Our GPU omnibus brings you all of the valuable, informational content we have published on the topic so far. Read on to know more.
At VEXXHOST, we have published a wide array of content on GPU and related topics through our blogs. The aim of this omnibus is to bring you all of those valuable, informational content together so that you can access it all from a one-stop solution. Each section is linked to a detailed blog where you can find more information about the said topic. Here's what you need to know about GPUs. Let's start from the basics.
Overview and History of GPUs
A Graphical Processing Unit or GPU is a specially designed processor that executes floating-point operations. GPUs came to the tech scene as a means to ease and improve the visual display aspect of computers; however, a few decades later, they have contributed to much more than graphics. GPUs can perform complex computations exponentially better than Central Processing Units (CPUs) and provide much-improved performance.
GPUs were first developed for the gaming industry – to process algorithms and render 3D images on 2D displays. Conventional CPUs weren't really capable of achieving this effectively. Later, it was identified that GPUs could be used to perform double the tasks or deliver calculations in a fraction of the time compared to CPUs. Hence, the purpose and usage of GPUs diversified. As the need for computations and better processing rose in server environments, GPUs in data centers became normal.
Benefits of Enterprise-Grade GPUs
As mentioned, GPUs have a lot more to offer than merely visual displays of computer and graphic features.As the popularity of the global GPU market grows so do the benefits. The major benefits GPUs provide enterprises currently are:
- Faster processing of large amounts of data
- Reduced cloud computing costs
- Increased productivity
GPUs in Data Centers
Due to their highly parallel structure, GPUs are capable of processing hundreds of thousands of small programs at once. Modern GPUs use caches, register files, frame-buffers, and other hardware that increase their capabilities and performance. These variances enable companies to make use of GPUs in ways that suit their particular processing needs. The only thing enterprises need to make effective use of GPUs in data centers in developing and implementing the code necessary for it to work.
The advantage of being able to use GPUs and access data quickly without noticeable stalling between tasks can have proven invaluable for those in the field of machine learning, deep learning, AI, data mining, etc. In data centers, GPUs have proven their worth as workload accelerators as well – Virtual Desktop Infrastructure (VDI) is a noteworthy example.
Changing the Data Mining Game
The main difficulties that are involved with attempting to mine these large data sets have to do with the volume, the velocity and the variety of the data. However, when factoring in the use of enterprise-grade GPUs to aid in the data mining process, these problems can be greatly diminished.
Volume . Through their numerous Algorithmic Logic Units (ALUs), GPUs don't require as much power as CPUs to process data and have an architecture that is dedicated to data processing as opposed to CPUs that are more inclined for data caching and flow control.
Velocity . With their many cores they have a parallel structure that is predisposed to handling repeated instructions and are perfectly suited for speeding up the workload, solving the issue of timeliness, and reducing total costs.
Variety . With an enterprise-grade GPU, companies have the flexibility to configure the programming to fit their needs. While the programming of GPUs for general-purpose computing is considered complex, the opportunities created by taking advantage of their flexibility far outweigh the initial challenges.
Impact on Deep Learning and Machine Learning
Deep Learning focuses on training systems to achieve specific results. But, depending on the data to be processed, the duration of training was an issue, especially with conventional CPUs.
Machine Learning (ML) is a growing subset of Artificial Intelligence (AI) that uses statistical techniques in order to make computer learning possible through data and without any specific programming. With GPUs' arrival into the equation, things started moving much faster for both deep learning and machine learning, producing miraculous results. Here are some contributing factors.
- GPUs have more dedicated logical cores to perform mathematical functions.
- GPUs have a great memory bandwidth
- Parallel task processing capabilities
- Faster processing of datasets
Impact on Blockchain Technology
The various components of blockchain have successfully solved some of the major problems associated with manual ledgers such as trust, transparency, and accountability. Regardless of blockchain's various business applications, GPUs have become invaluable to the technology. The addition of these circuits has allowed blockchain technology to reap the following benefits:
- Faster processing
- Lower costs
- Increased flexibility
GPU-Powered VEXXHOST Cloud Solutions
With the evolution of GPUs and their adoption in data centers, it is now possible to process hundreds of thousands of minuscule programs simultaneously. When it comes to harnessing the power of enterprise-grade GPUs, it is evident that they have the ability to impact your business substantially.
We at VEXXHOST use GPU-powered AMD EPYC™ & NVIDIA processors to power our clouds and can support a diverse cloud workload to suit a wide variety of needs. We provide OpenStack-based clouds with the help of projects continuously developed and updated from these communities, including public clouds and dedicated and highly secure private cloud environments,
VEXXHOST is celebrating its 15th anniversary this year, and we have a special gift for you. Take advantage of our limited-time deal just to set up a one-time, OpenStack-based private cloud deployment - at just $15000! The cloud will be running on the latest OpenStack release, Wallaby, which allows you to run Kubernetes and VMs in the same environment, and can be deployed in your own data centers with your hardware. Furthermore, all these will be deployed and tested in under a month!
What are you waiting for? Learn more!
