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Please see the article for deployment details Infiniband NDR OSFP Solution from FS community. RDMA Remote Direct Memory Access enables direct data transfer between devices in a network, and RoCE RDMA over Converged Ethernet is a leading implementation of this technology.
It improves data transmission with high speed and low latency, making it ideal for high-performance computing and cloud environments. As a type of RDMA, RoCE is a network protocol defined in the InfiniBand Trade Association IBTA standard, allowing RDMA over converged Ethernet network.
Shortly, it can be regarded as the application of RDMA technology in hyper-converged data centers, cloud, storage, and virtualized environments. It possesses all the benefits of RDMA technology and the familiarity of Ethernet. If you want to understand it in depth, you can read this article RDMA over Converged Ethernet Guide FS Community.
Generally, there are two RDMA over Converged Ethernet versions: RoCE v1 and RoCE v2. It depends on the network adapter or card used. Retaining the interface, transport layer, and network layer of InfiniBand IB , the RoCE protocol substitutes the link layer and physical layer of IB with the link layer and network layer of Ethernet.
In the link-layer data frame of a RoCE data packet, the Ethertype field value is specified by IEEE as 0x, unmistakably identifying it as a RoCE data packet. Consequently, routing at the network layer is unfeasible for RoCE data packets, restricting their transmission to routing within a Layer 2 network.
It transforms the InfiniBand IB network layer utilized by the RoCE protocol by incorporating the Ethernet network layer and a transport layer employing the UDP protocol. It harnesses the DSCP and ECN fields within the IP datagram of the Ethernet network layer for implementing congestion control.
This enables RoCE v2 protocol packets to undergo routing, ensuring superior scalability. As it fully supersedes the original RoCE protocol, references to the RoCE protocol generally denote the RoCE v2 protocol, unless explicitly specified as the first generation of RoCE.
Also Check- An In-Depth Guide to RoCE v2 Network FS Community. In comparison to InfiniBand, RoCE presents the advantages of increased versatility and relatively lower costs. It not only serves to construct high-performance RDMA networks but also finds utility in traditional Ethernet networks.
However, configuring parameters such as Headroom, PFC Priority-based Flow Control , and ECN Explicit Congestion Notification on switches can pose complexity. In extensive deployments, especially those featuring numerous network cards, the overall throughput performance of RoCE networks may exhibit a slight decrease compared to InfiniBand networks.
In actual business scenarios, there are major differences between the two in terms of business performance, scale, operation and maintenance. For detailed comparison, please refer to this article InfiniBand vs.
RoCE: How to choose a network for AI data center from FS community. RDMA over Converged Ethernet ensures low-latency and high-performance data transmission by providing direct memory access through the network interface. This technology minimizes CPU involvement, optimizing bandwidth and scalability as it enables access to remote switch or server memory without consuming CPU cycles.
The zero-copy feature facilitates efficient data transfer to and from remote buffers, contributing to improved latency and throughput with RoCE.
Notably, RoCE eliminates the need for new equipment or Ethernet infrastructure replacement, leading to substantial cost savings for companies dealing with massive data volumes. In the fast-evolving landscape of AI data center networks, selecting the right solution is paramount.
Drawing on a skilled technical team and vast experience in diverse application scenarios, FS utilizes RoCE to tackle the formidable challenges encountered in High-Performance Computing HPC. Take action now — registe r for more information and experience our products through a Free Product Trial.
To address the efficiency challenges of rapidly growing data storage and retrieval within data centers, the use of Ethernet-converged distributed storage networks is becoming increasingly popular.
However, in storage networks where data flows are mainly characterized by large flows, packet loss caused by congestion will reduce data transmission efficiency and aggravate congestion.
In order to solve this series of problems, RDMA technology emerged as the times require. RDMA Remote Direct Memory Access is an advanced technology designed to reduce the latency associated with server-side data processing during network transfers.
Allowing user-level applications to directly read from and write to remote memory without involving the CPU in multiple memory copies, RDMA bypasses the kernel and writes data directly to the network card.
This achieves high throughput, ultra-low latency, and minimal CPU overhead. RoCEv2, a connectionless protocol based on UDP User Datagram Protocol , is faster and consumes fewer CPU resources compared to the connection-oriented TCP Transmission Control Protocol.
The RDMA networks achieve lossless transmission through the deployment of PFC and ECN functionalities. With ECN technology, end-to-end congestion control is achieved by marking packets during congestion at the egress port, prompting the sending end to reduce its transmission rate.
Optimal network performance is achieved by adjusting buffer thresholds for ECN and PFC, ensuring faster triggering of ECN than PFC. The traditional TCP network heavily relies on CPU processing for packet management, often struggling to fully utilize available bandwidth. Therefore, in AI environments, RDMA has become an indispensable network transfer technology, particularly during large-scale cluster training.
It surpasses high-performance network transfers in user space data stored in CPU memory and contributes to GPU transfers within GPU clusters across multiple servers.
In building high-performance RDMA networks, essential elements like RDMA adapters and powerful servers are necessary, but success also hinges on critical components such as high-speed optical modules, switches, and optical cables.
These are precisely designed to meet the stringent requirements of low-latency and high-speed data transmission. Driven by the booming development of cloud computing and big data, InfiniBand has become a key technology and plays a vital role at the core of the data center.
But what exactly is InfiniBand technology? What attributes contribute to its widespread adoption? The following guide will answer your questions. InfiniBand is an open industrial standard that defines a high-speed network for interconnecting servers, storage devices, and more.
Moreover, it leverages point-to-point bidirectional links to enable seamless communication between processors located on different servers.
It is compatible with various operating systems such as Linux, Windows, and ESXi. InfiniBand, built on a channel-based fabric , comprises key components like HCA Host Channel Adapter , TCA Target Channel Adapter , InfiniBand links connecting channels, ranging from cables to fibers, and even on-board links , and InfiniBand switches and routers integral for networking.
Channel adapters, particularly HCA and TCA, are pivotal in forming InfiniBand channels, ensuring security and adherence to Quality of Service QoS levels for transmissions.
InfiniBand was developed to address data transmission bottlenecks in high-performance computing clusters. The primary differences with Ethernet lie in bandwidth, latency, network reliability, and more.
It provides higher bandwidth and lower latency, meeting the performance demands of large-scale data transfer and real-time communication applications. It supports Remote Direct Memory Access RDMA , enabling direct data transfer between node memories. This reduces CPU overhead and improves transfer efficiency.
The fabric allows for easy scalability by connecting a large number of nodes and supporting high-density server layouts. Additional InfiniBand switches and cables can expand network scale and bandwidth capacity. InfiniBand Fa InfiniBand Fabric incorporates redundant designs and fault isolation mechanisms, enhancing network availability and fault tolerance.
Alternate paths maintain network connectivity in case of node or connection failures. For those considering deployment in their high-performance data centers, further details are available from FS. This article will introduce the professional terminology and common network architecture of GPU computing.
In the domain of high-performance GPU computing, vital elements such as CPUs, memory modules, NVMe storage, GPUs, and network cards establish fluid connections via the PCIe Peripheral Component Interconnect Express bus or specialized PCIe switch chips.
NVLink is a wire-based serial multi-lane near-range communications link developed by Nvidia. Unlike PCI Express, a device can consist of muıltiple NVLinks, and devices use mesh networking to communicate instead of a central hub.
The protocol was first announced in March and uses proprietary high-speed signaling interconnect NVHS. The technology supports full mesh interconnection between GPUs on the same node. And the development from NVLink 1. NVSwitch is a switching chip developed by NVIDIA, designed specifically for high-performance computing and artificial intelligence applications.
Its primary function is to provide high-speed, low-latency communication between multiple GPUs within the same host. Unlike the NVSwitch, which is integrated into GPU modules within a single host, the NVLink Switch serves as a standalone switch specifically engineered for linking GPUs in a distributed computing environment.
Several GPU manufacturers have taken innovative ways to address the speed bottleneck by stacking multiple DDR chips to form so-called high-bandwidth memory HBM and integrating them with the GPU. This design removes the need for each GPU to traverse the PCIe switch chip when engaging its dedicated memory.
As a result, this strategy significantly increases data transfer speeds, potentially achieving significant orders of magnitude improvements.
In large-scale GPU computing training, performance is directly tied to data transfer speeds, involving pathways such as PCIe, memory, NVLink, HBM, and network bandwidth.
Different bandwidth units are used to measure these data rates. The storage network card in GPU architecture connects to the CPU via PCIe, enabling communication with distributed storage systems. It plays a crucial role in efficient data reading and writing for deep learning model training.
Additionally, the storage network card handles node management tasks, including SSH Secure Shell remote login, system performance monitoring, and collecting related data. These tasks help monitor and maintain the running status of the GPU cluster.
For the above in-depth exploration of various professional terms, you can refer to this article Unveiling the Foundations of GPU Computing-1 from FS community. In a full mesh network topology, each node is connected directly to all the other nodes.
Usually, 8 GPUs are connected in a full-mesh configuration through six NVSwitch chips, also referred to as NVSwitch fabric. This fabric optimizes data transfer with a bidirectional bandwidth, providing efficient communication between GPUs and supporting parallel computing tasks.
The bandwidth per line depends on the NVLink technology utilized, such as NVLink3, enhancing the overall performance in large-scale GPU clusters. The fabric mainly includes computing network and storage network.
The computing network is mainly used to connect GPU nodes and support the collaboration of parallel computing tasks. This involves transferring data between multiple GPUs, sharing calculation results, and coordinating the execution of massively parallel computing tasks.
The storage network mainly connects GPU nodes and storage systems to support large-scale data read and write operations. This includes loading data from the storage system into GPU memory and writing calculation results back to the storage system.
Want to know more about CPU fabric? Please check this article Unveiling the Foundations of GPU Computing-2 from FS community. The emergence of AI applications and large-scale models such as ChatGPT has made computing power an indispensable infrastructure for the AI industry.
With the ever-increasing demand for swifter communication in supercomputing, G high-speed optical modules have evolved into a crucial component of artificial intelligence servers.
Here are some key reasons why the industry is progressively favoring G optical transceiver and solutions. In artificial intelligence computing applications, especially those involving deep learning and neural networks, a significant amount of data is generated that needs to be transmitted over the network.
Research indicates that the higher capacity of G transceivers helps meet the bandwidth requirements of these intensive workloads. With the prevalence of cloud computing, the demand for efficient data center interconnectivity becomes crucial.
The G optical transceiver enable faster and more reliable connections between data centers, facilitating seamless data exchange and reducing latency. As east-west traffic experiences rapid growth within data centers, the traditional three-tier architecture is encountering progressively challenging tasks and heightened performance demands.
The adoption of G optical transceiver has propelled the emergence of a Spine-Leaf network architecture, offering multiple advantages such as high bandwidth utilization, outstanding scalability, predictable network latency, and enhanced security.
With the exponential growth in the volume of data processed by artificial intelligence applications, choosing to invest in G optical transceivers ensures that the network can meet the continuously growing data demands, providing future-oriented assurance for the infrastructure. The adoption of G optical transceiver offers a forward-looking solution to meet the ongoing growth in data processing and transmission.
Indeed, the collaborative interaction between artificial intelligence computing and high-speed optical communication will play a crucial role in shaping the future of information technology infrastructure. The transformative impact of artificial intelligence on data center networks highlights the critical role of G optical transceivers.
Ready to elevate your network experience? As a reliable network solution provider, FS provides a complete G product portfolio designed for global hyperscale cloud data centers. Seize the opportunity — register now for enhanced connectivity or apply for a personalized high-speed solution design consultation.
Take a deeper dive into the exciting advancements in G optical transceivers and their huge potential in the era of artificial intelligence with the following articles:. AI Computing Sparks Surge in G Optical Transceiver Demand.
Unleashing Next-Generation Connectivity: The Rise of G Optical Transceivers. In the AI Era: Fueling Growth in the Optical Transceiver Market.
As a critical hub for storing and processing vast amounts of data, data centers require high-speed and stable networks to support data transmission and processing.
The G network achieves a data transfer rate of Gigabits per second Gbps and can meet the demands of large-scale data transmission and processing in data centers, enhancing overall efficiency.
Therefore, many major internet companies are either constructing new G data centers or upgrading existing data centers from G, G to G speeds. However, the pursuit of G data transmission faces numerous complex challenges that necessitate innovative solutions.
Here, we analyze the intricate obstacles associated with achieving ultra-fast data transmission. The G network demands extensive data transmission, placing higher requirements on bandwidth. It necessitates network equipment capable of supporting greater data throughput, particularly in terms of connection cables.
Ordinary optical fibers typically consist of a single fiber within a cable, and their optical and physical characteristics are inadequate for handling massive data, failing to meet the high-bandwidth requirements of G.
While emphasizing high bandwidth, data center networks also require low latency to meet end-user experience standards. In high-speed networks, ordinary optical fibers undergo more refraction and scattering, resulting in additional time delays during signal transmission.
The high bandwidth requirements of G networks typically come with more connection ports and optical fibers. However, the limited space in data centers or server rooms poses a challenge. Achieving high-density connections requires accommodating more connection devices in the constrained space, leading to crowded layouts and increased challenges in space management and design.
The transition to an G network necessitates a reassessment of network architecture. Upgrading to higher data rates requires consideration of network design, scalability, and compatibility with existing infrastructure. Therefore, the cabling system must meet both current usage requirements and align with future development trends.
Given the long usage lifecycle of cabling systems, addressing how to match the cabling installation with multiple IT equipment update cycles becomes a challenging problem. Implementing G data transmission involves investments in infrastructure and equipment. Achieving higher data rates requires upgrading and replacing existing network equipment and cabling management patterns, incurring significant costs.
Cables, in particular, carry various network devices, and their required lifecycle is longer than that of network equipment. Frequent replacements can result in resource wastage. Effectively addressing these challenges is crucial to unlocking the full potential of a super-fast, efficient data network.
This design not only provides ample bandwidth support for data centers, meeting the high bandwidth requirements of an G network, but also helps save space and supports the high-density connection needs for large-scale data transfers.
By utilizing a low-loss cabling solution, they effectively contribute to reducing latency in the network. This, in turn, facilitates the straightforward deployment and reliable operation of G networks, reducing the risks associated with infrastructure changes or additions in terms of cost and performance.
In the implementation of G data transmission, the wiring solution is crucial. FS provides professional solutions for large-scale data center users who require a comprehensive upgrade to G speeds.
Aim to rapidly increase data center network bandwidth to meet the growing demands of business. Newly Built G Data Center Given the rapid expansion of business, many large-scale internet companies choose to build new G data centers to enhance their network bandwidth.
This strategic approach maximally conserves fiber resources, optimizes wiring space, and facilitates cable management, providing a more efficient and cost-effective cabling solution for the infrastructure of G networks. Certainly, many businesses choose to renovate and upgrade their existing data center networks.
The modules on both ends, previously G QSFP28 FR, were upgraded to G OSFP XDR8. This seamless deployment migrated the existing structured cabling to an G rate.
In short, this solution aims to increase the density of fiber optic connections in the data center and optimize cabling space. Not only improves current network performance but also takes into account future network expansion.
How to upgrade an existing G network to G in data centers? Based on the original G network, the core, backbone, and leaf switches have all been upgraded to an G rate, while the TOR Top of Rack remains at a G rate.
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