- Cloud ServicesDeep Learning
- ProductsRackmount &
- Cloud Services
- Deep Learning Servers
- Rackmount Servers
- Blade Servers
- Legacy Products
- SolutionsFor Today's
- IndustriesHPC and more
- CompanyAbout Cirrascale
- ContactGet in Touch
AMBER Certified Solutions/ Enabling Real AMBER MD Performance
Enabling Multi-GPU Scaling and Peering for AMBER
GPU-accelerated computing is enabling various scientific, analytics, engineering, consumer, and enterprise applications worldwide and that includes molecular dynamics. AMBER Multi-GPU computing can enable incredible results by utilizing multiple GPUs instead of just one. Until now, you couldn't get more than four GPUs to scale and peer. But today, Cirrascale offers system configurations for AMBER 14 that include 2, 4, and 8 multi-GPU configurations. AMBER 14 uses peer to peer communication to provide optimum multi-GPU scaling and Cirrascale takes full advantage of this with its proprietary 80-lane PCIe switch riser.
The Cirrascale RM4470-AC and GB5470-AC workstations and servers are designed to provide near-linear scaling of GPUs enabling up to eight GPUs to communicate peer-to-peer on just one PCIe root hub. This allows for the ultimate marriage of peering and scaling to provide you with the best performance for your GPU-accelerated applications such as AMBER 14.
Cirrascale Multi-GPU Solutions are Perfect for AMBER
Cirrascale AMBER certified workstation and server solutions are different from other GPU supporting hardware implementations. Most of the hardware configurations available today only provide maximum performance between specific pairs of GPUs; and since GPUs are paired up, jobs requiring communication between arbitrary GPUs experience a performance impact. Additionally, there can be a significant performance impacts with trying to scale more than four GPUs on multi-socket systems. These have been persistent problems for customers who are pushing the limits of GPUs with large, complex data-sets and calculations, or where data must be streamed between GPUs. Cirrascale has been able to overcome these issues, and achieve near linear performance scaling with its design.
Maximize PCIe Bandwidth
Cirrascale is a strong believer in utilizing a technology to its fullest potential whenever possible and GPUs and GPU Accelerators are no exception. If the GPU has a PCIe Gen3 x16 link, then it should use it when communicating with other GPUs — any other GPUs. Our switch riser technology allows us to scale and peer multiple PCIe x16 Gen3 cards on a single root hub ensuring that the maximum PCIe bandwidth is available utilized for inter-card communication.
Minimize Intercard Latency and Obtain Consistent Performance Between GPUs
Our switch riser allows GPUs to communicate as if they are all on the same bus... because they are. Gone are the days of needing a bounce-buffer in host memory, or leaving GPU DMA engines unused because they couldn't address other devices in the system. This reduces intercard latency while helping to maintain a consistent performance level between GPUs.
Enable GPU-Centric Development and Usage
Since most all of the GPU traffic is passed between the GPUs directly via the Cirrascale SR3415 switch riser, a very negligible amount of host resources are needed to perform GPU work. Additionally, with a single address space and simultaneous inter-card communication at full PCIe x16 Gen3 speeds, software can spend more time doing work than thinking about when to schedule data copies.
Supports the Largest Number of GPU Offerings for AMBER
We work closely with our technology partners to ensure you're given the broadest offerings for your application. The Cirrascale GB5400 Series supports both professional and consumer cards from the leading manufacturers including NVIDIA®. We can support any NVIDIA® GTX, Quadro®, or Tesla® GPU Accelerators. In fact, Cirrascale is an NVIDIA Tesla Preferred Partner and can create some of the most unique and powerful GPU-enabled solutions.
Our GPU Solutions In The News
The below articles and websites have mentions of the Cirrascale GB Series products, or have results posted from scaling and peering tests run on those products. We appreciate the coverage provided by these organizations and their help in furthering the message of scalable, peer-to-peer GPU implementations.
June 24, 2014, Ian Buck, NVIDIA
July 9, 2014, Rob Farber, TechEnablement.com
July 27, 2014, Rob Farber, TechEnablement.com