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Adrian Cockcroft Presents: Shrinking Microservices to Functions

In this fascinating talk, Cockcroft describes how hardware networking has reshaped how services like Machine Learning are being developed rapidly in the cloud with AWS Lamda. “We’ve seen the same service oriented architecture principles track advancements in technology from the coarse grain services of SOA a decade ago, through microservices that are usually scoped to a more fine grain single area of responsibility, and now functions as a service, serverless architectures where each function is a separately deployed and invoked unit.”

Addison Snell Presents: HPC Computing Trends

Addison Snell presented this deck at the Stanford HPC Conference. “Intersect360 Research returns with an annual deep dive into the trends, technologies and usage models that will be propelling the HPC community through 2017 and beyond. Emerging areas of focus and opportunities to expand will be explored along with insightful observations needed to support measurably positive decision making within your operations.”

Asperitas Startup Brings Immersive Cooling to Datacenters

Today Dutch startup Asperitas rolled out Immersed Computing cooling technology for datacenters. “The company’s first market ready solution, the AIC24, ‘the first water-cooled oil-immersion system which relies on natural convection for circulation of the dielectric liquid.’ This results in a fully self-contained and Plug and Play modular system. The AIC24 needs far less infrastructure than any other liquid installation, saving energy and costs on all levels of datacentre operations. The AIC24 is the most sustainable solution available for IT environments today. Ensuring the highest possible efficiency in availability, energy reduction and reuse, while increasing capacity. Greatly improving density, while saving energy at the same time.”

Video: The Era of Self-Tuning Servers

“Servers today have hundreds of knobs that can be tuned for performance and energy efficiency. While some of these knobs can have a dramatic effect on these metrics, manually tuning them is a tedious task. It is very labor intensive, it requires a lot of expertise, and the tuned settings are only relevant for the hardware and software that were used in the tuning process. In addition to that, manual tuning can’t take advantage of application phases that may each require different settings. In this presentation, we will talk about the concept of dynamic tuning and its advantages. We will also demo how to improve performance using manual tuning as well as dynamic tuning using DatArcs Optimizer.”

Deep Learning & HPC: New Challenges for Large Scale Computing

“In recent years, major breakthroughs were achieved in different fields using deep learning. From image segmentation, speech recognition or self-driving cars, deep learning is everywhere. Performance of image classification, segmentation, localization have reached levels not seen before thanks to GPUs and large scale GPU-based deployments, leading deep learning to be a first class HPC workload.”

Six Steps Towards Better Performance on Intel Xeon Phi

“As with all new technology, developers will have to create processes in order to modernize applications to take advantage of any new feature. Rather than randomly trying to improve the performance of an application, it is wise to be very familiar with the application and use available tools to understand bottlenecks and look for areas of improvement.”

IBM Machine Learning Platform Comes to the Private Cloud

“Machine Learning and deep learning represent new frontiers in analytics. These technologies will be foundational to automating insight at the scale of the world’s critical systems and cloud services,” said Rob Thomas, General Manager, IBM Analytics. “IBM Machine Learning was designed leveraging our core Watson technologies to accelerate the adoption of machine learning where the majority of corporate data resides. As clients see business returns on private cloud, they will expand for hybrid and public cloud implementations.”

Defining AI, Machine Learning, and Deep Learning

“In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.”

OpenFog Consortium Publishes Reference Architecture

The OpenFog Consortium was founded over one year ago to accelerate adoption of fog computing through an open, interoperable architecture. The newly published OpenFog Reference Architecture is a high-level framework that will lead to industry standards for fog computing. The OpenFog Consortium is collaborating with standards development organizations such as IEEE to generate rigorous user, functional and architectural requirements, plus detailed application program interfaces (APIs) and performance metrics to guide the implementation of interoperable designs.

Video: Computing of the Future

Jeffrey Welser from IBM Research Almaden presented this talk at the Stanford HPC Conference. “Whether exploring new technical capabilities, collaborating on ethical practices or applying Watson technology to cancer research, financial decision-making, oil exploration or educational toys, IBM Research is shaping the future of AI.”