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Nimbix 2017 Predictions: "Accelerated Compute" becomes known simply as "Compute"

VMblog Predictions 2017

Virtualization and Cloud executives share their predictions for 2017.  Read them in this 9th annual VMblog.com series exclusive.

Contributed by Leo Reiter, CTO, Nimbix

2017: "Accelerated Compute" becomes known simply as "Compute"

I predict 2017 will be the year when "accelerated compute" becomes known just simply as "compute".  This is a direct response to the use cases driving up utilization the most, and the explosion of accelerator availability in both the data center and the public cloud.  (GPUs and FPGAs are readily available in the Nimbix Cloud to speed up compute intensive workflows like simulation, machine learning, and genomics).  As these use cases continue to ramp up in the Enterprise (particularly machine learning), we'll see even more demand for computational accelerators. 

CPUs have been king for decades, and serve the general purpose quite well.  But what we're seeing now is an emphasis on deriving insight from data, versus just indexing it, and this requires orders of magnitude faster (and more specialized) resource in order to deliver feasible economics.  It's not that computational accelerators are necessarily "faster" than CPUs, but rather, they can be deployed as coprocessors and therefore take on very specialized identities.  Because of this specialization, they can be programmed to do certain very discrete computations much quicker and at lower aggregate power consumption.  Application developers and ISVs are pouncing on these capabilities (and their increasing availability) to create amazing new products and services.

A good example of a red-hot technology in this space are GPU-accelerated databases, such as GPUdb from Kinetica (available as a turnkey workflow on the Nimbix Cloud).  Rather than focusing on indexing massive amounts of information like a traditional RDBMS, it's used to ingest fragments into memory for tremendously fast queries.  In fact the queries are so fast that it blurs the line between analytics and machine learning (after all, machine learning involves processing massive data sets very quickly in order to create "models" that operate somewhat like human brains).  Despite the advanced computing underneath, these tools serve traditional enterprise markets, not just "research labs".  Not only does its product name imply it, but the use case simply would be impossible without GPUs. This is a very real example of mainstream technology that demands computational accelerators.

I talk to customers and business partners from all walks of life every day.  The one common thread they all seek is more accelerated computational power (at reasonable economics) to do even more advanced things.  I don't see this trend slowing down anytime soon, which is why I'm predicting that we'll drop the "accelerated" in front of "compute" as it will become a given.

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About the Author

Leo Reiter is a virtualization and cloud computing pioneer with over 20 years of experience in software development and technology strategy. Prior to Nimbix, Leo was co-founder and CTO of Virtual Bridges. Leo is an entrepreneur with a strong background in LeanStartup and Agile methodologies.  

Leo Reiter 

Published Tuesday, December 13, 2016 9:05 AM by David Marshall
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