Extreme Scale HPC: How Western Digital Corporation leveraged the virtually unlimited HPC capacity on AWS in their quest to speed up innovation and build better products

By Bala Thekkedath - Global HPC Marketing Lead, Amazon Web Services

December 10, 2018

Recently, AWS and Western Digital embarked on a very fun, challenging project of evaluating the impact of running their electro-magnetic simulations on a massive HPC cluster built on AWS using Amazon EC2 Spot Instances.   The lessons we learned and the results we were able to prove are very interesting and I am excited to share a quick overview here.

One of the biggest advantages of moving your HPC workloads to AWS is the ability to achieve extreme scales in terms of capacity and configurations – without a lot of upfront investment and heartache over long term commitments.  If you work for an organization that has moved HPC workloads to the cloud or has at least started the process by bursting to the cloud when demand spikes, you have experienced the agility and flexibility benefits afforded by the cloud.  You either have an individual account to access and request resources in the cloud or you request it via your HPC admin.  In both cases, you start building “your” cluster when you are ready. In most cases the cluster is built automatically by your job scheduler as you submit your jobs, and compute resources are ready within minutes. When the jobs are done, you shut down your cluster and stop paying for it.  When you request your cluster, unlike your on-premises environment, you can specify what type of CPUs (or GPUs, or FPGAs) you would like to run a particular application on.  Ever wonder how much faster your application would run if you had the latest CPU or GPU?  What if you wanted to determine if an I/O bandwidth optimized configuration versus CPU was better for parts of your workflow?   Well, now you can try many different configuration types without going through a cumbersome procurement process.  It becomes incredibly easy to fine tune specific portions of your HPC workflow, given the many different instance types available, and how easy it is to drop them into a workflow.   Then, there is the scale.   It does not matter if you request 1,000 cores for 8-hours or 8,000 cores for 1-hour.  You still pay the same.   So, if your application supports it, why not scale up your resources and get to results faster?

That is exactly what a recent collaborative project between AWS and Western Digital did.  First, a quick overview of the hard disk drive (HDD) market.  The HDD market is an extremely competitive one.  The ever-increasing demand for capacity from enterprises, particularly large hyper-scale data centers (like us) has been keeping Western Digital very busy.  Faced with the need to innovate to meet the growing demand for data storage capacity, the engineering teams at Western Digital are always pushing the limits of physics and engineering.  Enterprise HDDs are still confined to a 3.5 inches form factor (as they have been for years) with no chance to increase the size to accommodate additional capacity and performance requirements.  So, the only solution to meeting the increased capacity demands is to cram more bits into the same space and make sure the drives can handle the increasing demands for performance.  The technical term here is increasing the areal density of the media – meaning, keep on shrinking the geometry that you are allowed to use to capture the ones and zeros on the rotating media.  As you shrink those geometries, there are various aspects of cross talk, noise and atomic behavior that you have to comprehend to get to an ingredient combination that works 24x7x365, and can be manufactured at high volume. It is quite an art and science to get all those things to line up exactly, make it repeatable, make it manufacturable, make it operational, and, oh, by the way, get it to work for years without a failure.

A big focus of the engineering simulations work at Western Digital is to evaluate different combinations of technologies and/or solutions (or ingredients that make up the solutions) that goes into making new HDDs.  The basic design of the hard disk involves a rotating media and a head on a slider arm that moves over the media.  The engineering teams are looking at smaller and smaller geometries of recording channels on the media so they can fit more and more 1’s and 0’s or bits into the same space.  They are looking to achieve faster read and write times from that media.  The simulations thus involve many variable vectors to find the right combination of media, speed of rotation of the media, materials that constitute the media etc. that can provide that higher density and faster read-write times.   The end goal is to determine which combinations work and which don’t – and making sure those combinations that don’t work are avoided in the manufacturing process or in solutions/component recipes for the physical products.

As part of this precedent-setting collaborative work, Western Digital ran around 2.3 million simulation jobs on a Spot-based cluster of a little over one million vCPUs.   If they were to do those same 2.3 million simulations on a standard Spot based cluster of 16,000 vCPUs at a time (as they do today), it would have taken them about 20 days to get the same work done.     The idea of doing 20 days of work in 8 hours is a game changer.  The impacts go beyond the traditional business metrics – it is a great competitive advantage for a business that is driven by innovation.

So, what goes into scaling an application to run on extreme capacity infrastructure?  It is a coordinated effort between the application engineers, the infrastructure engineers, and the team at AWS.    At a 10K ft level, what we are doing here is taking a large statistical simulation, splitting it into jobs that run on a single vCPU, then when the jobs are done, bringing it all back and collating the results.   That requires work on both the application side and the infrastructure side.  The application has to ensure that the individual simulations are all done correctly, the infrastructure has to coordinate jobs across a vast number of servers/cores and bring all the data back to collate. What made this run even more interesting is that we used EC2 Spot instances, so the application had to be resilient for any job preemption or interruption that might happen. During the 8 hours run at the full one million vCPU scale, we experienced less than 1% of interruption. From an infrastructure point of view, we had to evaluate the limits that exists on number of underlying services (compute, storage, API calls) and since this was a cluster that was run all in a single region, but spanned multiple Availability Zones, we combined the features of AWS Spot Fleet with the highly-scalable cluster management and scheduling of Univa NavOps and GridEngine to coordinate cluster management across the wide capacity of our infrastructure and keep the cluster fully utilized even under such very high workload.

A few other points that are worth highlighting here.  First, Western Digital, Univa and AWS were able to fully exploit the configuration flexibility that running HPC workloads on the cloud offers.  Before embarking on this simulation, the engineers from both AWS and Western Digital spent a lot of prep time profiling the various instance types that Amazon EC2 offers. Through profiling this multitude of instance types (over 25 different instances types), we were able to land on the most optimal range of instances offering AVX acceleration for this workload, giving the AWS Spot Fleet the flexibility and freedom to find the cheapest and fastest hardware for the job.   Second, this simulation was also a major achievement in terms of the use of containers to run HPC workloads.  In this run, the entire application was ported onto containers, which is a big shift from having to haul around drivers and dependencies across jobs and VMs.   This run actually might have been one of the largest container fleets running a single application! Third, we used Amazon Simple Storage Service (Amazon S3) as the storage back-end for this simulation.  Being able to support this fast rate of data access at such massive scales required no tuning effort, as S3 bandwidth scaled gracefully and peaked at 7500 PUT/s.  And last, but not the least, this was a great example of how Spot Fleet can simplify cluster management.  In this particular case, we just had three Spot Fleet requests simultaneously and we were able to hit a million cores in the cluster in around 1 hour and 32 minutes!

To learn more, visit https://aws.amazon.com/hpc or reach out to your local AWS representative.

 

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!

U.S. Quantum Director Charles Tahan Calls for NQIA Reauthorization Now

February 29, 2024

(February 29, 2024) Origin stories make the best superhero movies. I am no superhero, but I still remember what my undergraduate thesis advisor said when I told him that I wanted to design quantum computers in graduate s Read more…

pNFS Provides Performance and New Possibilities

February 29, 2024

At the cusp of a new era in technology, enterprise IT stands on the brink of the most profound transformation since the Internet's inception. This seismic shift is propelled by the advent of artificial intelligence (AI), Read more…

Celebrating 35 Years of HPCwire by Recognizing 35 HPC Trailblazers

February 29, 2024

In 1988, a new IEEE conference debuted in Orlando, Florida. The planners were expecting 200-300 attendees because the conference was focused on an obscure topic called supercomputing, but when it was announced that S Read more…

Forrester’s State of AI Report Suggests a Wave of Disruption Is Coming

February 28, 2024

The explosive growth of generative artificial intelligence (GenAI) heralds opportunity and disruption across industries. It is transforming how we interact with technology itself. During this early phase of GenAI technol Read more…

Q-Roundup: Google on Optimizing Circuits; St. Jude Uses GenAI; Hunting Majorana; Global Movers

February 27, 2024

Last week, a Google-led team reported developing a new tool - AlphaTensor Quantum - based on deep reinforcement learning (DRL) to better optimize circuits. A week earlier a team working with St. Jude Children’s Hospita Read more…

AWS Solution Channel

Shutterstock 2283618597

Deep-dive into Ansys Fluent performance on Ansys Gateway powered by AWS

Today, we’re going to deep-dive into the performance and associated cost of running computational fluid dynamics (CFD) simulations on AWS using Ansys Fluent through the Ansys Gateway powered by AWS (or just “Ansys Gateway” for the rest of this post). Read more…

Argonne Aurora Walk About Video

February 27, 2024

In November 2023, Aurora was ranked #2 on the Top 500 list. That ranking was with half of Aurora running the HPL benchmark. It seems after much delay, 2024 will finally be Aurora's time in the spotlight. For those cur Read more…

Royalty-free stock illustration ID: 1988202119

pNFS Provides Performance and New Possibilities

February 29, 2024

At the cusp of a new era in technology, enterprise IT stands on the brink of the most profound transformation since the Internet's inception. This seismic shift Read more…

Celebrating 35 Years of HPCwire by Recognizing 35 HPC Trailblazers

February 29, 2024

In 1988, a new IEEE conference debuted in Orlando, Florida. The planners were expecting 200-300 attendees because the conference was focused on an obscure t Read more…

Forrester’s State of AI Report Suggests a Wave of Disruption Is Coming

February 28, 2024

The explosive growth of generative artificial intelligence (GenAI) heralds opportunity and disruption across industries. It is transforming how we interact with Read more…

Q-Roundup: Google on Optimizing Circuits; St. Jude Uses GenAI; Hunting Majorana; Global Movers

February 27, 2024

Last week, a Google-led team reported developing a new tool - AlphaTensor Quantum - based on deep reinforcement learning (DRL) to better optimize circuits. A we Read more…

South African Cluster Competition Team Enjoys Big Texas HPC Adventure

February 26, 2024

Texas A&M University's High-Performance Research Computing (HPRC) hosted an elite South African delegation on February 8 - undergraduate computer science (a Read more…

A Big Memory Nvidia GH200 Next to Your Desk: Closer Than You Think

February 22, 2024

Students of the microprocessor may recall that the original 8086/8088 processors did not have floating point units. The motherboard often had an extra socket fo Read more…

Apple Rolls out Post Quantum Security for iOS

February 21, 2024

Think implementing so-called Post Quantum Cryptography (PQC) isn't important because quantum computers able to decrypt current RSA codes don’t yet exist? Not Read more…

QED-C Issues New Quantum Benchmarking Paper

February 20, 2024

The Quantum Economic Development Consortium last week released a new paper on benchmarking – Quantum Algorithm Exploration using Application-Oriented Performa Read more…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N 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 Wins SC23, But Gets Socked by Microsoft’s AI Chip

November 16, 2023

Nvidia was invisible with a very small booth and limited floor presence, but thanks to its sheer AI dominance, it was a winner at the Supercomputing 2023. Nv 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…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y Read more…

Royalty-free stock illustration ID: 1675260034

RISC-V Summit: Ghosts of x86 and ARM Linger

November 12, 2023

Editor note: See SC23 RISC-V events at the end of the article At this year's RISC-V Summit, the unofficial motto was "drain the swamp," that is, x86 and Read more…

China Deploys Massive RISC-V Server in Commercial Cloud

November 8, 2023

If the U.S. government intends to curb China's adoption of emerging RISC-V architecture to develop homegrown chips, it may be getting late. Last month, China 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…

Leading Solution Providers

Contributors

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…

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…

Chinese Company Developing 64-core RISC-V Chip with Tech from U.S.

November 13, 2023

Chinese chip maker SophGo is developing a RISC-V chip based on designs from the U.S. company SiFive, which highlights challenges the U.S. government may face in 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…

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…

Royalty-free stock illustration ID: 1182444949

Forget Zettascale, Trouble is Brewing in Scaling Exascale Supercomputers

November 14, 2023

In 2021, Intel famously declared its goal to get to zettascale supercomputing by 2027, or scaling today's Exascale computers by 1,000 times. Moving forward t 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…

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…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire