The Fast Lane to AI: Exploring Low Latency File Access' Impact on Workload Acceleration

Updated on August 4, 2023
By Alex Lesser

Alex Lesser

Experienced and dedicated integrated hardware solutions evangelist for effective HPC platform deployments for the last 30+ years.

The Fast Lane to AI: Exploring Low Latency File Access’ Impact on Workload Acceleration

The use of artificial intelligence (AI) is rapidly moving from specialized environments to mainstream adoption. Businesses recognize the potential of AI to deliver operational cost savings, improve decision-making, enhance customer interactions, expedite data mining, and bolster data security. Consequently, there has been a 270% increase in companies implementing AI over the past four years. As a result, organizations are now focused on designing high-performance cloud computing architectures to support AI workloads.

Effectively supporting AI initiatives demands high-performance cloud computing capabilities to enable rapid analysis, neural network model tuning, and machine learning on large datasets. The requirements for AI are similar to other compute-intensive applications like Big Data analytics, forecasting, modeling, and finite element simulations, which are increasingly integrated into enterprise operations today. This convergence has brought forth numerous high-performance cloud-based compute, storage, and networking technologies that were once confined to supercomputing centers and academic labs.

However, the specific infrastructure elements needed for each AI application depend on various factors. Many AI applications require real-time execution, such as those powering autonomous systems, engaging customers in chat or natural language interactions, or detecting anomalies to prevent fraud. Such applications must process their analyses swiftly to provide actionable insights in real-time.

Selecting the right cloud technology solution.

To achieve the necessary performance, AI deployments typically rely on advanced GPU processing arrays. To ensure cost-effective performance, high data rates must be supported to keep the processors continuously processing data. Consequently, ultrafast interconnect technology and tightly coupled high-performance cloud storage are crucial components.

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Some of the GPU options include:

NVIDIA Tesla P100 GPU accelerators for PCIe based cloud servers, delivering up to a 50X performance boost for top HPC applications and deep learning frameworks.
NVIDIA Tesla V100 Tensor Core, a data center GPU designed to accelerate HPC and AI workloads.
NVIDIA T4 GPU, used for HPC, deep learning training, inference, machine learning, and data analytics in cloud environments.
GEFORCE RTX 2080 Ti, NVIDIA’s flagship graphics card based on the NVIDIA Turing™ GPU architecture and ultra-fast GDDR6 memory.

Cloud systems utilizing these GPUs for AI workloads require high-performance interconnect technologies to fully leverage their capabilities. The preferred internet technologies include InfiniBand, Omni-Path, and remote direct memory access (RDMA).

What are their capabilities?

InfiniBand: A computer-networking communications standard used in HPC systems, known for very high throughput and low latency. It serves as either a direct or switched interconnect between cloud servers and storage systems.
Omni-Path: A high-performance communication architecture from Intel, offering low communication latency and high throughput.
RDMA: An industry-standard enabling zero-copy networking, allowing the network adapter to move data directly to or from the application without involving the operating system or CPU, resulting in exceptional speed.

The abundance of GPU and interconnect technology choices can be both an advantage and a challenge. While the right combination can produce an optimized cloud system for a specific AI application, many businesses lack expertise in these technologies and may need assistance in selecting the best solution and optimizing the system’s performance.

When configuring a cloud system for any HPC application, the challenges remain relevant, but they become especially critical in the context of AI applications. Fast data access is vital to reduce training time in deep learning scenarios and support rapid decision-making in production environments.

Determining the best cloud storage for AI applications

Determining the ideal storage for an AI application involves considerations beyond the basics, such as cost/performance comparisons between hard drives, solid-state drives, and flash drives. Issues related to storage file systems and architecture must also be addressed. Questions may arise about using a distributed architecture or a parallel file system. In essence, AI applications require storage solutions that offer the highest throughput and lowest latency data access for CPU- and GPU-intensive AI and HPC workloads.

Ultimately, to optimize the execution of AI workloads and maximize the efficiency of expensive GPU arrays, cloud solutions must combine the right GPU, high-performance cloud storage, and interconnect technologies. These technologies must be tightly integrated and tuned to optimize the solution’s performance when running AI workloads.

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