The efficiency of networking performance plays a critical role in ensuring AI applications operate effectively. This efficiency determines how quickly a system processes information while also affecting overall application performance.
AI applications are usually data intensive and process large amounts of information, requiring fast access and speedy transfer across various network devices, such as switches, routers and servers. An inefficient network with slow speeds or high latency reduces processing times by disrupting real-time or near-real-time input signals. An application's algorithms rely on those signals to identify specific patterns crucial for accurate results.
When an application runs on network infrastructure, processors exchange information with remote memory through inter-processor transfers. This transfer contributes to significant latency and bandwidth reduction, which ultimately limits application efficiency. The increasing gap between the processing speed of CPUs and memory access speed presents AI applications with a challenge known as a memory wall.
Despite significant strides in CPU power, progress in improving memory access speeds has been comparatively slow. Consequently this bottleneck limits overall system performance.
The AI memory wall problem and networking
When it comes to AI applications, handling large datasets is an indisputable necessity. Yet this very process introduces a potential stumbling block. Transferring said datasets between different components, such as processing units and memory systems, can prove slow due to bandwidth limitations or high latencies characteristic of such systems.
To complicate matters, modern computers have separate memory tiers that differ in specific properties, such as access speed and capacity. Moving data between these distinct levels leads to a memory wall issue in which increased access times hamper performance.
Regarding caching, sometimes data is requested but not found in caches previously designed for quick retrieval. This failure adds another bottleneck-inducing issue called a cache miss. Such interruptions result in significant delays, often causing a lag in overall system performance. In addition, if multiple processing units or threads are accessing a single unit at once, contention for resources could occur, resulting in reduced efficiency.
Networking can mitigate these problems, however. A distributed system can use network resources by distributing computation and data across several nodes. This approach results in improved memory access times and lessens the effect of the memory wall problem on AI application performance.
One compelling way to curtail excessive overhead associated with moving information across varied nodes within a vast network is via networking technologies that incorporate remote direct memory access (RDMA).
RDMA enables direct data transfers between two remote systems' memories without CPU involvement. This process expedites data transfer while minimizing any resulting CPU overhead. In reference to AI applications, RDMA opens avenues for memory access optimization, streamlining communication across various parts of the network with speed and maximum efficiency.
For example, in a distributed deep learning system, enterprises could use RDMA to dispatch data from a GPU to another GPU or an offsite storage facility with remarkable agility. RDMA optimizes the use of available memory while circumventing potential RAM impediments and limiting the effect of the memory wall problem. This paradigm shift has big implications for AI-based applications where seamless communication often translates into the difference between mediocre and competent performance.
Networking needs beyond performance
AI applications require more than just impressive networking performance. The following are other areas where networking can benefit AI applications:
AI applications often deal with sensitive information like personal details or financial transactions. It's essential to ensure the confidentiality and integrity of such data using security measures such as encryption techniques and authentication controls.
Large-scale distributed systems need high scalability because they provide the foundation of AI-powered tools and fast response times. Using techniques that scale quickly, such as software-defined networking, can ensure AI applications grow seamlessly as needed.
Most AI applications need to provide real-time or near-real-time insights and predictions, making it paramount to maintain high-speed connectivity. Addressing this issue head on requires using networking designs with high reliability and fault tolerance features, redundant links, and failover mechanisms to ensure uninterrupted operation even when issues arise.
Different types of information might require differing levels of prioritization. With high-priority data taking precedence over others, networking offerings have evolved to offer QoS features. These features give applications the ability to allocate network bandwidth across various types of data traffic and to ensure the most critical information is processed on a priority basis.
SmartNICs and AI applications
Deploying AI applications effectively can benefit from specialized peripherals, such as smart network interface controllers (smartNICs). A key capability of smartNICs is the ability to offload network processing from a host computer's CPU to dedicated hardware accelerators. This reduces CPU load while freeing more resources for running AI applications.
SmartNICs use hardware accelerators that perform tasks such as encryption, compression and protocol processing. This method can also speed up data transfers, leading to less latency and increased network throughput speeds for faster data transfers and improved processing times.
Additionally, RDMA support on smartNICs ensures the direct transfer of large datasets between two systems without using the host CPU, which boosts efficiency and lowers latency. SmartNICs that support virtualization enable multiple virtual networks to share physical network infrastructure. This sharing promotes resource usage while efficiently scaling AI applications.
Using smartNICs also makes it easier to tackle the memory wall issues all AI applications face. SmartNICs transform how server systems handle their network infrastructure needs. Their ability to take on certain tasks that typically burden a host CPU means dramatically faster performance, especially with memory-intensive operations, such as data analysis.
Offloading packet filtering and flow classification duties onto dedicated hardware within a smartNIC -- rather than relying on a server CPU's general-purpose architecture -- effectively reduces server CPU usage and reaps better overall results. In addition, local caching functionality is available with many smartNIC models, which means less need for lengthy network transfers and less time waiting around for crucial information.
Considering their unique requirements compared with other types of applications, AI applications pose significant demands on network infrastructure regarding throughput, latency, security, reliability and scalability. Consequently, it might become necessary for enterprises to adapt their current data center network infrastructure to support these needs.
It's important to remember that AI workloads quickly exchange large datasets between systems, and these require high-speed connectivity. It might be necessary to upgrade to faster technologies, such as 100 Gigabit Ethernet, for optimized performance output.
In addition, optimizing latency has become increasingly vital in the scope of real-time processing within AI-based workloads. SmartNICs that support RDMA can achieve this goal without sacrificing quality significantly.
To further enhance performance and resource usage, enterprises can implement network virtualization to scale up AI applications and deploy traffic separation with network segmentation, which prioritizes each data stream appropriately.
Lastly, it's crucial to maintain a high degree of network reliability to prevent loss or corruption during crucial data transfer processes. This is important due to the sensitive nature and sheer volume involved in handling AI workloads.
About the author
Saqib Jang is founder and principal of Margalla Communications, a market analysis and consulting firm with expertise in cloud infrastructure and services. He is a marketing and business development executive with over 20 years' experience in setting product and marketing strategy and delivering infrastructure services for cloud and enterprise markets.