Quobyte First Distributed File System with TensorFlow Plug-in to Enhance Machine Learning Capabilities

April 16, 2019

SANTA CLARA, Calif., April 16, 2019 — Quobyte Inc., a leading developer of modern storage system software, today announced that the Quobyte Data Center File System is the first distributed file system to offer a TensorFlow plug-in, providing increased throughput performance and linear scalability for ML-powered applications to enable faster training across larger data sets while achieving higher-accuracy results.

TensorFlow is an open source library for numerical computation and large-scale machine learning used across industries such as autonomous vehicles, robotics, financial services, healthcare, government, aerospace, defense, and many others. Using Quobyte storage with TensorFlow helps to simplify and streamline the operation of machine learning.

Quobyte’s TensorFlow File System Plug-in allows TensorFlow applications to talk directly to Quobyte, bypassing the operating system kernel to significantly reduce kernel mode context switches and lower CPU usage. While Quobyte storage can be used with all stages of ML, the resulting increased GPU utilization from the TensorFlow plug-in speeds up model training of ML workflows.

Quobyte provides users the flexibility to train anywhere and seamlessly move models into production to better support ML workloads from the data center to the cloud to the edge. The TensorFlow plug-in can be used to train models locally on sample data sets and use the Google Cloud Platform for training at scale because Quobyte runs on-prem and in the cloud. Additionally, because it bypasses the kernel entirely, Quobyte’s TensorFlow plug-in works with both current and older versions of Linux, providing a full range of flexible deployment options for use in ML. Using the Quobyte TensorFlow plug-in is seamless since there are no application modifications required.

“As more and more businesses look to leverage ML to increase innovation, achieve a faster time to market and provide a more positive customer experience, there is an increasing need for storage infrastructures that offer higher performance and increased flexibility that these workloads need,” said Frederic Van Haren, Lead Analyst HPC and AI Systems of analyst firm Evaluator Group. “Vendors, like Quobyte, that offer high performance, broad platform support and flexibility of deployment options are well positioned to help companies handle bigger data sets, achieve more accurate results and run ML workloads in any environment.”

With Quobyte, there is no need for specialized storage systems to get the most out of ML. Quobyte is a single storage system that addresses many different performance profiles, including the high-throughput, low-latency requirement of ML’s model training stage, as well as large block sequential, small block random or mixed general workloads. Quobyte supports the broadest set of access protocols and clients, such as S3, Linux, Hadoop, Windows and NFS for greater platform flexibility and more complete data ingest and preparation. Data is readily available at any stage all within a single global namespace and all managed through Quobyte’s intuitive management console.

Additional benefits of Quobyte’s TensorFlow File System Plug-in include:

  • The ability to leverage HDD and SSD to get the best price-performance ratio without cumbersome tiering
  • Prefetching of training data can deliver substantial performance improvement. Much machine-generated data uses a sequential naming convention that makes it ideal for prefetching.
  • Infinite scalability that allows users to grow storage in terms of throughput and capacity when they need it. As ML project requirements change – oftentimes more quickly than anticipated – the Quobyte installation will adapt. Disks or servers can be quickly and easily added when needed to provide more capacity or performance without any interruption to applications or services.
  • Multi-tenancy that provides additional security by allowing users to define isolated namespaces and physical separation of data/workloads inside the same cluster. Administrators can further isolate tenants by controlling to which physical hardware they have access in order to ensure performance and that data is not accessible to any unauthorized users on the network.

“By providing the first distributed file system with a TensorFlow plug-in, we are ensuring as much as a 30 percent faster throughput performance improvement for ML training workflows, helping companies better meet their business objectives through improved operational efficiency,” said Bjorn Kolbeck, Quobyte CEO. “With the higher accuracy of results, scalability to handle bigger data sets and flexibility to run on-prem to the cloud, and edge, we believe we are providing an optimal experience that allows customers to fully leverage the value of their Machine Learning infrastructure investments.”

About Quobyte

Building on a decade of research and experience with the open-source distributed file system XtreemFS and from working on Google’s infrastructure, Quobyte delivers on the promise of software-defined storage for the world’s most demanding application environments including High Performance Computing (HPC), Machine Learning (ML), Media & Entertainment (M&E), Life Sciences, Financial Services, and Electronic Design Automation (EDA). Quobyte uniquely leverages hyperscaler parallel distributed file system technologies to unify file, block, and object storage. This allows customers to easily replace storage silos with a single, scalable storage system — significantly saving manpower, money, and time spent on storage management. Quobyte allows companies to scale storage capacity and performance linearly on commodity hardware while eliminating the need to expand administrative staff through the software’s ability to self-monitor, self-maintain, and self-heal. Please visit www.quobyte.com for more information.


Source: Quobyte

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