Hoefler’s Whirlwind ISC20 Virtual Tour of ML Trends in 9 Slides

By John Russell

June 23, 2020

The ISC20 experience this year via livestreaming and pre-recordings is interesting and perhaps a bit odd. That said presenters’ efforts to condense their comments makes for economic use of your time. Torsten Hoefler’s whirlwind 12-minute tour of ML is a great example. Hoefler, leader of the planned ISC20 Machine Learning Day, does a nice job scanning a few recent industry highlights and whetting one’s appetite for next year by summarizing the ML sessions that had been planned.

Presented here are a few of his lightly-edited comments and slides. (Link to video)

MICROSOFT BUILDS MAMMOUTH AI SYSTEM

Torsten Hoefler, ETH Zurich

“Let me start a little bit about the interesting industry developments. Here we have the long-term player, Google Cloud. The news is that you can actually rent a TPU pod with about 100 petaflops brain float performance (Brain Floating Point Format, Bfloat16), which is just quite amazing. Even though it is a reduced precision format, so only eight exponent bits and seven mantissa bits – so maybe not suitable for scientific computing but perfectly suitable for machine learning. [Other] news and this was nearly a year ago is you can also buy a mini TPU in Raspberry Pi format, which unfortunately only support six bit floating point but it supports four teraflops per second at only two watts. Quite amazing,” said Hoefler.

“Up next is another very big company. Microsoft pledged $1 billion for OpenAI to support the holy grail of artificial intelligence, general intelligence. Basically developing models that go way beyond what we are used to today where we have to fine tune them to tasks. I will talk about this in a couple of minutes. Microsoft recently announced that they built an AI supercomputer, which is quite a respectable machine. They assess that it would be in the top five of the Top500 list. It has about 285,000 CPU cores, 10,000 GPUs and 400 gigabit per second network connectivity. You can read more in the Microsoft block (slide). Kevin Scott, the CTO of Microsoft, has given a wonderful talk about that machine as well.”

Talking about Nvidia’s latest A100 GPU, Hoefler said, “This is a true engineering marvel. So getting to the maximum size of the reticle that you can build today, so 820 something square millimeters out of the maximum 850. [It has] 54 billion transistors and very impressive datasheet as we can see here. So number of [FP64 and FP32 CUDA cores] is going up. The number of tensor cores is slightly going down as is the frequency however the overall performance is just incredibly impressive.

“[Here’s another] interesting news item. If you thought as a scientific computing person, you could get away without using tensor cores, unfortunately if you want to achieve peak performance on that architecture, with FP64, even, you have to use tensor cores. It also has quite impressive memory bandwidth. I don’t want to go through all of this. One interesting feature with these tensor cores is now that there’s a new format, a TF32, even though I would have called it for fairness, TF19, because actually, it only has 19 significant bits that are used for the computation, but it’s backwards compatible to 32-bit calculations, which makes it quite interesting for legacy codes. I’m not so sure if this is very useful for newly tuned codes, because at the end, what’s going to happen with this format is you’re going to use the full memory bandwidth of an FP32 or 32 bits for every single calculation and you’re only performing that calculation in 16-bit accuracy, of course, with a 32-bit accumulator. So that’s a wonderful backwards compatibility feature,” the said.

TRAINING A MODEL WITH 175 BILLION PARAMETERS

“We had very interesting [work] in the recent weeks where a new application is showing quite massive [improvement]. I mean OpenAI’s GPT-3 models. So OpenAI has this history of these GPT (generative pretrained transformer) models, where they train very large transformers in a generative setting. GPT3 is the largest one of those and probably the largest machine learning model ever trained. It has 175 billion parameters. So if you store this with two bytes per parameter, so just 16 bits for each parameter, that’s going to be [a] 350-gigabyte model size. That’s just a model size. Imagine you want to run inference on this; even inference is quite expensive, but training is even more expensive,” said Hoefler adding it would cost “$12 million to train that model in just in GPU credits.’

“There’s a wonderful paper out of OpenAI (June 2020) that explains what you can do with this model. And it’s quite astonishing. So this model is actually a so-called few-shot learner. The idea is you have a large trained model that is trained on a very large corpus that was basically grabbed off the internet. But you don’t fine tune it to the specific tasks. You tune it to specify the task during inference time. So there’s the so-called few-shot setting and the zero-shot setting. So the zero-shot setting is easiest to explain, it basically behaves like a human. I ask the model, please translate cat from English to French, and it will [respond with] that prospective French word. That is a very interesting feature of this network, but it can actually do it, and it achieves state-of-the-art and better results [using] these zero-shot training settings.

“A few-shots basically means that I present a couple of examples to the model. So I provide a series of translations, so cat, and then [a few more] French terms, usually on the order of 10 to 50. This model outperforms pretty much all the state of the art. So you can read the paper, if you want to learn about all the wonderful results that OpenAI has achieved with this. They have not done fine tuning, by the way, probably it is too expensive. I would be very, very curious to see what would happen if you fine-tuned that massive model. But it’s so expensive that they couldn’t even retrain it after they found a bug in their data cleaning procedure. So they had to work around the bug and there are some, some interesting notes in the paper.

“From an HPC perspective, this table (slide below) from that paper is one of the most important tables. You can see here the number of parameters is quite massive and has been growing over time and you can also see the total flops required to train so we are essentially at 314 zetaflops. I’ve made a little analysis to see what this means, so 314 zettaflop, of course mixed-precision FP16 and FP32 most likely. [This would require] 155 years on a V100 if we assume very high performance, like the record performance that has been published. Or that is 400 megawatt hours of that same GPU or for that single training run nearly $4 million, assuming zero communication overhead and perfect use of these GPUs,” said Hoefler.

THE 2020 ML DAY LINEUP…MAYBE NEXT YEAR

“Let me now get a little bit more into the ML day program we had planned for this year and now have planned for next year. The first planned session was on machine learning for climate and weather to be hosted by Peter Dueben, who fortunately sent me some slides that summarize what is going on there.

“The idea here is that we have the earth of course that we are simulating, [and it] is really large and the solution is very, very limited, because we cannot represent every single air particle essentially in a computer. We have to have a very limited resolution at the kilometer scale., but unfortunately, the system itself that we are simulating has very chaotic dynamics; it has all kinds of so called sub-grid effects that are very, very hard to capture in scientific simulations. These processes are simply not resolved and that is one of the bigger problems, especially in earth system component models that are connected in non-trivial ways.”

“However, what we have is very large number of observations. So satellite data, we have plane data – well, we had plane data when the planes were still flying. We have a lot of data that we could train machine learning models on. The idea now is, why don’t we use these machine learning models for multiple different opportunities. Here’s some of the opportunities that we could we could talk about,” said Hoefler.

“We could refine the observations [and] this is something that the community is already doing. We could refine the data assimilation process. We could help with numerical weather forecasting itself, so we could accelerate it using machine learning accelerators, for example the TPU or these low precision tensor cores, and then implement post processing and help with post processing and make simulations even less require it or improve the data that comes out of the simulation. So this is actually something that we have done in my lab.”

“Then the second session was to be organized by Rio Yokota from Tokyo Tech. And he invited a set of wonderful speakers. So first Boris Ginsburg from Nvidia talking about the latest, greatest developments in stochastic device and the gradient descent methods and how to accelerate this with Nvidia GPUs. [This has been done] with one of his PhD students looking at second order optimization or higher order optimization in general, which can not only lead to more accurate models, but also it can also lead in certain circumstances to higher performance. This is a very, very interesting research direction that we are also embarking in. Then Yang You who just graduated from Berkeley and is now taking an assistant professor position on how to work with very, very, large scale systems. So using the LAMB optimizer that we all know and love.”

“The third session was organized by Maryam Dehnavi and Tal Ben-Nun and they invited Amir Gholami (UC Berkeley) to talk about their integrated approach to deep neural network design. They have a nice set of methods that you can combine into a tool chain to do end-to-end training. And then Ce Zhang from ETH Zurich who was going to talk about various ways to implement distributed training ranging from centralized synchronous to synchronous, asynchronous, and then to various decentralized, synchronous and asynchronous training methods. We hope that we can have all these speakers appear in the forthcoming years, and we can we can listen to what they have to say.”

Link to video, https://2020.isc-program.com/presentation/?id=inv_sp139&sess=sess348

Hoefler Bio

Torsten Hoefler is an Associate Professor of Computer Science at ETH Zürich, Switzerland. Before joining ETH, he led the performance modeling and simulation efforts of parallel petascale applications for the NSF-funded Blue Waters project at NCSA/UIUC. He is also a key member of the Message Passing Interface (MPI) Forum where he chairs the “Collective Operations and Topologies” working group. Torsten won best paper awards at the ACM/IEEE Supercomputing Conference SC10, SC13, SC14, EuroMPI’13,

HPDC’15, HPDC’16, IPDPS’15, and other conferences. He published numerous peer-reviewed scientific conference and journal articles and authored chapters of the MPI-2.2 and MPI-3.0 standards. He received the Latsis prize of ETH Zurich as well as an ERC starting grant in 2015. His research interests revolve around the central topic of “Performance-centric System Design” and include scalable networks, parallel programming techniques, and performance modeling. Additional information about Torsten can be found on his homepage at htor.inf.ethz.ch.

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!

Watch Nvidia’s GTC21 Keynote with Jensen Huang Livestreamed Right Here at HPCwire Today at 8:30 am PT

April 12, 2021

Join HPCwire right here on Monday, April 12, at 8:30 am PT to see the Nvidia GTC21 keynote from Nvidia’s CEO, Jensen Huang, livestreamed in its entirety. Hosted by HPCwire, you can click to join the Huang keynote on our livestream to hear Nvidia’s expected news and... Read more…

The US Places Seven Additional Chinese Supercomputing Entities on Blacklist

April 8, 2021

As tensions between the U.S. and China continue to simmer, the U.S. government today added seven Chinese supercomputing entities to an economic blacklist. The U.S. Entity List bars U.S. firms from supplying key technolog Read more…

Argonne Supercomputing Supports Caterpillar Engine Design

April 8, 2021

Diesel fuels still account for nearly ten percent of all energy-related U.S. carbon emissions – most of them from heavy-duty vehicles like trucks and construction equipment. Energy efficiency is key to these machines, Read more…

Habana’s AI Silicon Comes to San Diego Supercomputer Center

April 8, 2021

Habana Labs, an Intel-owned AI company, has partnered with server maker Supermicro to provide high-performance, high-efficiency AI computing in the form of new training and inference servers that will power the upcoming Read more…

Intel Partners Debut Latest Servers Based on the New Intel Gen 3 ‘Ice Lake’ Xeons

April 7, 2021

Fresh from Intel’s launch of the company’s latest third-generation Xeon Scalable “Ice Lake” processors on April 6 (Tuesday), Intel server partners Cisco, Dell EMC, HPE and Lenovo simultaneously unveiled their first server models built around the latest chips. And though arch-rival AMD may... Read more…

AWS Solution Channel

Volkswagen Passenger Cars Uses NICE DCV for High-Performance 3D Remote Visualization

 

Volkswagen Passenger Cars has been one of the world’s largest car manufacturers for over 70 years. The company delivers more than 6 million automobiles to global customers every year, from 50 production locations on five continents. Read more…

What’s New in HPC Research: Tundra, Fugaku, µHPC & More

April 6, 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…

Watch Nvidia’s GTC21 Keynote with Jensen Huang Livestreamed Right Here at HPCwire Today at 8:30 am PT

April 12, 2021

Join HPCwire right here on Monday, April 12, at 8:30 am PT to see the Nvidia GTC21 keynote from Nvidia’s CEO, Jensen Huang, livestreamed in its entirety. Hosted by HPCwire, you can click to join the Huang keynote on our livestream to hear Nvidia’s expected news and... Read more…

The US Places Seven Additional Chinese Supercomputing Entities on Blacklist

April 8, 2021

As tensions between the U.S. and China continue to simmer, the U.S. government today added seven Chinese supercomputing entities to an economic blacklist. The U Read more…

Habana’s AI Silicon Comes to San Diego Supercomputer Center

April 8, 2021

Habana Labs, an Intel-owned AI company, has partnered with server maker Supermicro to provide high-performance, high-efficiency AI computing in the form of new Read more…

Intel Partners Debut Latest Servers Based on the New Intel Gen 3 ‘Ice Lake’ Xeons

April 7, 2021

Fresh from Intel’s launch of the company’s latest third-generation Xeon Scalable “Ice Lake” processors on April 6 (Tuesday), Intel server partners Cisco, Dell EMC, HPE and Lenovo simultaneously unveiled their first server models built around the latest chips. And though arch-rival AMD may... 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…

HPE Launches Storage Line Loaded with IBM’s Spectrum Scale File System

April 6, 2021

HPE today launched a new family of storage solutions bundled with IBM’s Spectrum Scale Erasure Code Edition parallel file system (description below) and featu Read more…

RIKEN’s Ongoing COVID Research Includes New Vaccines, New Tests & More

April 6, 2021

RIKEN took the supercomputing world by storm last summer when it launched Fugaku – which became (and remains) the world’s most powerful supercomputer – ne Read more…

CERN Is Betting Big on Exascale

April 1, 2021

The European Organization for Nuclear Research (CERN) involves 23 countries, 15,000 researchers, billions of dollars a year, and the biggest machine in the worl 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…

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…

CERN Is Betting Big on Exascale

April 1, 2021

The European Organization for Nuclear Research (CERN) involves 23 countries, 15,000 researchers, billions of dollars a year, and the biggest machine in the worl Read more…

Programming the Soon-to-Be World’s Fastest Supercomputer, Frontier

January 5, 2021

What’s it like designing an app for the world’s fastest supercomputer, set to come online in the United States in 2021? The University of Delaware’s Sunita Chandrasekaran is leading an elite international team in just that task. Chandrasekaran, assistant professor of computer and information sciences, recently was named... Read more…

HPE Launches Storage Line Loaded with IBM’s Spectrum Scale File System

April 6, 2021

HPE today launched a new family of storage solutions bundled with IBM’s Spectrum Scale Erasure Code Edition parallel file system (description below) and featu 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…

Saudi Aramco Unveils Dammam 7, Its New Top Ten Supercomputer

January 21, 2021

By revenue, oil and gas giant Saudi Aramco is one of the largest companies in the world, and it has historically employed commensurate amounts of supercomputing Read more…

Quantum Computer Start-up IonQ Plans IPO via SPAC

March 8, 2021

IonQ, a Maryland-based quantum computing start-up working with ion trap technology, plans to go public via a Special Purpose Acquisition Company (SPAC) merger a Read more…

Leading Solution Providers

Contributors

Can Deep Learning Replace Numerical Weather Prediction?

March 3, 2021

Numerical weather prediction (NWP) is a mainstay of supercomputing. Some of the first applications of the first supercomputers dealt with climate modeling, and Read more…

Livermore’s El Capitan Supercomputer to Debut HPE ‘Rabbit’ Near Node Local Storage

February 18, 2021

A near node local storage innovation called Rabbit factored heavily into Lawrence Livermore National Laboratory’s decision to select Cray’s proposal for its CORAL-2 machine, the lab’s first exascale-class supercomputer, El Capitan. Details of this new storage technology were revealed... Read more…

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

African Supercomputing Center Inaugurates ‘Toubkal,’ Most Powerful Supercomputer on the Continent

February 25, 2021

Historically, Africa hasn’t exactly been synonymous with supercomputing. There are only a handful of supercomputers on the continent, with few ranking on the Read more…

The History of Supercomputing vs. COVID-19

March 9, 2021

The COVID-19 pandemic poses a greater challenge to the high-performance computing community than any before. HPCwire's coverage of the supercomputing response t Read more…

HPE Names Justin Hotard New HPC Chief as Pete Ungaro Departs

March 2, 2021

HPE CEO Antonio Neri announced today (March 2, 2021) the appointment of Justin Hotard as general manager of HPC, mission critical solutions and labs, effective Read more…

AMD Launches Epyc ‘Milan’ with 19 SKUs for HPC, Enterprise and Hyperscale

March 15, 2021

At a virtual launch event held today (Monday), AMD revealed its third-generation Epyc “Milan” CPU lineup: a set of 19 SKUs -- including the flagship 64-core, 280-watt 7763 part --  aimed at HPC, enterprise and cloud workloads. Notably, the third-gen Epyc Milan chips achieve 19 percent... Read more…

Microsoft, HPE Bringing AI, Edge, Cloud to Earth Orbit in Preparation for Mars Missions

February 12, 2021

The International Space Station will soon get a delivery of powerful AI, edge and cloud computing tools from HPE and Microsoft Azure to expand technology experi Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire