Optimizing Data Center Infrastructure For AI Workloads

The growing demand for AI workloads in data centers has led many data center managers to invest in the hardware needed to process it (and store its results). To get the best value for this hardware, however, it needs to be optimized. With that in mind, here is a quick guide to four key best practices for optimizing data center infrastructure for AI workloads.

GPU acceleration

Graphical Processing Units (GPUs) are specifically designed to handle parallel processing. This means they can process AI workloads much more efficiently than traditional Central Processing Units. Their efficiency can be increased by using GPU acceleration techniques.

Overclocking: Overclocking involves increasing the clock speed of the GPU beyond its factory-set specifications to enhance processing power. This also increases its heat production so it’s essential to ensure that the GPU is given extra cooling.

Memory bandwidth optimization: Optimizing memory bandwidth involves maximizing the data transfer rate between the GPU’s memory and the processing cores. This helps to reduce memory-access latency and increase throughput. It therefore improves the overall performance of AI workloads that rely heavily on memory access.

Kernel fusion: Kernel fusion combines multiple computational operations into a single kernel or operation. It minimizes memory access and synchronization overhead and hence enables faster execution of AI workloads.

Batch processing: By batching input data, the GPU can amortize the overhead of memory transfers and kernel launches across multiple computations, effectively utilizing the GPU’s processing cores and accelerating AI workload execution.

Asynchronous execution: Asynchronously launching kernel computations and memory transfers enables the GPU to continue processing tasks while waiting for data to be transferred to or from the device. This improves overall throughput and efficiency for AI workloads.

High-performance computing (HPC)

At this point, leveraging high-performance computing is effectively mandatory for AI workloads (at least heavy ones). Here are just five of the most important benefits it offers.

Parallel processing: By breaking down tasks into smaller parallelizable components, HPC enables simultaneous execution. This significantly reduces computation time for AI model training and inference.

Specialized hardware accelerators: Hardware accelerators are designed to handle the complex mathematical operations inherent in deep learning algorithms. They significantly increase computational throughput compared to traditional CPUs. This means they enable faster model training and inference.

Distributed computing: By partitioning data and computations and distributing them across the network, distributed computing enables scalability and accelerates the processing of large datasets. This makes it ideal for training complex AI models.

In-memory computing: In-memory computing eliminates the need for data transfers between storage and processing units. It therefore reduces latency and accelerates AI workloads, particularly those requiring iterative processing or frequent access to large datasets.

Optimized software stack: HPC software stacks include specialized libraries, frameworks, and tools optimized for parallel execution and efficient resource utilization on HPC architectures. By leveraging these optimized software components, developers can maximize performance and scalability for AI applications running on HPC systems.

Data storage solutions

AI workloads demand not just the best in processing power but also the best in memory and storage. Here are five strategies for optimizing your data memory and storage solutions.

High-speed technologies: Solid-State Drives (SSDs) and Non-Volatile Memory Express (NVMe) SSDs offer significantly faster read and write speeds compared to traditional solutions. This is crucial for AI tasks like model training and inference that involve frequent data access.

Tiered storage architectures: Frequently accessed data is stored on high-speed, low-latency tiers such as SSDs or NVMe SSDs, while less frequently accessed data is stored on lower-cost, high-capacity tiers like HDDs.

Scalable and distributed file systems: These file systems distribute data across multiple storage nodes, enabling parallel access and processing of data across the cluster. This facilitates efficient storage management for large-scale AI datasets.

Flash caching and acceleration: Frequently accessed data is cached on flash storage. This reduces latency and improves overall storage performance.

Data compression and deduplication techniques: Data compression algorithms reduce storage requirements without sacrificing data integrity. Deduplication identifies and eliminates redundant data, further reducing storage overhead. Both techniques help manage the growing volumes of data associated with AI workloads while minimizing storage costs.

Network configurations

Getting the most out of AI workloads means having top-quality network connectivity. Here are three strategies you can use to achieve this.

Optimize network topologies: Use technologies that offer high bandwidth and fault tolerance. These facilitate efficient data transfer and communication between network components.

Deploy high-speed interconnects: These provide low-latency, high-bandwidth communication between compute nodes, storage systems, and accelerators in AI infrastructure.

Define quality of service (QoS) policies: By assigning different priority levels to traffic generated by AI workloads, QoS policies ensure that critical data transfers are prioritized over less time-sensitive traffic. This helps to optimize network performance and ensure the smooth operation of AI applications.