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

By Tiffany Trader

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 by Livermore Computing CTO Bronis de Supinski at the Riken-CCS International Symposium, which took place earlier this week (Jan. 15).

A 2 exaflops supercomputer slated for delivery at Livermore in late 2022 or early 2023, El Capitan is a partnership between the Department of Energy lab and HPE (which acquired Cray in 2019). As has been previously disclosed, El Capitan will be powered by AMD CPUs and GPUs as part of the Cray EX architecture (formerly known as Shasta) with Slingshot networking.

Under a non recurring engineering (NRE) contract funded by Livermore, HPE is developing near node local storage technology that it calls its Rabbit program. De Supinski explains that NRE contracts allow procurers, such as Livermore, to improve elements of the overall system, and those innovations then flow to the broader HPC market through the partnering vendor’s portfolio. 

At its core, Rabbit is a 4U solution for node local storage that encompasses 18 SSDs (16 and 2 spares) and one (AMD Epyc) storage processor. HPE refers to Rabbit as near node local storage that combined with its custom Rabbit software supports a wide range of use cases, including resolving network bursts, optimizing input, and even running analysis processes.

De Supinski described the arrangement: “You have PCI connections from each of the compute nodes into what HPE calls the Rabbit-S boards, and that gives you a PCIe network that connects you to a fairly large number of SSDs and also to a Rabbit-P board, which has a separate AMD processor located on it that allows you to interact with the storage independent of the compute blades.”

Here’s what HPE expects the Rabbit design to look like:

“If we think about how Rabbit works, this is really like building a little nest of PCIe networks within the larger system,” said de Supinski.

The 4U Rabbit box plugs into the back of the Cray XE racks, where the switch modules are housed. “Instead of populating all the switch blades, which would have given us far more interconnect bandwidth than we feel our workloads need, we’re going to populate some of those slots with Rabbit modules,” said de Supinski.

The HPE Rabbit software is of course where much of the magic happens and allows the SSDs to be accessed both as direct attached storage and network attached storage. “You can actually access the SSDs directly from the compute blade, or you can treat it as just a block storage device allocated to a specific compute blade,” said de Supinski.

Rabbit can be implemented as a “transient Lustre file system” across the compute blades. The software facilitates moving files from these block devices — these transient Lustre file systems — to the capability tier of the overall IO subsystem of El Capitan, said De Supinski. “It will automate movements between these different tiers, but they are effectively very different in how we intend to use them.”

Another important aspect of the Rabbit software is it allows customer code to be run in containers on the Rabbit processors. This ties to Livermore’s choice of resource manager for El Capitan, which is Flux, a departure from Slurm. Designed by Livermore, Flux has awareness of the local compute node relationship to the SSDs, which is not supported in Slurm, according to de Supinski.

De Supinski reviewed some of the different use cases that Rabbit will facilitate at Livermore that make sense for the lab’s IO patterns. “There’s productive output, which is really the reason we run the simulations,” he shared. “And there’s checkpoints [typically a defensive output]. Those checkpoints can be one per MPI process, or at least one per all of the processes running on a given compute node. Or they can be shared across multiple processes and frequently across all of the processes in a given job. And so that’s this N-to-N versus N-to-M checkpoint.

“What we actually see in our jobs is frequently a combination of these where they maybe have a shared file that’s accessed by all of the processes in the job and then they write the bulk of their data to a file per compute process. And then you can have things that are semi-defensive or are semi-productive. In fact, it turns out that usually our application sciences want to use a checkpoint for subsequent data analysis.”

Using Rabbit to run analysis processes is an attractive use case for Livermore, and both post facto and in transit data analysis can be done in containers running on the Rabbit processors.

Like other leading HPC centers, Livermore has experienced compute capability outpacing the ability to deploy large file systems. “The cost would make it so that basically we can’t save all those checkpoints,” said de Supinski. “We tend to do much less input than output. The input can be executables, things like the applications themselves, but also operating system files and other system software files. We have the simulation input, which may just be something as simple as the parameters of a job, or you can restart data, i.e. the checkpoints that were previously output.”

The lab anticipates using Rabbit for caching OS files in order to reduce boot time, and notes that Rabbit will be an efficient input mechanism for machine learning model training.

When Rabbit is used as network attached storage, it’s as if the NVRAM servers were directly attached to the interconnect. While that does a good job of alleviating the swamping of the storage area network, it doesn’t alleviate the problem of swamping the high performance interconnect, said de Supinski. To address this, Livermore is developing a file system called UnifyFS. “We will use the Rabbit processors to run that, use the PCIe connections to access those disks, but with a file system that gives us shared file access.”  

Livermore is planning to put one Rabbit module in every El Capitan compute chassis (each chassis houses 8 blades / 16 compute nodes). “We expect that using these Rabbit models is going to significantly reduce system interference from IO on the overall system,” said de Supinski.

Checkpointing is the lab’s top priority, but other aspects of IO are becoming more important. De Supinski believes the Rabbit modules will support these other storage workloads very well. 

“When we were evaluating the responses we got to the CORAL-2 RFP at Livermore, we found the Rabbit solution to be one of the key innovations that HPE was offering,” said de Supinski. “It was a significant factor in our choice of the Cray response for El Capitan.”

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!

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. 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. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named 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…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech 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…

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…

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…

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

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…

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…

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…

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…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire