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DPU, CPU, GPU – WHAT’S THE DIFFERENCE?

central processing unit (CPU) is the electronic circuitry that executes instructions encompassing a computer program. Flexible and responsive, for many years CPUs were the sole programmable element in most computers.

graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device.

Originally used to deliver rich, real-time graphics, their parallel processing capabilities make them ideal for accelerated computing tasks of all kinds. Thanks to these capabilities, GPUs are essential to artificial intelligence, deep learning and big data analytics applications.

WHAT EXACTLY IS A DPU?

System on a chip that combines:

  1. Industry-standard, high-performance, software-programmable multi-core CPU
  2. High-performance network interface
  3. Flexible and programmable acceleration engines

Specialists in moving data in data centers, DPUs, or data processing units, are a new class of programmable processor and will join CPUs and GPUs as one of the three pillars of computing. Over the past decade, computing has broken out of the boxy confines of PCs and servers — with CPUs and GPUs powering sprawling new hyperscale data centers. These data centers are knit together with a powerful new category of processors. The DPU has become the third member of the data-centric accelerated computing model.

According to NVIDIA CEO Jensen Huang: “The CPU is for general-purpose computing, the GPU is for accelerated computing, and the DPU, which moves data around the data center, does data processing.”

CPU v GPU v DPU: WHAT MAKES A DPU DIFFERENT?

A DPU is a new class of programmable processor that combines three key elements. A DPU is a system on a chip, or SoC, that combines:

  1. An industry-standard, high-performance, software-programmable, multi-core CPU, typically based on the widely used Arm architecture, tightly coupled to the other SoC components.
  2. A high-performance network interface capable of parsing, processing and efficiently transferring data at line rate, or the speed of the rest of the network, to GPUs and CPUs.
  3. A rich set of flexible and programmable acceleration engines that offload and improve applications performance for AI and machine learning, security, telecommunications and storage, among others.

All these DPU capabilities are critical to enable an isolated, bare-metal, cloud-native computing platform that will define the next generation of cloud-scale computing.

DPUs Incorporated into SmartNICs

The DPU can be used as a stand-alone embedded processor. More often it is incorporated into a SmartNIC, a network interface controller used as a critical component in a next-generation server.

For example, some vendors use proprietary processors that don’t benefit from the broad Arm CPU ecosystem’s rich development and application infrastructure. Others claim to have DPUs but make the mistake of focusing solely on the embedded CPU to perform data path processing.

A FOCUS ON DATA PROCESSING

That approach isn’t competitive and doesn’t scale, because trying to beat the traditional x86 CPU with a brute force performance attack is a losing battle. If 100 Gigabit/sec packet processing brings an x86 to its knees, why would an embedded CPU perform better?

Instead, the network interface needs to be powerful and flexible enough to handle all network data path processing. The embedded CPU should be used for control path initialization and exception processing, nothing more.

Complete the form to find out the minimum 10 capabilities a network data path acceleration engine needs to be able to deliver. These are just 10 of the acceleration and hardware capabilities that are critical to being able to answer yes to the question: “What is a DPU?”

So what is a DPU?

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