by Run Almog Head of Product Strategy, Drivenets (edited by Alan J Weissberger)
Different networking attributes are needed for different use cases. Endpoints can be the source of a service provided via the internet or can also be a handheld device streaming a live video from anywhere on the planet. In between endpoints we have network vertices that handle this continuous and ever-growing traffic flow onto its destination as well as handle the knowhow of the network’s whereabouts, apply service level assurance, handle interruptions and failures and a wide range of additional attributes that eventually enable network service to operate.
This two part article will focus on a use case of running artificial intelligence (AI) and/or high-performance computing (HPC) applications with the resulting networking aspects described. The HPC industry is now integrating AI and HPC, improving support for AI use cases. HPC has been successfully used to run large-scale AI models in fields like cosmic theory, astrophysics, high-energy physics, and data management for unstructured data sets.
In this Part I article, we examine: HPC/AI workloads, disaggregation in data centers, role of the Open Compute Project, telco data center networking, AI clusters and AI networking.
HPC/AI Workloads, High Performance Compute Servers, Networking:
HPC/AI workloads are applications that run over an array of high performance compute servers. Those servers typically host a dedicated computation engine like GPU/FPGA/accelerator in addition to a high performance CPU, which by itself can act as a compute engine, and some storage capacity, typically a high-speed SSD. The HPC/AI application running on such servers is not running on a specific server but on multiple servers simultaneously. This can range from a few servers or even a single machine to thousands of machines all operating in synch and running the same application which is distributed amongst them.
The interconnect (networking) between these computation machines need to allow any to any connectivity between all machines running the same application as well as cater for different traffic patterns which are associated with the type of application running as well as stages of the application’s run. An interconnect solution for HPC/AI would resultingly be different than a network built to serve connectivity to residential households or a mobile network as well as be different than a network built to serve an array of servers purposed to answers queries from multiple users as a typical data center structure would be used for.
Disaggregation in Data Centers (DCs):
Disaggregation has been successfully used as a solution for solving challenges in cloud resident data centers. The Open Compute Project (OCP) has generated open source hardware and software for this purpose. The OCP community includes hyperscale data center operators and industry players, telcos, colocation providers and enterprise IT users, working with vendors to develop and commercialize open innovations that, when embedded in product are deployed from the cloud to the edge.
High-performance computing (HPC) is a term used to describe computer systems capable of performing complex calculations at exceptionally high speeds. HPC systems are often used for scientific research, engineering simulations and modeling, and data analytics. The term high performance refers to both speed and efficiency. HPC systems are designed for tasks that require large amounts of computational power so that they can perform these tasks more quickly than other types of computers. They also consume less energy than traditional computers, making them better suited for use in remote locations or environments with limited access to electricity.
HPC clusters commonly run batch calculations. At the heart of an HPC cluster is a scheduler used to keep track of available resources. This allows for efficient allocation of job requests across different compute resources (CPUs and GPUs) over high-speed networks. Several HPC clusters have integrated Artificial Intelligence (AI).
While hyperscale, cloud resident data centers and HPC/AI clusters have a lot of similarities between them, the solution used in hyperscale data centers is falling short when trying to address the additional complexity imposed by the HPC/AI workloads.
Large data center implementations may scale to thousands of connected compute servers. Those servers are used for an array of different application and traffic patterns shift between east/west (inside the data center) and north/south (in and out of the data center). This variety boils down to the fact that every such application handles itself so the network does not need to cover guarantee delivery of packets to and from application endpoints, these issues are solved with standard based retransmission or buffering of traffic to prevent traffic loss.
An HPC/AI workload on the other hand, is measured by how fast a job is completed and is interfacing to machines so latency and accuracy are becoming more of a critical factor. A delayed packet or a packet being lost, with or without the resulting retransmission of that packet, drags a huge impact on the application’s measured performance. In HPC/AI world, this is the responsibility of the interconnect to make sure this mishaps do not happen while the application simply “assumes” that it is getting all the information “on-time” and “in-synch” with all the other endpoints it shares the workload with.
–>More about how Data centers use disaggregation and how it benefits HPC/AI in the second part of this article (Part II).
Telco Data Center Networking:
Telco data centers/central offices are traditionally less supportive of deploying disaggregated solutions than hyper scale, cloud resident data centers. They are characterized by large monolithic, chassis based and vertically integrated routers. Every such router is well-structured and in fact a scheduled machine built to carry packets between every group of ports is a constant latency and without losing any packet. A chassis based router would potentially pose a valid solution for HPC/AI workloads if it could be built with scale of thousands of ports and be distributed throughout a warehouse with ~100 racks filled with servers.
However, some tier 1 telcos, like AT&T, use disaggregated core routing via white box switch/routers and DriveNets Network Cloud (DNOS) software. AT&T’s open disaggregated core routing platform was carrying 52% of the network operators traffic at the end of 2022, according to Mike Satterlee, VP of AT&T’s Network Core Infrastructure Services. The company says it is now exploring a path to scale the system to 500Tbps and then expand to 900Tbps.
“Being entrusted with AT&T’s core network traffic – and delivering on our performance, reliability and service availability commitments to AT&T– demonstrates our solution’s strengths in meeting the needs of the most demanding service providers in the world,” said Ido Susan, DriveNets founder and CEO. “We look forward to continuing our work with AT&T as they continue to scale their next-gen networks.”
Satterlee said AT&T is running a nearly identical architecture in its core and edge environments, though the edge system runs Cisco’s disaggregates software. Cisco and DriveNets have been active parts of AT&T’s disaggregation process, though DriveNets’ earlier push provided it with more maturity compared to Cisco.
“DriveNets really came in as a disruptor in the space,” Satterlee said. “They don’t sell hardware platforms. They are a software-based company and they were really the first to do this right.”
AT&T began running some of its network backbone on DriveNets core routing software beginning in September 2020. The vendor at that time said it expected to be supporting all of AT&T’s traffic through its system by the end of 2022.
Attributes of an AI Cluster:
Artificial intelligence is a general term that indicates the ability of computers to run logic which assimilates the thinking patterns of a biological brain. The fact is that humanity has yet to understand “how” a biological brain behaves, how are memories stored and accessed, how come different people have different capacities and/or memory malfunction, how are conclusions being deduced and how come they are different between individuals and how are actions decided in split second decisions. All this and more are being observed by science but not really understood to a level where it can be related to an explicit cause.
With evolution of compute capacity, the ability to create a computing function that can factor in large data sets was created and the field of AI focuses on identifying such data sets and their resulting outcome to educate the compute function with as many conclusion points as possible. The compute function is then required to identify patterns within these data sets to predict the outcome of new data sets which it did not encounter before. Not the most accurate description of what AI is (it is a lot more than this) but it is sufficient to explain why are networks built to run AI workloads different than regular data center networks as mentioned earlier.
Some example attributes of AI networking are listed here:
- Parallel computing – AI workloads are a unified infrastructure of multiple machines running the same application and same computation task
- Size – size of such task can reach thousands of compute engines (e.g., GPU, CPU, FPGA, Etc.)
- Job types – different tasks vary in their size, duration of the run, the size and number of data sets it needs to consider, type of answer it needs to generate, etc. this as well as the different language used to code the application and the type of hardware it runs on contributes to a growing variance of traffic patterns within a network built for running AI workloads
- Latency & Jitter – some AI workloads are resulting a response which is anticipated by a user. The job completion time is a key factor for user experience in such cases which makes latency an important factor. However, since such parallel workloads run over multiple machines, the latency is dictated by the slowest machine to respond. This means that while latency is important, jitter (or latency variation) is in fact as much a contributor to achieve the required job completion time
- Lossless – following on the previous point, a response arriving late is delaying the entire application. Whereas in a traditional data center, a message dropped will result in retransmission (which is often not even noticed), in an AI workload, a dropped message means that the entire computation is either wrong or stuck. It is for this reason that AI running networks requires lossless behavior of the network. IP networks are lossy by nature so for an IP network to behave as lossless, certain additions need to be applied. This will be discussed in. follow up to this paper.
- Bandwidth – large data sets are large. High bandwidth of traffic needs to run in and out of servers for the application to feed on. AI or other high performance computing functions are reaching interface speeds of 400Gbps per every compute engine in modern deployments.
The narrowed down conclusion from these attributes is that a network purposed to run AI workloads differs from a traditional data center network in that it needs to operate “in-synch.
There are several such “in-synch” solutions available. The main options are: Chassis based solutions, Standalone Ethernet solutions, and proprietary locked solutions.–>These will be briefly described to their key advantages and deficiencies in our part II article.
There are a few differences between AI and HPC workloads and how this translates to the interconnect used to build such massive computation machines.
While the HPC market finds proprietary implementations of interconnect solutions acceptable for building secluded supercomputers for specific uses, the AI market requires solutions that allow more flexibility in their deployment and vendor selection.
AI workloads have greater variance of consumers of outputs from the compute cluster which puts job completion time as the primary metric for measuring the efficiency of the interconnect. However, unlike HPC where faster is always better, some AI consumers will only detect improvements up to a certain level which gives interconnect jitter a higher impact than latency.
Traditional solutions provide reasonable solutions up to the scale of a single machine (either standalone or chassis) but fail to scale beyond a single interconnect machine and keep the required performance to satisfy the running workloads. Further conclusions and merits of the possible solutions will be discussed in a follow up article.
DriveNets is a fast-growing software company that builds networks like clouds. It offers communications service providers and cloud providers a radical new way to build networks, detaching network growth from network cost and increasing network profitability.
DriveNets Network Cloud uniquely supports the complete virtualization of network and compute resources, enabling communication service providers and cloud providers to meet increasing service demands much more efficiently than with today’s monolithic routers. DriveNets’ software runs over standard white-box hardware and can easily scale network capacity by adding additional white boxes into physical network clusters. This unique disaggregated network model enables the physical infrastructure to operate as a shared resource that supports multiple networks and services. This network design also allows faster service innovation at the network edge, supporting multiple service payloads, including latency-sensitive ones, over a single physical network edge.