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Analytics Processing Unit (APU)

By: Jonathan Friedmann
| Last updated: February 2, 2022

What Does Analytics Processing Unit (APU) Mean?

An analytics processing unit (APU) is a dedicated System on Chip (SoC) for optimizing data analytics in column-oriented databases, as well as boosting analytical workloads. Each APU consists of multiple, multi-core, massively parallel processors and dedicated hardware pipelines.

An APU can be thought of as an accelerator for distributed computational and I/O processes. Just as graphic processing units (GPUs) are used to augment compute-intensive workloads for deep learning applications, APUs can be used to augment compute-intensive workloads for big data analytics in the cloud and on-premises.

In addition to accelerating compute, the APU’s architecture dramatically reduces the need for DRAM bandwidth, which effectively increases the bandwidth to memory and improves memory capacity. Because multiple I/O operations are carried out in parallel in dedicated hardware, ETL (extract, transform, load) workloads can be accelerated substantially. A single server with a few APUs, for example, can replace multiple racks of CPUs, while also dramatically saving space, reducing energy costs and improving run times exponentially.

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Techopedia Explains Analytics Processing Unit (APU)

Big Data is exploding and analytics is increasingly becoming the lifeblood of all successful organizations. Over the next few years, analytics and database processing is expected to be an even bigger workload than artificial intelligence (AI) with regard to dollars spent. That's why industries are looking for solutions to accelerate database analytics. It's going to be the key to gaining or maintaining a competitive advantage.

The problem is that current processing units aren’t designed to manage today’s workloads. As data grows bigger, caches are less effective and memory access for analytics applications can consume a disproportionate amount of energy. Some vendors have tried using Field Programmable Gate Arrays (FPGAs) as accelerators for big data analytics workloads, but the results have been uneven.

As time goes on, it's becoming increasingly clear that application-specific integrated circuits (ASICs), including analytics processing units, will play an important role in reducing bottlenecks for specific types of workloads in the cloud. The challenge for vendors in this marketspace will be to ensure that acceleration hardware is compatible with legacy software and existing frameworks.

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