I recently caught up with Mike Perez, Vice President of Services at Kinetica, to talk about GPU-accelerated databases and discuss how the Kinetica new Install Accelerator and Application Accelerator programs are are helping customers quickly integrate Kinetica into their environments.
Kinetica Unveils Accelerator Solutions for Installing and Deploying Applications on its GPU-Accelerated Database
Kinetica, provider of the fastest, in-memory database accelerated by GPUs, announced the immediate availability of Install Accelerator and Application Accelerator programs, two new software and services offerings designed to help customers quickly ingest, explore and visualize streaming data sets, including for Internet of Things (IoT) use cases, by leveraging the power of GPUs.
Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ:AMZN), today announced the availability of P2 instances, a new GPU instance type for Amazon Elastic Compute Cloud (Amazon EC2) designed for compute-intensive applications that require massive parallel floating point performance, including artificial intelligence, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, genomics, and rendering.
Kinetica Unveils GPU-accelerated Database for Analyzing Streaming Data with Enhanced Performance, Visualization and High Availability
Kinetica announced the newest release of its distributed, in-memory database accelerated by GPUs that simultaneously ingests, explores, and visualizes streaming data.
The presentation below is an educational resource that sets the stage for parallel programming with GPUs (graphics processing units) and was sponsored by the Center for Astrophysics and Supercomputing at Swinburne University of Technology. GPUs are becoming quite popular for the implementation of deep learning solutions.
I was very pleased to attend the GPU Technology Conference 2016 as the guest of host company NVIDIA on April 4-7 in Silicon Valley. I was impressed enough with the experience that I wanted to write this Field Report to give readers an in-depth perspective for what I saw.
MapD Technologies unveiled its GPU-powered database and visual analytics software platform that enables data analysts to interactively explore large data sets at high speed.
As the use of GPUs continues to rise in fields like deep learning, we thought it would be useful to readers not yet familiar with this technology to offer the “Introduction to GPU Computing” presentation below.
“NVIDIA will present an update on accelerated computing, in particular, the latest de- velopments in the platform. They will touch upon NVLink, OpenPOWER, ARM64, and new software updates and also cover the broad-sweeping impact that a new field of machine learning, called Deep Learning, is having on applications and domains.”
In this video from GTC 2014, Todd Mostak from MapD demonstrates the company’s GPU-powered in-memory relational database software for Big Data. The Cambridge, Mass., based startup has built a high-speed GPU in-memory database that brings interactivity to big data. It can, for example, track more than a billion tweets worldwide at a time – and provide real-time visual analysis of the data. MapD was also announced as the winner of the GPU Technology Conference’s Early Stage Challenge this year, and they will be coming home with a cool $100,000 check.