Certainly one of these applied sciences that was highlighted at Microsoft Ignite this previous November was Hole Core Fiber, an revolutionary optical fiber that’s set to optimize the Microsoft Azure international cloud infrastructure, providing superior community high quality and safe information transmission.
This weblog is a part of the ‘Infrastructure for the period of AI’ collection that focuses on rising expertise and traits in large-scale computing. This piece dives deeper into considered one of our latest applied sciences, hole core fiber (HCF).
AI is on the forefront of individuals’s minds, and improvements are occurring at lightning pace. However to proceed the tempo of AI innovation, firms want the suitable infrastructure for the compute-intensive AI workloads they’re making an attempt to run. That is what we name ‘purpose-built infrastructure’ for AI, and it’s a dedication Microsoft has made to its prospects. This dedication doesn’t simply imply taking {hardware} that was developed by companions and inserting it in its’ datacenters; Microsoft is devoted to working with companions, and sometimes by itself, to develop the latest and biggest expertise to energy scientific breakthroughs and AI options.
Certainly one of these applied sciences that was highlighted at Microsoft Ignite in November was hole core fiber (HCF), an revolutionary optical fiber that’s set to optimize Microsoft Azure’s international cloud infrastructure, providing superior community high quality, improved latency and safe information transmission.
Transmission by air
HCF expertise was developed to satisfy the heavy calls for of workloads like AI and enhance international latency and connectivity. It makes use of a proprietary design the place gentle propagates in an air core, which has important benefits over conventional fiber constructed with a stable core of glass. An fascinating piece right here is that the HCF construction has nested tubes which assist cut back any undesirable gentle leakage and preserve the sunshine moving into a straight path by way of the core.
As gentle travels sooner by way of air than glass, HCF is 47% sooner than normal silica glass, delivering elevated total pace and decrease latency. It additionally has a better bandwidth per fiber, however what’s the distinction between pace, latency and bandwidth? Whereas pace is how rapidly information travels over the fiber medium, community latency is the period of time it takes for information to journey between two finish factors throughout the community. The decrease the latency, the sooner the response time. Moreover, bandwidth is the quantity of knowledge that’s despatched and obtained within the community. Think about there are two automobiles travelling from level A to level B setting off on the identical time. The primary car is a automobile (representing single mode fiber (SMF)) and the second is a van (HCF). Each automobiles are carrying passengers (which is the info); the automobile can take 4 passengers, whereas the van can take 16. The automobiles can attain completely different speeds, with the van travelling sooner than the automobile. This implies it is going to take the van much less time to journey to level B, due to this fact arriving at its vacation spot first (demonstrating decrease latency).
For over half a century, the trade has been devoted to creating regular, but small, developments in silica fiber expertise. Regardless of the progress, the beneficial properties have been modest as a result of limitations of silica loss. A major milestone with HCF expertise was reached in early 2024, attaining the bottom optical fiber loss (attenuation) ever recorded at a 1550nm wavelength, even decrease than pure silica core single mode fiber (SMF). 1 Together with low attenuation, HCF provides larger launch energy dealing with, broader spectral bandwidth, and improved sign integrity and information safety in comparison with SMF.
The necessity for pace
Think about you’re taking part in a web-based online game. The sport requires fast reactions and split-second choices. When you’ve got a high-speed reference to low latency, your actions within the recreation can be transmitted rapidly to the sport server and to your pals, permitting you to react in actual time and luxuriate in a easy gaming expertise. Alternatively, when you have a gradual reference to excessive latency, there can be a delay between your actions and what occurs within the recreation, making it troublesome to maintain up with the fast-paced gameplay. Whether or not you’re lacking key motion occasions or lagging behind others, lagging is very annoying and might significantly disrupt gameplay. Equally, in AI fashions, having decrease latency and high-speed connections might help the fashions course of information and make choices sooner, enhancing their efficiency.
Decreasing latency for AI workloads
So how can HCF assist the efficiency of AI infrastructure? AI workloads are duties that contain processing giant quantities of knowledge utilizing machine studying algorithms and neural networks. These duties can vary from picture recognition, pure language processing, pc imaginative and prescient, speech synthesis, and extra. AI workloads require quick networking and low latency as a result of they typically contain a number of steps of knowledge processing, comparable to information ingestion, preprocessing, coaching, inference, and analysis. Every step can contain sending and receiving information from completely different sources, comparable to cloud servers, edge gadgets, or different nodes in a distributed system. The pace and high quality of the community connection have an effect on how rapidly and precisely the info might be transferred and processed. If the community is gradual or unreliable, it may trigger delays, errors, or failures within the AI workflow. This can lead to poor efficiency, wasted sources, or inaccurate outcomes. These fashions typically want enormous quantities of processing energy and ultra-fast networking and storage to deal with more and more subtle workloads with billions of parameters, so finally low latency and high-speed networking might help pace up mannequin coaching and inference, enhance efficiency and accuracy, and foster AI innovation.
Serving to AI workloads all over the place
Quick networking and low latency are particularly essential for AI workloads that require real-time or near-real-time responses, comparable to autonomous automobiles, video streaming, on-line gaming, or good gadgets. These workloads have to course of information and make choices in milliseconds or seconds, which implies they can’t afford any lag or interruption within the community. Low latency and high-speed connections assist be sure that the info is delivered and processed in time, permitting the AI fashions to offer well timed and correct outcomes. Autonomous automobiles exemplify AI’s real-world utility, counting on AI fashions to swiftly determine objects, predict actions, and plan routes amid unpredictable environment. Speedy information processing and transmission, facilitated by low latency and high-speed connections, allow close to real-time decision-making, enhancing security and efficiency. HCF expertise can speed up AI efficiency, offering sooner, extra dependable, and safer networking for AI fashions and functions.
Regional implications
Past the direct {hardware} that runs your AI fashions, there are extra implications. Datacenter areas are costly, and each the gap between areas, and between areas and the shopper, make a world of distinction to each the shopper and Azure because it decides the place to construct these datacenters. When a area is positioned too removed from a buyer, it leads to larger latency as a result of the mannequin is ready for the info to go to and from a middle that’s additional away.
If we take into consideration the automobile versus van instance and the way that pertains to a community, with the mixture of upper bandwidth and sooner transmission pace, extra information might be transmitted between two factors in a community, in two thirds of the time. Alternatively, HCF provides longer attain by extending the transmission distance in an present community by as much as 1.5x with no influence on community efficiency. In the end, you’ll be able to go an additional distance on the identical latency envelope as conventional SMF and with extra information. This has enormous implications for Azure prospects, minimizing the necessity for datacenter proximity with out growing latency and decreasing efficiency.
The infrastructure for the period of AI
HCF expertise was developed to enhance Azure’s international connectivity and meet the calls for of AI and future workloads. It provides a number of advantages to finish customers, together with larger bandwidth, improved sign integrity, and elevated safety. Within the context of AI infrastructure, HCF expertise can allow quick, dependable, and safe networking, serving to to enhance the efficiency of AI workloads.
As AI continues to evolve, infrastructure expertise stays a crucial piece of the puzzle, guaranteeing environment friendly and safe connectivity for the digital period. As AI developments proceed to put extra pressure on present infrastructure, AI customers are more and more looking for to profit from new applied sciences like HCF, digital machines just like the not too long ago introduced ND H100 v5, and silicon like Azure’s personal first associate AI accelerator, Azure Maia 100. These developments collectively allow extra environment friendly processing, sooner information switch, and finally, extra highly effective and responsive AI functions.
Sustain on our “Infrastructure for the Period of AI” collection to get a greater understanding of those new applied sciences, why we’re investing the place we’re, what these developments imply for you, and the way they allow AI workloads.