Strategy Behind Virtualizing GPUs

August 20, 2018

Overview

The use of graphics processing units (GPUs) to accelerate portions of general-purpose scientific and engineering applications is widespread today. However, the adoption for running GPU-based high performance computing (HPC) and artificial intelligence jobs is limited due to the high acquisition cost, high power consumption and low utilization of GPUs. Typically, applications can only access GPUs located within the local node where they are being executed which limits their usage. In addition, the sharing of GPUs is not considered by job schedulers such as SLURM that may be used for HPC or AI compute runs.

In today’s datacenter environment, the ability to leverage system resources, especially that of GPUs needs to be more flexible.  An ideal solution is sharing the notably expensive, power hungry GPUs among several nodes in a cluster as part of a virtualization solution. Virtualizing and sharing GPUs efficiently addresses several concerns including maximizing utilization across remote GPU resources for either or HPC and AI workloads.

The GPU virtualization middleware solution from rCUDA (remote CUDA) solves these issues by turning GPUs into virtual compute resources when running on networks with underlying high-performance networking technologies such as Mellanox InfiniBand®. According to Dr. Federico Silla, Associate Professor at the Department of Computer Engineering and rCUDA team leader, from Universitat Politècnica de València (Technical University of Valencia) in Spain, “Sharing GPUs among nodes in the cluster by remotely virtualizing them is a powerful mechanism that can provide important energy savings while at the same time the overall cluster throughput (in terms of jobs completed per time unit) is noticeably increased. Furthermore, it is possible to provide differentiated quality service levels to customers paying different fees. rCUDA is a modern tool that adds value to the GPUs in your cluster.”

Introducing rCUDA

The rCUDA framework was developed by Universitat Politècnica de València (Spain). rCUDA is a middleware product that enables remote virtualization of GPUs. With rCUDA, physical GPUs are installed only in some of the nodes of the cluster, and they are transparently shared among all the nodes. Nodes equipped with GPUs provide GPU services to all of the nodes within the cluster.

Benefits of running rCUDA in the data center

  • Energy savings
  • rCUDA improves GPU utilization and makes GPU usage more efficient and flexible, allowing up to 100% of available GPU capacity
  • More GPUs are available for a single application
  • Using rCUDA does not mean a drop or reduced performance (on average, the overhead of using rCUDA is negligible when InfiniBand or RDMA over Converged Ethernet (RoCE) are leveraged)
  • Same fabric, no special network is needed
  • rCUDA is transparent to applications (source code of NVIDIA CUDA® applications does not need to be modified)
  • rCUDA is not tied to a specific processor architecture
  • GPU virtualization enables cluster configurations with a reduced number of GPUs reducing the costs associated with the use of GPUs

How does rCUDA work?

While NVIDIA’s CUDA® platform is limited to interact with GPUs that are physically installed in the node where the application is being executed, remote GPU virtualization frameworks follow a client-server distributed approach. With rCUDA, applications are not limited to local GPUs, and can leverage any GPU in the cluster— this is known as remote GPU virtualization. The client part of the rCUDA middleware is installed in the cluster node that is executing the application which is requesting GPU services. The server side runs on the system owning the actual GPU. When the client receives a CUDA request from an accelerated application, it processes it and forwards it to the remote server. The server node receives the request and forwards it to the GPU, which completes the execution of the request and provides the associated results back to the server which is executing the application process as shown in Figure 1.

Figure 1. rCUDA architecture allows applications to use GPUs across the network

Applications do not need to be modified to use rCUDA. However, applications must be linked to the rCUDA libraries instead of the CUDA libraries. rCUDA then decouples GPUs from the nodes where they are installed and creates a GPU clustering environment with multiple GPUs which provide services to multiple local or remote compute systems. Clustered GPUs can be transparently shared by any of the nodes in the facility.

rCUDA runs on many systems and applications

Mellanox InfiniBand and RoCE enabled solutions have native RDMA engines which are supported across system architectures and can easily implement rCUDA functionality. Because rCUDA is also not tied to a specific processor architecture —it can run on a variety of systems including x86, ARM, and IBM POWER processors as shown in Figure 2.

Figure 2. rCUDA can run on all major system architectures

“In addition, rCUDA has successfully run on popular GPU and HPC applications such as BARRACUDA, CUDAmeme, GPUBlast, GPU-LIBSVM, Gromacs, LAMMPS, MAGMA and NAMD. Deep learning frameworks are also supported. rCUDA has been successfully run with TensorFlow version 1.7, Caffe, Torch, Theano, PyTorch and MXNET. Finally, renderers such as Blender and Octane are also supported,” states Silla.

How Mellanox integrates with rCUDA

Mellanox Technologies is a leading supplier of end-to-end Ethernet and Mellanox InfiniBand® intelligent interconnect solutions and services for servers, storage, and hyper-converged infrastructure. Mellanox intelligent interconnect solutions increase data center efficiency by providing the highest throughput and lowest latency, delivering data faster to applications and unlocking system performance. Because Mellanox InfiniBand is based on open standards, its non-proprietary solutions easily integrate with all middleware technologies, including rCUDA’s innovative GPU virtualization technology.

Previously, virtualized GPUs were impaired by the low bandwidth of the underlying network. However, there is a negligible performance impact when running rCUDA on a high-performance network fabric such as Mellanox InfiniBand— execution time is usually increased by less than 4% when a high performance network fabric is used. “By taking full advantage of the native RMDA engines, the high bandwidth and ultra-low latency of Mellanox InfiniBand, rCUDA provides near-native performance to applications using any remote GPU,” states Scot Schultz, Sr. Director, HPC / Artificial Intelligence and Technical Computing – Mellanox Technologies.

Summary

Until recently, GPU usage for HPC and AI processing has been limited because native CUDA software could only use GPUs that were physically installed in the node where the application is executed. rCUDA’s virtualized middleware installed on systems using Mellanox’s high bandwidth networking architecture allows GPUs to be shared among all the nodes from the entire cluster rather than limiting the user application to a single node’s local GPUs. rCUDA also provides significant energy and cost savings with negligible impact to application performance.

According to Silla, “Different remote GPU virtualization solutions provide varying performance values when used across clusters. Therefore, you have to try remote GPU virtualization by yourself in your cluster to draw your own conclusions. Do not accept demos carried out in clusters other than your own. Unlike other solutions, you can try rCUDA in your cluster to prove its value in your system.”

 

Mellanox

 

 

 

rCUDA

 

 

 

References

rCUDA slides: http://www.rcuda.net/pub/rCUDA_isc18.pdf

rCUDA technical paper: https://dl.acm.org/citation.cfm?id=2830015

http://www.mellanox.com

 

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Graphcore Introduces Next-Gen Intelligence Processing Unit for AI Workloads

July 15, 2020

British hardware designer Graphcore, which emerged from stealth in 2016 to launch its first-generation Intelligence Processing Unit (IPU), has announced its next-generation IPU platform: the IPU-Machine M2000. With the n Read more…

By Oliver Peckham

heFFTe: Scaling FFT for Exascale

July 15, 2020

Exascale computing aspires to provide breakthrough solutions addressing today’s most critical challenges in scientific discovery, energy assurance, economic competitiveness, and national security. This has been the mai Read more…

By Jack Dongarra and Stanimire Tomov

There’s No Storage Like ATGC: Breakthrough Helps to Store ‘The Wizard of Oz’ in DNA

July 15, 2020

Even as storage density reaches new heights, many researchers have their eyes set on a paradigm shift in high-density information storage: storing data in the four nucleotides (A, T, G and C) that constitute DNA, a metho Read more…

By Oliver Peckham

Get a Grip: Intel Neuromorphic Chip Used to Give Robotics Arm a Sense of Touch

July 15, 2020

Moving neuromorphic technology from the laboratory into practice has proven slow-going. This week, National University of Singapore researchers moved the needle forward demonstrating an event-driven, visual-tactile perce Read more…

By John Russell

What’s New in HPC Research: Volcanoes, Mobile Games, Proteins & More

July 14, 2020

In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

AWS Solution Channel

INEOS TEAM UK Accelerates Boat Design for America’s Cup Using HPC on AWS

The America’s Cup Dream

The 36th America’s Cup race will be decided in Auckland, New Zealand in 2021. Like all the teams, INEOS TEAM UK will compete in a boat whose design will have followed guidelines set by race organizers to ensure the crew’s sailing skills are fully tested. Read more…

Intel® HPC + AI Pavilion

Supercomputing the Pandemic: Scientific Community Tackles COVID-19 from Multiple Perspectives

Since their inception, supercomputers have taken on the biggest, most complex, and most data-intensive computing challenges—from confirming Einstein’s theories about gravitational waves to predicting the impacts of climate change. Read more…

Joliot-Curie Supercomputer Used to Build First Full, High-Fidelity Aircraft Engine Simulation

July 14, 2020

When industrial designers plan the design of a new element of a vehicle’s propulsion or exterior, they typically use fluid dynamics to optimize airflow and increase the vehicle’s speed and efficiency. These fluid dyn Read more…

By Oliver Peckham

Graphcore Introduces Next-Gen Intelligence Processing Unit for AI Workloads

July 15, 2020

British hardware designer Graphcore, which emerged from stealth in 2016 to launch its first-generation Intelligence Processing Unit (IPU), has announced its nex Read more…

By Oliver Peckham

heFFTe: Scaling FFT for Exascale

July 15, 2020

Exascale computing aspires to provide breakthrough solutions addressing today’s most critical challenges in scientific discovery, energy assurance, economic c Read more…

By Jack Dongarra and Stanimire Tomov

Get a Grip: Intel Neuromorphic Chip Used to Give Robotics Arm a Sense of Touch

July 15, 2020

Moving neuromorphic technology from the laboratory into practice has proven slow-going. This week, National University of Singapore researchers moved the needle Read more…

By John Russell

Max Planck Society Begins Installation of Liquid-Cooled Supercomputer from Lenovo

July 9, 2020

Lenovo announced today that it is supplying a new high performance computer to the Max Planck Society, one of Germany's premier research organizations. Comprise Read more…

By Tiffany Trader

President’s Council Targets AI, Quantum, STEM; Recommends Spending Growth

July 9, 2020

Last week the President Council of Advisors on Science and Technology (PCAST) met (webinar) to review policy recommendations around three sub-committee reports: Read more…

By John Russell

Google Cloud Debuts 16-GPU Ampere A100 Instances

July 7, 2020

On the heels of the Nvidia’s Ampere A100 GPU launch in May, Google Cloud is announcing alpha availability of the A100 “Accelerator Optimized” VM A2 instance family on Google Compute Engine. The instances are powered by the HGX A100 16-GPU platform, which combines two HGX A100 8-GPU baseboards using... Read more…

By Tiffany Trader

Q&A: HLRS’s Bastian Koller Tackles HPC and Industry in Germany and Europe

July 6, 2020

In this exclusive interview for HPCwire – sadly not face to face – Steve Conway, senior advisor for Hyperion Research, talks with Dr.-Ing Bastian Koller about the state of HPC and its collaboration with Industry in Europe. Koller is a familiar figure in HPC. He is the managing director at High Performance Computing Center Stuttgart (HLRS) and also serves... Read more…

By Steve Conway, Hyperion

OpenPOWER Reboot – New Director, New Silicon Partners, Leveraging Linux Foundation Connections

July 2, 2020

Earlier this week the OpenPOWER Foundation announced the contribution of IBM’s A21 Power processor core design to the open source community. Roughly this time Read more…

By John Russell

Supercomputer Modeling Tests How COVID-19 Spreads in Grocery Stores

April 8, 2020

In the COVID-19 era, many people are treating simple activities like getting gas or groceries with caution as they try to heed social distancing mandates and protect their own health. Still, significant uncertainty surrounds the relative risk of different activities, and conflicting information is prevalent. A team of Finnish researchers set out to address some of these uncertainties by... Read more…

By Oliver Peckham

[email protected] Turns Its Massive Crowdsourced Computer Network Against COVID-19

March 16, 2020

For gamers, fighting against a global crisis is usually pure fantasy – but now, it’s looking more like a reality. As supercomputers around the world spin up Read more…

By Oliver Peckham

[email protected] Rallies a Legion of Computers Against the Coronavirus

March 24, 2020

Last week, we highlighted [email protected], a massive, crowdsourced computer network that has turned its resources against the coronavirus pandemic sweeping the globe – but [email protected] isn’t the only game in town. The internet is buzzing with crowdsourced computing... Read more…

By Oliver Peckham

Supercomputer Simulations Reveal the Fate of the Neanderthals

May 25, 2020

For hundreds of thousands of years, neanderthals roamed the planet, eventually (almost 50,000 years ago) giving way to homo sapiens, which quickly became the do Read more…

By Oliver Peckham

DoE Expands on Role of COVID-19 Supercomputing Consortium

March 25, 2020

After announcing the launch of the COVID-19 High Performance Computing Consortium on Sunday, the Department of Energy yesterday provided more details on its sco Read more…

By John Russell

Neocortex Will Be First-of-Its-Kind 800,000-Core AI Supercomputer

June 9, 2020

Pittsburgh Supercomputing Center (PSC - a joint research organization of Carnegie Mellon University and the University of Pittsburgh) has won a $5 million award Read more…

By Tiffany Trader

Honeywell’s Big Bet on Trapped Ion Quantum Computing

April 7, 2020

Honeywell doesn’t spring to mind when thinking of quantum computing pioneers, but a decade ago the high-tech conglomerate better known for its control systems waded deliberately into the then calmer quantum computing (QC) waters. Fast forward to March when Honeywell announced plans to introduce an ion trap-based quantum computer whose ‘performance’ would... Read more…

By John Russell

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

By Doug Black

Leading Solution Providers

Contributors

Nvidia’s Ampere A100 GPU: Up to 2.5X the HPC, 20X the AI

May 14, 2020

Nvidia's first Ampere-based graphics card, the A100 GPU, packs a whopping 54 billion transistors on 826mm2 of silicon, making it the world's largest seven-nanom Read more…

By Tiffany Trader

‘Billion Molecules Against COVID-19’ Challenge to Launch with Massive Supercomputing Support

April 22, 2020

Around the world, supercomputing centers have spun up and opened their doors for COVID-19 research in what may be the most unified supercomputing effort in hist Read more…

By Oliver Peckham

Australian Researchers Break All-Time Internet Speed Record

May 26, 2020

If you’ve been stuck at home for the last few months, you’ve probably become more attuned to the quality (or lack thereof) of your internet connection. Even Read more…

By Oliver Peckham

15 Slides on Programming Aurora and Exascale Systems

May 7, 2020

Sometime in 2021, Aurora, the first planned U.S. exascale system, is scheduled to be fired up at Argonne National Laboratory. Cray (now HPE) and Intel are the k Read more…

By John Russell

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

TACC Supercomputers Run Simulations Illuminating COVID-19, DNA Replication

March 19, 2020

As supercomputers around the world spin up to combat the coronavirus, the Texas Advanced Computing Center (TACC) is announcing results that may help to illumina Read more…

By Staff report

$100B Plan Submitted for Massive Remake and Expansion of NSF

May 27, 2020

Legislation to reshape, expand - and rename - the National Science Foundation has been submitted in both the U.S. House and Senate. The proposal, which seems to Read more…

By John Russell

John Martinis Reportedly Leaves Google Quantum Effort

April 21, 2020

John Martinis, who led Google’s quantum computing effort since establishing its quantum hardware group in 2014, has left Google after being moved into an advi Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This