Date Archives: April 2018

Curious about cloud computing? Here’s everything you need to know
Ask people what cloud computing is, and you’re likely to get a dozen different explanations, which probably doesn’t help you understand any better. The truth is, cloud computing has so many different applications it can be tough to define it on the spot. For that, you deserve a more complete explanation. Here’s everything you need to know about cloud computing!

Cloud computing refers to moving processes and data off of the devices right in front of you (desktops, phones, etc.) and onto the internet, where they are stored in remote servers and used as needed. The “cloud” refers to the many server networks where all these data/services now exist, waiting to be accessed by users.

An excellent example of this shift is Microsoft Office. Prior to the cloud, Microsoft Office was sold as a single license and a physical disc that you uploaded to your local hard drive so you could run the software. Now (while that older model still exists), Microsoft Office is primarily offered as a subscription service that is offered over the cloud via the Office 365 suite of services, as well as a web app. It’s no longer necessary to have a physical disc, and its easier to access a bundle of specific services your home or business needs.

More => https://www.digitaltrends.com/computing/what-is-cloud-computing/

By Paul Teich
This article focuses on the “under the hood” machine learning (ML) work IBM announced last week at Think 2018, work that will soon accelerate Watson and PowerAI training performance even further. This coincidentally highlights IBM’s partnership with NVIDIA and access to NVIDIA’s NVLink interconnect technology for GPUs.

Last year, I discussed an IBM paper that described how to train a ML image classification model in under an hour at 95% scaling efficiency and 75% accuracy, using the same data sets that Facebook used for training. IBM ran that training benchmark in the first half of 2017 using the 64 POWER8-based Power System S822LC for High Performance Computing systems. Each of those systems had four NVIDIA Tesla P100 SXM2-connected GPUs and used IBM’s PowerAI software platform with Distributed Deep Learning (DDL).

IBM’s new paper, “Snap Machine Learning,” describes a new IBM machine learning (ML) library that more effectively uses available network, memory and heterogeneous compute resources for ML training tasks. It is also based on a new platform—IBM Power Systems AC922 server. IBM’s AC922 has four SXM2-connected NVIDIA Tesla V100 GPUs attached to dual-POWER9 processors via NVIDIA’s latest NVLINK 2.0 interface.

More => https://www.forbes.com/sites/tiriasresearch/2018/03/26/ibm-has-ai-training-advantage/#7e1ee1d45434