Super-Connecting the Supercomputers

By Gilad Shainer, Mellanox Technologies

June 10, 2019

Supercomputers are the essential tools we need to conduct research, enable scientific discoveries, design new products, and develop self-learning software algorithms. Supercomputing leadership means scientific leadership, which explains the investments made by many governments and research institutes to build faster and more powerful supercomputing platforms.

The heart of a supercomputer is the network that connects the compute elements together, enabling parallel and synchronized computing cycles. Over the past decades, multiple network technologies were created and multiple have disappeared. InfiniBand, an industry standard developed in 1999, continues to show a strong presence in the high-performance computing market. It connected one of the top three supercomputers in 2013 and maintains a strong roadmap into the future.

Many proprietary networks that existed 10 and 15 years ago are no longer in use today; QsNet, Myrinet, Seastar are but a few examples. QSNet technology was later used by Gnodal, which added Ethernet gateways to form an Ethernet switch network, but its development was halted several years ago. Part of that technology and concept is being used in the first generation of Slingshot. Slingshot is planned to replace a former proprietary Aries technology, which replaced Gemini proprietary technology, which replaced Seastar. One of the main disadvantages of a proprietary network is that it requires recreating old concepts again and again—concepts such as congestion control, routing schemes and more.

Being a standard-based interconnect, InfiniBand enjoys the continuous development of new capabilities, better performance, and scalability. It is used in many of the leading supercomputers around the world, demonstrating 96% network utilization with probably the most advanced adaptive routing capabilities (“The Design, Deployment, and Evaluation of the CORAL Pre-Exascale Systems”), and delivering leading performance for the most demanding high compute intensive applications.

InfiniBand technology can be separated into three main pillars: connectivity, network, and communication. The connectivity pillar refers to the elements around the interconnect infrastructure such as topologies. The network pillar refers to the network transport and routing for example. And the communication pillar refers to co-design elements related to communication frameworks such as MPI, SHMEM/PGAS and more.

The Connectivity Pillar

InfiniBand was specified and designed as the ultimate software-defined network. One can define and manage complete routing schemes of the network from a centralized place, and everything is programmable. This advantage enables support for any interconnect topology and optimizes topologies to best fit the applications and workloads needs. Many of today’s supercomputers use the Fat Tree topology as it provides low latency and effectively supports a variety of applications. There are some Torus topologies in use, which best serve stencil applications. Other topologies including Hypercube, Enhanced Hypercube, Dragonfly+ and more are coming in the future.

Dragonfly+ is hybrid topology based on the conventional Dragonfly and extended using the properties of Fat Tree providing the benefit of both. It includes a Fully Progressive Adapting Routing technique, is more scalable than Dragonfly at the same cost, and able to provide the same or better throughput for equivalent Dragonfly and Fat Tree topologies under various traffic patterns (“Dragonfly+: Low Cost Topology for Scaling Datacenters,” Alexander Shpiner, Zachy Haramaty, Saar Eliad, Vladimir Zdornov, Barak Gafni and Eitan Zahavi).

Furthermore, the traditional Dragonfly presents performance limitations for adversarial traffic, as within a group, there is only one route from ingress switch to egress switch. Therefore, network bandwidth decreases with higher switch radix. The more ports on the switch, the lower the data throughput. InfiniBand Dragonfly+ includes multiple routes from ingress switch to egress switch, thereby delivering the highest data throughput. Moreover, due to its hybrid design, Dragonfly+ can simply be extended over time with no need to reroute any of the long cables—an advantage over Fat Trees and traditional Dragonfly networks.

Multi-Host technology enables multiple hosts to connect to a single interconnect adapter by separating the PCIe interface into multiple and independent interfaces, with no performance degradation. This results in lower total cost of ownership (TCO) in the data center by reducing CAPEX requirements from multiple cables, network adapters, and switch ports to only one of each, and by reducing OPEX by cutting down on switch port management and overall power usage.

The Network Pillar

InfiniBand is a pure offload interconnect, managing all network function and transport at the network level, and not imposing overheads on the CPU as other networks such as Ethernet or OmniPath. This results in more CPU cycles being dedicated to the applications and higher overall performance and scalability.

In many networks, a management software utility is responsible for receiving notifications of network errors and in order to modify network routes or change job scheduling to avoid the errors. But this can be time consuming—around 5 seconds for 1000 nodes and 30 seconds for clusters with 10000 or more endpoints—certainly not fast enough to ensure the seamless integrity of a running computation. In fact, no software mechanism can be responsive enough at very large scales to detect and fix fabrics that suffer from a link failure. To address this problem, InfiniBand includes a new and innovative solution called SHIELD (Self-Healing Interconnect Enhancement for Intelligent Datacenters), which takes advantage of the intelligence already built into InfiniBand switches. By providing the fabric with self-healing autonomy, the speed with which communications can be corrected in the face of a link failure can be sped up by 5000x. This is fast enough to save communications from expensive retransmissions or absolute failure.

The Communication Pillar

Mellanox Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)™ technology is included in the EDR and HDR InfiniBand switches. SHARP improves upon the performance of MPI operation by offloading collective operations from the CPU to the switch network, and eliminating the need to send data multiple times between endpoints. This innovative approach decreases the amount of data traversing the network as aggregation nodes are reached, and dramatically reduces the MPI operations time. Implementing collective communication algorithms in the network also has additional benefits, such as freeing up valuable CPU resources for computation rather than using them to process communication.

SHARP provides lower and flat latencies for data aggregation and reduction operations (e.g., MPI Reduce, All-Reduce, Barrier, Broadcast, etc.) compared to other options, so adding more nodes to compute clusters does not adversely affect. SHARP is also the best technology to enable the Exascale supercomputing generation.

Furthermore, SHARP provides key performance enhancement for deep learning and artificial intelligence applications. The combination of SHARP with leading GPUs and the NVIDIA Collective Communications Library (NCCL) deliver leading efficiency and scalability for example.

Another new technology is SNAP (Software-defined Network Accelerated Processing) which enables hardware virtualization of PCIe devices, such as NVMe storage. The NVMe SNAP framework allows users to easily integrate networked storage solutions into their high-performance compute and storage infrastructures. It enables the efficient disaggregation of compute and storage to facilitate fully-optimized resource utilization.

NVMe SNAP logically presents networked storage, such as NVMe over Fabrics (NVMe-oF), as a local NVMe drive. This allows the host operating system to use a standard NVMe-driver instead of a remote networking storage protocol. The host benefits from the performance and simplicity of local NVMe storage, unaware that remote InfiniBand connected storage is being utilized and virtualized by NVMe SNAP.

Furthermore SNAP may apply sophisticated logic and data protection mechanisms (mirroring, compression, data-deduplication, thin-provisioning, encryption, etc.) to the network storage that it virtualizes as local NVMe.

Super-Connecting the #1 Supercomputers

By providing leading data throughput, extremely low latency, and, most importantly, In-Network Computing engines and full programmability, InfiniBand is the leading interconnect technology for compute intensive applications, high performance computing, deep learning and other applications. InfiniBand overtakes proprietary networks and accelerates many of the top supercomputers around the world.

 

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