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!

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