Sign up for our newsletter and get the latest HPC news and analysis.
Send me information from insideHPC:


Deconstructing the Complexities of the Data Pipeline for Connected Cars

A connected car can generate up to a gigabyte of data per day, and perhaps even more. Impressively, it is estimated that there are approximately 2 million connected cars on our roadways at this very moment. This means the storage demands can be up to 200 exabytes per day. IBM walks readers through the complexities of the data pipeline for connected cars, and how to address these challenges and storage questions. 

How Quantum Corp. Wrangles Big Data for HPC, Ai, and Autonomous Cars

In this video from SC18 in Dallas, Jason Coari from Quantum Corp. describes how the company wrangles Big Data for HPC, Ai, and Autonomous Cars. “Scientists and analysts today need intelligent data management throughout the entire research workflow, from ingest to HPC processing to archive. With multi-tier storage from Quantum, teams can better harness their data and transform the world.”

Solving the Data Management Challenge of Autonomous Driving

The action of driving an automobile is destined to take a very different route in the coming years as autonomous cars become commonplace on our roadways. The notion of autonomous driving is both scary and exciting, pushing the boundaries of what we know. IBM takes a look at one of the most vexing technology challenges posed by autonomous vehicles: how to manage all the data used in artificial intelligence development.

How NVIDIA Powers HPC for Energy at Dell EMC

In this video from the GPU Technology Conference, Peter Lilian from NVIDIA describes how the company works with Dell EMC to deliver extreme performance solutions for the Energy sector. “Dell EMC and NVIDIA have expanded their collaboration by signing a new strategic agreement to include joint product development of new products and solutions that address the burgeoning workload and datacenter requirements, with GPU-accelerated solutions for HPC, data analytics, and artificial intelligence.”

Nvidia in the Driver’s Seat for Deep Learning

In this special guest feature, Robert Roe from Scientific Computing World describes why Nvidia is in the driver’s seat for Deep Learning. “Nvidia CEO Jen-Hsun Huang’s theme for the opening keynote was based on “a new computing model.” Huang explained that Nvidia builds computing technologies for the most demanding computer users in the world and that the most demanding applications require GPU acceleration. ‘The computers you need aren’t run of the mill. You need supercharged computing, GPU accelerated computing’ said Huang.”