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 industy updates delivered to you every week!

TACC Supercomputer Delves Into Protein Interactions

September 22, 2021

Adenosine triphosphate (ATP) is a compound used to funnel energy from mitochondria to other parts of the cell, enabling energy-driven functions like muscle contractions. For ATP to flow, though, the interaction between t Read more…

The Latest MLPerf Inference Results: Nvidia GPUs Hold Sway but Here Come CPUs and Intel

September 22, 2021

The latest round of MLPerf inference benchmark (v 1.1) results was released today and Nvidia again dominated, sweeping the top spots in the closed (apples-to-apples) datacenter and edge categories. Perhaps more interesti Read more…

Why HPC Storage Matters More Now Than Ever: Analyst Q&A

September 17, 2021

With soaring data volumes and insatiable computing driving nearly every facet of economic, social and scientific progress, data storage is seizing the spotlight. Hyperion Research analyst and noted storage expert Mark No Read more…

GigaIO Gets $14.7M in Series B Funding to Expand Its Composable Fabric Technology to Customers

September 16, 2021

Just before the COVID-19 pandemic began in March 2020, GigaIO introduced its Universal Composable Fabric technology, which allows enterprises to bring together any HPC and AI resources and integrate them with networking, Read more…

What’s New in HPC Research: Solar Power, ExaWorks, Optane & More

September 16, 2021

In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

AWS Solution Channel

Supporting Climate Model Simulations to Accelerate Climate Science

The Amazon Sustainability Data Initiative (ASDI), AWS is donating cloud resources, technical support, and access to scalable infrastructure and fast networking providing high performance computing (HPC) solutions to support simulations of near-term climate using the National Center for Atmospheric Research (NCAR) Community Earth System Model Version 2 (CESM2) and its Whole Atmosphere Community Climate Model (WACCM). Read more…

Cerebras Brings Its Wafer-Scale Engine AI System to the Cloud

September 16, 2021

Five months ago, when Cerebras Systems debuted its second-generation wafer-scale silicon system (CS-2), co-founder and CEO Andrew Feldman hinted of the company’s coming cloud plans, and now those plans have come to fruition. Today, Cerebras and Cirrascale Cloud Services are launching... Read more…

The Latest MLPerf Inference Results: Nvidia GPUs Hold Sway but Here Come CPUs and Intel

September 22, 2021

The latest round of MLPerf inference benchmark (v 1.1) results was released today and Nvidia again dominated, sweeping the top spots in the closed (apples-to-ap Read more…

GigaIO Gets $14.7M in Series B Funding to Expand Its Composable Fabric Technology to Customers

September 16, 2021

Just before the COVID-19 pandemic began in March 2020, GigaIO introduced its Universal Composable Fabric technology, which allows enterprises to bring together Read more…

Cerebras Brings Its Wafer-Scale Engine AI System to the Cloud

September 16, 2021

Five months ago, when Cerebras Systems debuted its second-generation wafer-scale silicon system (CS-2), co-founder and CEO Andrew Feldman hinted of the company’s coming cloud plans, and now those plans have come to fruition. Today, Cerebras and Cirrascale Cloud Services are launching... Read more…

AI Hardware Summit: Panel on Memory Looks Forward

September 15, 2021

What will system memory look like in five years? Good question. While Monday's panel, Designing AI Super-Chips at the Speed of Memory, at the AI Hardware Summit, tackled several topics, the panelists also took a brief glimpse into the future. Unlike compute, storage and networking, which... Read more…

ECMWF Opens Bologna Datacenter in Preparation for Atos Supercomputer

September 14, 2021

In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) – a juggernaut in the weather forecasting scene – signed a four-year, $89-million contract with European tech firm Atos to quintuple its supercomputing capacity. With the deal approaching the two-year mark, ECMWF... Read more…

Quantum Computer Market Headed to $830M in 2024

September 13, 2021

What is one to make of the quantum computing market? Energized (lots of funding) but still chaotic and advancing in unpredictable ways (e.g. competing qubit tec Read more…

Amazon, NCAR, SilverLining Team for Unprecedented Cloud Climate Simulations

September 10, 2021

Earth’s climate is, to put it mildly, not in a good place. In the wake of a damning report from the Intergovernmental Panel on Climate Change (IPCC), scientis Read more…

After Roadblocks and Renewals, EuroHPC Targets a Bigger, Quantum Future

September 9, 2021

The EuroHPC Joint Undertaking (JU) was formalized in 2018, beginning a new era of European supercomputing that began to bear fruit this year with the launch of several of the first EuroHPC systems. The undertaking, however, has not been without its speed bumps, and the Union faces an uphill... Read more…

Ahead of ‘Dojo,’ Tesla Reveals Its Massive Precursor Supercomputer

June 22, 2021

In spring 2019, Tesla made cryptic reference to a project called Dojo, a “super-powerful training computer” for video data processing. Then, in summer 2020, Tesla CEO Elon Musk tweeted: “Tesla is developing a [neural network] training computer called Dojo to process truly vast amounts of video data. It’s a beast! … A truly useful exaflop at de facto FP32.” Read more…

Berkeley Lab Debuts Perlmutter, World’s Fastest AI Supercomputer

May 27, 2021

A ribbon-cutting ceremony held virtually at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) today marked the official launch of Perlmutter – aka NERSC-9 – the GPU-accelerated supercomputer built by HPE in partnership with Nvidia and AMD. Read more…

Esperanto, Silicon in Hand, Champions the Efficiency of Its 1,092-Core RISC-V Chip

August 27, 2021

Esperanto Technologies made waves last December when it announced ET-SoC-1, a new RISC-V-based chip aimed at machine learning that packed nearly 1,100 cores onto a package small enough to fit six times over on a single PCIe card. Now, Esperanto is back, silicon in-hand and taking aim... Read more…

Enter Dojo: Tesla Reveals Design for Modular Supercomputer & D1 Chip

August 20, 2021

Two months ago, Tesla revealed a massive GPU cluster that it said was “roughly the number five supercomputer in the world,” and which was just a precursor to Tesla’s real supercomputing moonshot: the long-rumored, little-detailed Dojo system. “We’ve been scaling our neural network training compute dramatically over the last few years,” said Milan Kovac, Tesla’s director of autopilot engineering. Read more…

CentOS Replacement Rocky Linux Is Now in GA and Under Independent Control

June 21, 2021

The Rocky Enterprise Software Foundation (RESF) is announcing the general availability of Rocky Linux, release 8.4, designed as a drop-in replacement for the soon-to-be discontinued CentOS. The GA release is launching six-and-a-half months after Red Hat deprecated its support for the widely popular, free CentOS server operating system. The Rocky Linux development effort... Read more…

Intel Completes LLVM Adoption; Will End Updates to Classic C/C++ Compilers in Future

August 10, 2021

Intel reported in a blog this week that its adoption of the open source LLVM architecture for Intel’s C/C++ compiler is complete. The transition is part of In Read more…

Google Launches TPU v4 AI Chips

May 20, 2021

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I Read more…

Hot Chips: Here Come the DPUs and IPUs from Arm, Nvidia and Intel

August 25, 2021

The emergence of data processing units (DPU) and infrastructure processing units (IPU) as potentially important pieces in cloud and datacenter architectures was Read more…

Leading Solution Providers

Contributors

AMD-Xilinx Deal Gains UK, EU Approvals — China’s Decision Still Pending

July 1, 2021

AMD’s planned acquisition of FPGA maker Xilinx is now in the hands of Chinese regulators after needed antitrust approvals for the $35 billion deal were receiv Read more…

HPE Wins $2B GreenLake HPC-as-a-Service Deal with NSA

September 1, 2021

In the heated, oft-contentious, government IT space, HPE has won a massive $2 billion contract to provide HPC and AI services to the United States’ National Security Agency (NSA). Following on the heels of the now-canceled $10 billion JEDI contract (reissued as JWCC) and a $10 billion... Read more…

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…

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

Quantum Roundup: IBM, Rigetti, Phasecraft, Oxford QC, China, and More

July 13, 2021

IBM yesterday announced a proof for a quantum ML algorithm. A week ago, it unveiled a new topology for its quantum processors. Last Friday, the Technical Univer Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Frontier to Meet 20MW Exascale Power Target Set by DARPA in 2008

July 14, 2021

After more than a decade of planning, the United States’ first exascale computer, Frontier, is set to arrive at Oak Ridge National Laboratory (ORNL) later this year. Crossing this “1,000x” horizon required overcoming four major challenges: power demand, reliability, extreme parallelism and data movement. Read more…

Intel Unveils New Node Names; Sapphire Rapids Is Now an ‘Intel 7’ CPU

July 27, 2021

What's a preeminent chip company to do when its process node technology lags the competition by (roughly) one generation, but outmoded naming conventions make it seem like it's two nodes behind? For Intel, the response was to change how it refers to its nodes with the aim of better reflecting its positioning within the leadership semiconductor manufacturing space. Intel revealed its new node nomenclature, and... Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire