Scientific Computing Options Maturing in the Cloud

By Agam Shah

August 31, 2023

Supercomputing remains largely an on-premises affair for many reasons that include horsepower, security, and system management. Companies need more time to move workloads to the cloud, but the options are increasing. (See the recently posted HPC-AI forecast from Intersect360 Research.)

In August, Google Cloud and Amazon Web Services announced high-performance computing virtual machines, which effectively are online versions of the computing provided by on-premises systems. The HPC VMs are built on cloud providers’ proprietary tech, including the latest processors, superfast interconnects, security features, and memory capacity.

The HPC VMs support hybrid deployments, where companies can split workloads between on-premises systems and virtual machines offered by AWS and Google. Some HPC users prefer to dispatch low-priority workloads to the cloud, which frees up on-premises computing resources to run more critical workloads.

The biggest disadvantage of HPC in the cloud remains bandwidth limitations, given the slow network speeds over large geographical distances. Nevertheless, many engineering and pharmaceutical companies are turning to the cloud because of the rich development tools, a laundry list of data sets, analytical and database tools, and other middleware available to customers. Integrators like Rescale and Altair provide software and support to create shared hybrid environments for HPC applications.

The new VMs from the cloud providers are focused square on conventional scientific computing. The systems are not targeted at AI and are not bundled with GPUs. AWS and Google offer pricey instances of Nvidia’s H100 GPUs, targeted at parallel computing and AI applications.

AWS recently announced EC2 Hpc7, which is a VM based on AMD’s fourth-generation Epyc chips code-named Genoa. Hpc7a is x86, an upgrade from the recent EC2 Hpc6a instances based on AMD’s previous-generation Epyc chips code-named Milan.

The Hpc7a has double the memory capacity in its fully loaded VM configurations and 300Gbps network bandwidth. Amazon claimed that Hpc7a provides 2.5 times faster than Hpc6a instances. The largest hpc7a.96xlarge instance offers 192 CPU cores and 768GB of DDR5 memory. The VMs support Elastic Fiber Adapter and file systems such as Lustre, which are popular in HPC.

AWS offers other HPC VMs, including the Arm-based Hpc7g, which runs on the homegrown Graviton3E chip. The Riken Center of Computational Science has built a “virtual Fugaku” for Hpc7g, or a cloud version of the software stack in Fugaku, the world’s second fastest supercomputer, on AWS. Fugaku is also built on Arm processors, making replicating the software environment possible.

Google announced the H3 VM instance for HPC in August, which balances price with performance with the help of fast network speeds and a large bevy of CPU cores.

The H3 configurations are based on Intel’s latest Sapphire Rapids CPUs, with each node aggregating 88 CPU cores and 352GB of memory. The VMs are targeted at applications that are not parallelized and are run in single-threaded environments.

The virtual machines are built on top of the Intel-Google co-developed custom data processor E2000, code-named Mount Evans. The H3 nodes can communicate at speeds of 200 Gbps and have 16 Arm-based Neoverse N1 CPU cores.

Google’s benchmarks compared the H3 to previous C2 VMs based on Intel’s Cascade Lake CPUs, which are two generations behind Sapphire Rapids. The H3 CPU-only VM is three times faster in performance-per-node and can save customers 50% in costs.

Google H3 vs C2 Performance
Google H3 vs C2 Performance for popular HPC applications.

The comparison is not an apples-to-apples as server chips are typically benchmarked to previous-generation chips, in this case, Ice Lake. But Google’s comparison is more in line with server upgrade cycles, which occur every two to three years.

At its recent Google Cloud Next summit, the company expanded its high-performance computing options for AI. The company announced pods with its latest TPU v5e AI chips and announced the general availability of its A3 supercomputing systems, which can host 26,000 Nvidia GPUs and support parallel computing. Both the chips are targeted at training and inference in AI applications.

Google Cloud’s Hugo Saleh, director of product management for HPC, answered some questions by HPCwire on the H3 and its design.

HPCwire: As a public preview, who can test H3? When will it become publicly available?

Saleh: We’ve gotten valuable feedback from select customers and partners over the last few weeks while H3 was in private preview. We announced the start of our public preview period, where any interested customer can access H3 VMs free of charge. To begin using H3 instances, customers can select H3 under the Compute Optimized machine family when creating a new VM or GKE node pool in the Google Cloud console. H3 VMs are currently available in the US-central1 (Iowa) and Europe-west4 (Netherlands) regions. Following the public preview window, general availability will be announced later this year.

HPCwire: Does Google provide help in moving HPC workloads from on-prem to the new instances?

Saleh: There are a number of options to help HPC customers on their journey to Google Cloud. We recommend connecting with Google Cloud’s HPC specialists, who can help with most questions and can bring in additional resources as needed to help with migrations. For customers needing specialized support, we also have a Professional Services organization as well as an extensive list of partners ready to help HPC users migrate their workloads from on-premises or other clouds.

HPCwire: Is real-time a priority here? HPC users care about speed, but bandwidth to deliver results over the Internet is a bottleneck.

Saleh: Google invests heavily in making access to the cloud seamless, secure, and reliable at a worldwide scale. Time to insight and results is key … which is why we have designed the H3 platform with 200 Gbps low-latency networking, twice the bandwidth of our previous generation VMs. H3 machines also support compact placements and are deployed in large, dense pools to reduce latency and network jitter, improving HPC application scalability.

HPCwire: Why are partners like Rescale.AI important? How do they connect the gap between HPC users and Google Cloud?

Saleh: PC users and their workloads span a wide spectrum of needs and tend to have a diverse set of requirements. There is already a well-established and rich ecosystem of software and services companies adept at supporting and delivering solutions to address those users’ needs. Partnering with companies like Rescale, Altair, and Parallel Works, among others, to support custom end-to-end solutions enables customers to use Google Cloud products best. In some cases, this might look like supporting a customer’s move to the cloud, optimizing for a hybrid environment, or deploying specific applications at scale. In other cases, it might be the need to support a specific operating system or scheduler that’s key to a customer’s workload and environment.

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!

Quantum Watchers – Terrific Interview with Caltech’s John Preskill by CERN

July 17, 2024

In case you missed it, there's a fascinating interview with John Preskill, the prominent Caltech physicist and pioneering quantum computing researcher that was recently posted by CERN’s department of experimental physi Read more…

Aurora AI-Driven Atmosphere Model is 5,000x Faster Than Traditional Systems

July 16, 2024

While the onset of human-driven climate change brings with it many horrors, the increase in the frequency and strength of storms poses an enormous threat to communities across the globe. As climate change is warming ocea Read more…

Researchers Say Memory Bandwidth and NVLink Speeds in Hopper Not So Simple

July 15, 2024

Researchers measured the real-world bandwidth of Nvidia's Grace Hopper superchip, with the chip-to-chip interconnect results falling well short of theoretical claims. A paper published on July 10 by researchers in the U. Read more…

Belt-Tightening in Store for Most Federal FY25 Science Budets

July 15, 2024

If it’s summer, it’s federal budgeting time, not to mention an election year as well. There’s an excellent summary of the curent state of FY25 efforts reported in AIP’s policy FYI: Science Policy News. Belt-tight Read more…

Peter Shor Wins IEEE 2025 Shannon Award

July 15, 2024

Peter Shor, the MIT mathematician whose ‘Shor’s algorithm’ sent shivers of fear through the encryption community and helped galvanize ongoing efforts to build quantum computers, has been named the 2025 winner of th Read more…

Weekly Wire Roundup: July 8-July 12, 2024

July 12, 2024

HPC news can get pretty sleepy in June and July, but this week saw a bump in activity midweek as Americans realized they still had work to do after the previous holiday weekend. The world outside the United States also s Read more…

Aurora AI-Driven Atmosphere Model is 5,000x Faster Than Traditional Systems

July 16, 2024

While the onset of human-driven climate change brings with it many horrors, the increase in the frequency and strength of storms poses an enormous threat to com Read more…

Shutterstock 1886124835

Researchers Say Memory Bandwidth and NVLink Speeds in Hopper Not So Simple

July 15, 2024

Researchers measured the real-world bandwidth of Nvidia's Grace Hopper superchip, with the chip-to-chip interconnect results falling well short of theoretical c Read more…

Shutterstock 2203611339

NSF Issues Next Solicitation and More Detail on National Quantum Virtual Laboratory

July 10, 2024

After percolating for roughly a year, NSF has issued the next solicitation for the National Quantum Virtual Lab program — this one focused on design and imple Read more…

NCSA’s SEAS Team Keeps APACE of AlphaFold2

July 9, 2024

High-performance computing (HPC) can often be challenging for researchers to use because it requires expertise in working with large datasets, scaling the softw Read more…

Anders Jensen on Europe’s Plan for AI-optimized Supercomputers, Welcoming the UK, and More

July 8, 2024

The recent ISC24 conference in Hamburg showcased LUMI and other leadership-class supercomputers co-funded by the EuroHPC Joint Undertaking (JU), including three Read more…

Generative AI to Account for 1.5% of World’s Power Consumption by 2029

July 8, 2024

Generative AI will take on a larger chunk of the world's power consumption to keep up with the hefty hardware requirements to run applications. "AI chips repres Read more…

US Senators Propose $32 Billion in Annual AI Spending, but Critics Remain Unconvinced

July 5, 2024

Senate leader, Chuck Schumer, and three colleagues want the US government to spend at least $32 billion annually by 2026 for non-defense related AI systems.  T Read more…

Point and Click HPC: High-Performance Desktops

July 3, 2024

Recently, an interesting paper appeared on Arvix called Use Cases for High-Performance Research Desktops. To be clear, the term desktop in this context does not Read more…

Atos Outlines Plans to Get Acquired, and a Path Forward

May 21, 2024

Atos – via its subsidiary Eviden – is the second major supercomputer maker outside of HPE, while others have largely dropped out. The lack of integrators and Atos' financial turmoil have the HPC market worried. If Atos goes under, HPE will be the only major option for building large-scale systems. Read more…

Everyone Except Nvidia Forms Ultra Accelerator Link (UALink) Consortium

May 30, 2024

Consider the GPU. An island of SIMD greatness that makes light work of matrix math. Originally designed to rapidly paint dots on a computer monitor, it was then 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…


Nvidia Economics: Make $5-$7 for Every $1 Spent on GPUs

June 30, 2024

Nvidia is saying that companies could make $5 to $7 for every $1 invested in GPUs over a four-year period. Customers are investing billions in new Nvidia hardwa Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst fir Read more…

AMD Clears Up Messy GPU Roadmap, Upgrades Chips Annually

June 3, 2024

In the world of AI, there's a desperate search for an alternative to Nvidia's GPUs, and AMD is stepping up to the plate. AMD detailed its updated GPU roadmap, w Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

Intel’s Next-gen Falcon Shores Coming Out in Late 2025 

April 30, 2024

It's a long wait for customers hanging on for Intel's next-generation GPU, Falcon Shores, which will be released in late 2025.  "Then we have a rich, a very Read more…

Leading Solution Providers


Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l 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…

IonQ Plots Path to Commercial (Quantum) Advantage

July 2, 2024

IonQ, the trapped ion quantum computing specialist, delivered a progress report last week firming up 2024/25 product goals and reviewing its technology roadmap. 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…

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…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh 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…

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…

  • arrow
  • Click Here for More Headlines
  • arrow