How do CPU, GPU and DPU differ from one another?

How do CPU, GPU and DPU differ from one another?

In the world of computing, the term “processor” often refers to the central processing unit, or the CPU. The CPU is the most ubiquitous processor, but it is not the sole processing unit available to data centers. GPUs and DPUs can help manage increasingly complex processing loads and computing tasks.

All three processing units support complex computing, but each is suited for different tasks or workloads. By using multiple types of processing units in your data center, these units can support each other and further accelerate large or complicated tasks.

What is a CPU?

The CPU is often described as the brain of the computer and is considered the “main” processor. The CPU uses logic circuitry to interpret, process and execute instructions and commands sent to it from the OS, programs or various computer components.

CPU is integral to system operations. It performs everything from basic arithmetic and logic to I/O operations. It also handles sending instructions and feeding data to specialized hardware, such as graphics cards.

In the early days of computer history, a CPU often had a single processing core. Today, they can contain multiple cores to perform many instructions at once. This increases overall system performance and speed throughout the data center.

What is a GPU?

Graphics processing units (GPUs) were initially designed to complement the CPU. The units have a lot in common: They’re both critical computing engines that can handle data, but GPUs specifically accelerate graphics rendering.

Although a CPU can send instructions to a graphics card, CPUs can handle only a few software threads at a time. With multiple processing cores, they’re great for serial processing — executing a wide variety of workloads or a series of tasks by focusing on completing individual tasks quickly — but image rendering is complex. A graphics processing unit contains many more cores than a CPU, which enables it to tackle thousands of operations at once rather than just a handful.

This breaks down the complex tasks of graphics rendering, which involves manipulating computer graphics and image processing simultaneously, consistently and at high speeds. For example, the GPU can accelerate the intensive tasks of ray tracing, bump mapping, lighting computations and smooth decoding quickly to render animations or video and output to a display.

In short, the GPU differs from the CPU in that it performs parallel operations rather than serial operations. Although the GPU was intended to process computer graphics, its parallel processing architecture made it a clear fit for other types of complex workloads such as supercomputing, AI, machine learning, database calculation and big data analysis.

Its ability to handle complex mathematical processes efficiently enables the GPU to enhance performance for data center applications and greatly accelerate data center workloads. GPUs can support big data and scientific computing scenarios, streamline container orchestration and process work in a fraction of the time CPUs do.

What is a DPU?

Originally, the CPU had a single processing core and acted as the central component of personal computers. The CPU has evolved over the years, the GPU began to handle more complex computing tasks, and now, a new pillar of computing emerges in the data processing unit.

The DPU offloads networking and communication workloads from the CPU. It combines processing cores with hardware accelerator blocks and a high-performance network interface to tackle data-centric workloads at scale. This architectural approach enables the DPU to make sure the right data goes to the right place in the right format quickly.

The DPU is essentially designed to process data moving around the data center. It focuses on data transfer, data reduction, data security and powering data analytics, as well as encryption and compression. This means it supports more efficient data storage and frees up the CPU to focus on application processing.

A DPU can also address server node inefficiency when you place it at the heart of data-centric infrastructure. It can mitigate sprawl, deliver high availability and reliability and ensure the quick accessibility and shareability of data regardless of how much data requires processing and transferring.

DPU processing is specific to use cases with large-scale data-processing needs, such as data centers supporting cloud environments or supercomputers driving complex AI, ML and deep learning algorithms.

Comparison chart: CPU vs. GPU vs. DPU
CPUs, GPUs and DPUs differ in number of cores and style of processing.

The future of processing units

The CPU, GPU and DPU were each created to adapt to an evolving IT landscape and meet increasingly complex computing needs. Data centers are the first market segment to adopt DPUs because of the technology’s ability to process massive amounts of data. DPUs also offload much of the strain on CPUs and GPUs currently being used to power AI/ML applications.

In the future, new processing units might further transform technology operations, but for now, the DPU represents an exciting recent advancement that drives efficiency at scale for data centers.

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2022 Feb 24th

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