almost twelve million pictures of plants, about one and a half terabytes of data. Too much for the existing computer systems.” With two techniques from high performance computing, Codreanu and his colleagues managed to make the material manageable. On a heavy supercomputer in France (which at the time had the latest hardware), they succeeded in increasing the reliability of plant recognition by a third within 16 hours.

The significance of this project goes beyond biology. Codreanu: “You can use these techniques for every domain in which you need to distinguish large numbers of categories. Just think of facial recognition.”

Simulation in record time

A completely different application of deep learning was developed by SURF researchers for CERN, the European Organization for Nuclear Research. “They had a problem,” says Codreanu. “Simulations are essential for their research with the particle accelerator, but they consume half of their computer capacity. And you should realize that their computing grid is twenty times more powerful than our national supercomputer.”

CERN sought the solution in a deep learning application: ‘generative adversarial networks’. These are models that can generate new content based on a training set. Codreanu: “They are also used in the media to realistically edit the faces of celebrities: deep fakes. But here the input consisted of data from existing simulations to develop new, deep-learning simulations.”

SURF’s expertise consisted of scaling up the model without any loss of output. “As a result, we reduced the training time from weeks to hours. In this way, deep learning simulations can be developed and perfected much more quickly.”

Combining CT scans

SURF also uses generative adversarial networks in research for the Netherlands Cancer Institute NKI. In radiotherapy, a CT scan is made before treatment. But a treatment takes weeks. During this time, the physical condition of the patient can change and the CT scan is no longer always representative.

During the treatment, however, CT scans are also made daily to verify whether the patient is lying in the right position on the examination table. Unfortunately, these are of insufficient quality to base a new treatment plan on. In the collaborative project, NKI and SURF try to generate CT’s of sufficient quality on the basis of the original CT scan and these daily images.

“Our first goal is to achieve the required accuracy in the images,” says Codreanu. “This is not easy, because the scans are in 3D: about one gigabyte per scan. Again, the GPUs that are normally used for deep learning are of no use here.”

Enormous societal need

These latter applications of deep learning, simulations and medical research, will be central to SURF’s work in this area in the near future. Codreanu: “Researchers want to run more and more detailed simulations on our systems. Then you really need new approaches such as deep learning. And, of course, medical research meets an enormous societal need.”

To help researchers with the use of deep learning, SURF has set up a High Performance Machine Learning Group of five experts led by Codreanu. “In the end, we have only one goal,” he emphasizes: “to help Dutch scientists conduct world-class research. And for that, we ourselves must continue to lead the way.”

About SURF Open Innovation Lab

The deep learning innovations take place in the context of the SURF Open Innovation Lab. Innovation is crucial for SURF and its members to meet major challenges in research, education and society. The SURF Open Innovation Lab brings together all activities and experiments in the field of early innovation and open collaboration. SURF does this in partnership with institutions and the business community.


Source: Aad van de Wijngaart, SURF 

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