Close to the Edge: If I Don't Use ECC, Have I Made an Error?

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Written by Douglas Eadline

Published: 24 June 2019

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A continuing installment of our (Close to the) Edge Computing series.

The main memory in computing devices is an ephemeral repository of information. Processing units (CPUs) may analyze and change the information, but ultimately the results of everything eventually end up in the main memory store. Of course, the information may move to nonvolatile memory or disk storage, which provides a more permanent resting place.

Main memory integrity is important. If errors occur in main memory, anything from nothing to a full crash of the entire computer is possible. In order to prevent and possibly correct memory errors, Error-Correcting Code memory (ECC) memory has been developed and deployed in systems where data errors may result in harmful results, like in real-time financial systems, for instance. The goal is that the data written to memory should be the same when it is read back in the future.

Close to the Edge: No Data Center Needed Computing

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Written by Douglas Eadline

Published: 22 May 2019

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Introduction

Welcome to a new series on ClusterMonkey! While the news and articles have been a bit sparse lately, it is not because the head monkey has been idle. Indeed, there is so much to write about and so little time. Another issue we ran into was how to present all the recent projects that may seem rather disparate with an easy-to-understand overriding theme. Welcome to edge computing.

Defining edge computing has become tricky because it now has a marketing buzz associated with it. Thus, like many over-hyped technology topics, it may take on several forms and have some core aspects that allow it to be treated as a "thing."

In this series, the definition of edge is going to be as specific as possible. In general, edge computing is that which does not take place in the data center or the cloud (hint: the cloud is a data center). Such a definition is too broad, however, since computing is everywhere (from cell phones to actual desktop workstations). A more precise definition of edge computing can be written as:

Data center level computing that happens outside of the physical data center or cloud.

That definition seems to eliminate many smaller forms of computing but still is a little gray in terms of "data center level computing." This category of computing usually operates 24/7 and provides a significantly higher level of performance and storage than mass-marketed personal systems.

Spring 2019 HPC Market Update

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Written by Number Six

Published: 21 June 2019

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As part of this years ISC-19 a new Top500 list has been posted. There was very little churn at the top of the list, however the entire list is now Peta-flop scale systems. In addition, the number 136 system was an AWS cloud based cluster. There has been some buzz as to whether a cloud based system violates the Top500 rule of not allowing "stunt" clusters to be ranked on the list. The intention is that the cluster should be permanent and used for production work after the list publication.

It was also another good year for HPC according to our friends at Intersect360:

This was a solid year for the HPC market, although there was some variability between categories. The total HPC market revenue was $36.1 billion in 2018, growing 3.2% over 2017. Public cloud consumption for HPC had a second strong year, with 16.0% growth. Spending on HPC servers grew 9.1%, driven in large part by end users shifting to more computationally rich environments, fueled greatly by new requirements in machine learning. HPC services declined, due to a number of market trends including: competition with the cloud and bundling of services with server sales.

More information can be found here. Full reports can be purchased by contacting sales(you know what foes here) intersect360.com.

Big Data In Little Spaces: Hadoop And Spark At The Edge

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Written by Administrator

Published: 25 April 2019

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Ever wonder what Edge computing is all about? Data happens and information takes work. Estimates are that by 2020, 1.7 megabytes of new data will be created every second for every person in the world. That is a lot of raw data.

Two questions come to mind. What are we going to do with it and where we going to keep it. Big Data is often described by the three Vs – Volume, Velocity, and Variability – and note not all three need apply. What is missing is the letter “U” which stands for Usability. A Data Scientist will first ask, how much of my data is usable? Data usability can take several forms and include things like quality (is it noisy, incomplete, accurate) and pertinence (is there any extraneous information that will not make a difference to my analysis). There is also the issue of timeliness. Is there a “use by” date for the analysis or might the data be needed in the future for some as of yet unknown reason. The usability component is hugely important and often determines the size of any scalable analytics solution. Usable data is not the same as raw data.

Get the full article at The Next Platform. You may recognize the author.