Ethernet in the Age of AI Q&A

Raguraman Sundaram Leave a comment

AI is having a transformative impact on networking. It’s a topic that the SNIA Data, Storage & Networking Community covered in our live webinar, “Ethernet in the Age of AI: Adapting to New Networking Challenges.” The presentation explored various use cases of AI, the nature of traffic for different workloads, the network impact of these workloads, and how Ethernet is evolving to meet these demands. The webinar audience was highly engaged and asked many interesting questions. Here are the answers to them all.

Q. What is the biggest challenge when designing and operating an AI Scale out fabric?

A. The biggest challenge in designing and operating an AI scale-out fabric is achieving low latency and high bandwidth at scale. AI workloads, like training large neural networks, demand rapid, synchronized data transfers between thousands of GPUs or accelerators. This requires specialized interconnects, such as RDMA, InfiniBand, or NVLink, and optimized topologies like fat-tree or dragonfly to minimize communication delays and bottlenecks.

Balancing scalability with performance is critical; as the system grows, maintaining consistent throughput and minimizing congestion becomes increasingly complex. Additionally, ensuring fault tolerance, power efficiency, and compatibility with rapidly evolving AI workloads adds to the operational challenges.

Unlike standard data center networks, AI fabrics handle intensive east-west traffic patterns that require purpose-built infrastructure. Effective software integration for scheduling and load balancing is equally essential. The need to align performance, cost, and reliability makes designing and managing an AI scale-out fabric a multifaceted and demanding task.

Q. What are the most common misconceptions about AI scale-out fabrics? Read More

Storage for AI Q&A

Jayanthi Ramakalanjiyam Leave a comment

Our recent SNIA Data, Networking & Storage Forum (DNSF) webinar, “AI Storage: The Critical Role of Storage in Optimizing AI Training Workloads,” was an insightful look at how AI workloads interact with storage at every stage of the AI data pipeline with a focus on data loading and checkpointing. Attendees gave this session a 5-star rating and asked a lot of wonderful questions. Our presenter, Ugur Kaynar, has answered them here. We’d love to hear your questions or feedback in the comments field.

Q. Great content on File and Object Storage, Are there any use cases for Block Storage in AI infrastructure requirements?

A. Today, by default, AI frameworks cannot directly access block storage, and need a file system to interact with block storage during training. Block storage provides raw storage capacity, but it lacks the structure needed to manage files and directories. Like most AI frameworks, PyTorch depends on a file system to manage and access data stored on block storage.

Q. Do high speed networks make some significant enhancements to I/O and checkpointing process?

A. High-speed networks enable faster data transfer rates and the I/O bandwidth can be better utilized which can significantly reduce the time required to save checkpoints. This minimizes downtime and helps maintain system performance.

However, it is important to keep in mind that the performance of checkpointing depends on both the storage network and the storage system. It’s essential to maintain a balance between the two for optimal results.

If the network is fast but the storage system is slow, or vice versa, the slower component will create a bottleneck. This imbalance can lead to inefficiencies and longer checkpointing times. When both the network and storage systems are balanced, data can flow smoothly between them. This maximizes throughput, ensuring that data is written to storage as quickly as it is transferred over the network.

 Q. What is the rule of thumb or range of storage throughput per GPU?

A. Please see answer below.

Q. What is the typical IO performance requirements for AI training in terms of IOs per second, bytes per second? Read More

The Evolution of Congestion Management in Fibre Channel

Erik Smith Leave a comment

The Fibre Channel (FC) industry introduced Fabric Notifications as a key resiliency mechanism for storage networks in 2021 to combat congestion, link integrity, and delivery errors. Since then, numerous manufacturers of FC SAN solutions have implemented Fabric Notifications and enhanced the overall user experience when deploying FC SANs.

On August 27, 2024, the SNIA Data, Networking & Storage Forum is hosting a live webinar, “The Evolution of Congestion Management in Fibre Channel,” for a deep dive into Fibre Channel congestion management. We’ve convened a stellar, multi-vendor group of Fibre Channel experts with extensive Fibre Channel knowedge and different technology viewpoints to explore the evolution of Fabric Notifications and the available solutions of this exciting new technology. You’ll learn: Read More

SNIA Networking Storage Forum – New Name, Expanded Charter

Christine McMonigal Leave a comment

Anyone who follows technology knows that it is a fast-paced world with rapid changes and constant innovations. SNIA, together with its members, technical work groups, Forums, and Initiatives, continues to embrace, educate, and develop standards to make technology more available and better understood.

At the SNIA Networking Storage Forum, we’ve been at the forefront of diving into technology topics that extend beyond traditional networked storage, providing education on AI, edge, acceleration and offloads, hyperconverged infrastructure, programming frameworks, and more. We still care about and spend a lot of time on networked storage and storage protocols, but we felt it was time that the name of the group better reflected the broad range of timely topics we’re covering. Read More

Q&A for Accelerating Gen AI Dataflow Bottlenecks

Erik Smith Leave a comment

Generative AI is front page news everywhere you look. With advancements happening so quickly, it is hard to keep up. The SNIA Networking Storage Forum recently convened a panel of experts from a wide range of backgrounds to talk about Gen AI in general and specifically discuss how dataflow bottlenecks can constrain Gen AI application performance well below optimal levels. If you missed this session, “Accelerating Generative AI: Options for Conquering the Dataflow Bottlenecks,” it’s available on-demand at the SNIA Educational Library.

We promised to provide answers to our audience questions, and here they are.

Q: If ResNet-50 is a dinosaur from 2015, which model would you recommend using instead for benchmarking?

A: Setting aside the unfair aspersions being cast on the venerable ResNet-50, which is still used for inferencing benchmarks 😊, we suggest checking out the MLCommons website. In the benchmarks section you’ll see multiple use cases on Training and Inference. There are multiple benchmarks available that can provide more information about the ability of your infrastructure to effectively handle your intended workload. Read More

Hidden Costs of AI Q&A

Erik Smith Leave a comment

At our recent SNIA Networking Storage Forum webinar, “Addressing the Hidden Costs of AI,” our expert team explored the impacts of AI, including sustainability and areas where there are potentially hidden technical and infrastructure costs. If you missed the live event, you can watch it on-demand in the SNIA Educational Library. Questions from the audience ranged from training Large Language Models to fundamental infrastructure changes from AI and more. Here are answers to the audience’s questions from our presenters.

Q: Do you have an idea of where the best tradeoff is for high IO speed cost and GPU working cost? Is it always best to spend maximum and get highest IO speed possible?

A: It depends on what you are trying to do If you are training a Large Language Model (LLM) then you’ll have a large collection of GPUs communicating with one another regularly (e.g., All-reduce) and doing so at throughput rates that are up to 900GB/s per GPU! For this kind of use case, it makes sense to use the fastest network option available. Any money saved by using a cheaper/slightly less performant transport will be more than offset by the cost of GPUs that are idle while waiting for data.

If you are more interested in Fine Tuning an existing model or using Retrieval Augmented Generation (RAG) then you won’t need quite as much network bandwidth and can choose a more economical connectivity option.

It’s worth noting Read More

Throughput, IOPs, and Latency Q&A

Erik Smith Leave a comment

Throughput, IOPs, and latency are three terms often referred to as storage performance metrics. But the exact definitions of these terms and how they differ can be confusing. That’s why the SNIA Networking Storage Forum (NSF) brought back our popular webinar series, “Everything You Wanted to Know About Storage, But Were Too Proud to Ask,” with a live webinar, “Everything You Wanted to Know about Throughput, IOPs, and Latency But Were Too Proud to Ask.”

The live session was a hit with over 850 views in the first 48 hours. If you missed the live event, you can watch it on-demand. Our audience asked several interesting questions, here are our answer to them.

Q: Discussing congestion and mechanisms at play in RoCEv2 (DCQCN and delay-change control) would be more interesting than legacy BB_credit handling in FC SAN… Read More

Here’s Everything You Wanted to Know About Throughput, IOPs, and Latency

Erik Smith Leave a comment

Any discussion about storage systems is incomplete without the mention of Throughput, IOPs, and Latency. But what exactly do these terms mean, and why are they important? To answer these questions, the SNIA Networking Storage Forum (NSF) is bringing back our popular webinar series, “Everything You Wanted to Know About Storage, But Were Too Proud to Ask.”

Collectively, these three terms are often referred to as storage performance metrics. Performance can be defined as the effectiveness of a storage system to address I/O needs of an application or workload. Different application workloads have different I/O patterns, and with that arises different bottlenecks, so there is no “one-size fits all” in storage systems. These storage performance metrics help with storage solution design and selection based on application/workload demands.

Join us on February 7, 2024, for “Everything You Wanted to Know About Throughput, IOPS, and Latency, But Were Too Proud to Ask.” In this webinar, we’ll cover: Read More

Accelerating Generative AI

David McIntyre Leave a comment

Workloads using generative artificial intelligence trained on large language models are frequently throttled by insufficient resources (e.g., memory, storage, compute or network dataflow bottlenecks). If not identified and addressed, these dataflow bottlenecks can constrain Gen AI application performance well below optimal levels.

Given the compelling uses across natural language processing (NLP), video analytics, document resource development, image processing, image generation, and text generation, being able to run these workloads efficiently has become critical to many IT and industry segments. The resources that contribute to generative AI performance and efficiency include CPUs, DPUs, GPUs, FPGAs, plus memory and storage controllers. Read More

Addressing the Hidden Costs of AI

Erik Smith Leave a comment

The latest buzz around generative AI ignores the massive costs to run and power the technology. Understanding what the sustainability and cost impacts of AI are and how to effectively address them will be the topic of our next SNIA Networking Storage Forum (NSF) webinar, “Addressing the Hidden Costs of AI.” On February 27, 2024, our SNIA experts will offer insights on the potentially hidden technical and infrastructure costs associated with generative AI. You’ll also learn best practices and potential solutions to be considered as they discuss: Read More