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Speed Genomic Analysis with 32 GPUs
in a Single Node

The GigaIO SuperNODE™ is the first single-node server with up to 32 accelerators and 1 petabyte of storage.

The power of all these accelerators seamlessly connected by FabreX™ can now be harnessed to drastically speed up time to results. SuperNODE can connect up to 32 AMD Instinct™ MI210 accelerators and up to 24 NVIDIA A100s; other types of accelerators will be validated as testing progresses. 

Workloads run faster because they utilize the power of each accelerator as if it was located in a single rack-scale server rather than in multiple constrained server nodes connected by a congested, complex network. All resources communicate over a single dynamic memory fabric.

SuperNODE In the News

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GigaIO’s SuperNODE to Power TensorWave Deployment with AMD MI300X

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GigaIO Secures Largest Order for SuperNODE Featuring AMD Instinct MI300X Accelerators

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GigaIO Reveals Latest Breakthrough in Single-Node GPU Power

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Building Impossible Servers with GigaIO

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Crafting A DGX-Alike AI Server Out Of AMD GPUs And PCI Switches

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GigaIO’s New SuperNode Takes-off with Record Breaking AMD GPU Performance

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One of the most difficult problems of computational fluid dynamics: Concorde during landing [video]

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It only takes 32 AMD GPUs and 33 hours to run a 40 billion cell simulation of the Concorde supersonic plane

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32 AMD MI210 GPUs Render Massive Simulation; 33 Hours for One Second of Concorde Landing

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Así fue la mayor simulación aerodinámica: el Concorde, 32 GPU AMD y 33 horas de trabajo ¡para 1 segundo!

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Mit 32 AMD-GPUs: GPU-Server erstellt sehenswerte Strömungssimulation der legendären Concorde

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Simulating the Concorde Supersonic Plane: A GPU-Powered Marvel

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GigaIO introduces single-node AI supercomputer

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GigaIO Introduces 32 GPU Single-Node Supercomputer

Genomic Sequencing Challenges

  • Sequencers are getting faster and faster, swamping genomics pipelines
  • Secondary Analysis processing times are now the bottleneck in genomics
  • Personalized Medicine is throttled by the lack of computing resources
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The Solution

GigaIO SuperNODE with FabreX Dynamic Memory Fabric

  • Connects up to 32 AMD Instinct™ MI210 GPUs or 24 NVIDIA A100s and up to 1PB storage to a single off-the-shelf server
  • Enables lower power, adaptive GPU supercomputing
  • Delivers the ability to train large models with tools like PyTorch or TensorFlow, scaling via peer-to-peer communication on a single node, instead of MPI over several nodes
  • Accelerators can be split over several servers, and scale using node-to-node communication for larger data sets

Benefits of GigaIO SuperNODE

  • Speed Secondary Analysis
  • Keep code simple: use your existing software without any changes – “It just works”
  • Easily expand accelerator resources to keep up with future sequencers
  • A single node solution reduces network overhead, cost, latency, and server administration
  • Save on power consumption (7KW per 32-GPU deployment)
  • Save on rack space (30% per 32-GPU deployment)
  • Immediate availability with no supply chain delay

Composable Disaggregated Infrastructure for Genomic Workloads

The GigaIO SuperNODE can connect up to 32 AMD Instinct™ MI210 GPUs or 24 NVIDIA A100s to a single node at the same latency and performance as if they were physically located inside the server box. The power of all these accelerators, seamlessly connected by GigaIO’s transformative PCIe memory fabric, FabreX, can now be harnessed to drastically speed up time to results.

Speed Analysis With Targeted FPGA and GPU Acceleration

The SuperNODE is a simplified system capable of scaling multiple accelerator technologies such as GPUs and FPGAs without the latency, cost, and power overhead required for multi-CPU systems.

See For Yourself

Testing Results for GigaIO SuperNODE, Hashcat and ResNet50
Be the first to know the latest SuperNODE test results

Made Possible by FabreX

The GigaIO SuperNODE is powered by FabreX, GigaIO’s transformative high-performance PCIe memory fabric. In addition to enabling unprecedented device-to-node configurations, FabreX is also unique in making possible node-to-node and device-to-device communication across the same high-performance PCIe memory fabric. FabreX can span multiple servers and multiple racks to scale up single-server systems and scale out multi-server systems, all unified via the FabreX software.

FabreX: How it Works

Resources normally located inside of a server — including accelerators such as GPUs and FPGAs, storage, and even memory — can now be pooled in accelerator or storage enclosures, where they are available to all of the servers in the system. These resources and servers continue to communicate over  the FabreX native PCIe memory fabric for the lowest possible latency and highest possible bandwidth performance, just as they would if they were still plugged into the server motherboard.

Unprecedented Compute Capability Available Now

Available today for emerging AI and accelerated computing workloads, the SuperNODE engineered solution, part of the GigaPod family, offers both unprecedented accelerated computing power when you need it, and the ultimate in flexibility and in accelerator utilization when your workloads only require a few GPUs. 

What will YOU discover?
Want to try your own code on SuperNODE for free?
Request access to our lab.
I Want to Give It a Try

Resources

 SuperNODE Engineered Solution Brief

Download the PDF

Download the PDF

Official SuperNODE
Press Release

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Download the PDF

 Watch it Now! SuperNODE Demo Video

Watch the Video

Watch the Video

SuperNODE Configuration Information

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Niraj Mathur

Niraj has over 20 years of industry experience in strategic and product marketing, product management, business development, customer applications and advanced silicon engineering. He has held senior leadership roles and led global, cross-functional teams to support these disciplines. Niraj was instrumental in driving numerous successful networking products at Nortel Networks, Quake Technologies, AppliedMicro, Snowbush, Gennum, Semtech and Rambus. He has defined, developed and supported carrier grade hardware and software for the world’s leading telecom, enterprise and cloud customers. His past projects include Ethernet PHYs, core Internet switches, metro optical routers, high-speed silicon IPs and PCI Express products. Niraj holds a Bachelor of Computer Engineering from McGill University and an MBA from Cornell University.

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