Showing posts with label GP-GPU. Show all posts
Showing posts with label GP-GPU. Show all posts

Monday, July 17, 2017

GPUs in action

AI Learns to Lip-Sync From Audio Clips
University of Washington researchers developed a deep learning-based system that converts audio files into realistic mouth shapes, which are then grafted onto and blended with the head of that person from another existing video.
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Turn Your Selfies Into Chat Stickers
The developers of Prisma, Apple’s 2016 iPhone App of the Year, launched their second AI-based app called Sticky AI that turns your selfies into stickers to use in messages and on social networks.
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Simulating Chemical Attacks to Save Lives
Researchers at University of Texas at San Antonio used a GPU-accelerated supercomputer to develop an early-warning intelligence system that could alert civilians to impending danger.
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Deep Learning Key Copying Service
New York-based startup KeyMe recently raised $20 million in a Series B funding allows you to scan a key, either via mobile app or at one of their thousands in-store kiosk, and then ships you a key when you want a copy, or you can have one printed instantly at a kiosk.
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NVIDIA Grant Alert: Up to $400K Available for Cancer Research
The NVIDIA Foundation is now accepting proposals for its annual Compute the Cure Cancer Research grant program, which supports researchers using innovative computing methods to advance the fight against cancer.
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https://news.developer.nvidia.com/nvidia-grant-alert-up-to-400k-available-for-cancer-research/

Tuesday, March 12, 2013

Independent Test: Xeon Phi Shocks Tesla GPU



This article is taken from Go Parallel: Independent Test: Xeon Phi Shocks Tesla GPU

Intel’s Xeon Phi coprocessor outperforms Nvidia’s Tesla graphic-processing unit (GPU) on the operations used by “solver” applications in science and engineering, according to independent tests at Ohio State University.

When comparing Intel’s Xeon Phi to Nvidia’s Tesla, most reviewers dwell on how much easier it is to rewrite parallel programs for the Intel coprocessor, since it runs the same x86 instruction set as a 64-bit Pentium. 

Nvidia’s “Cuda” cores on its Tesla coprocessor, on the other hand, do not even try to emulate the x86 instruction set, opting instead for more economical instructions that allow it to cram many more cores on a chip.

As a result, Nvidia’s Tesla has 40-times more cores (2,496) than Intel’s Xeon Phi (60). The question then becomes: “is it worth it” to rewrite x86 parallel software for Nvidia’s Cuda, in order to gain access to the thousands of more cores available with Tesla over Xeon Phi?
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Do read the article Independent Test: Xeon Phi Shocks Tesla GPU

Wednesday, November 10, 2010

Open CL Programming Webinar Series

This is a series of webinars where AMD experts discuss and answer questions about data parallel computing on GPU leveraging on the OpenCL(tm) architecture. These webinars will include beginning and advanced tracks offered at varying times

  1. OpenCL Programming Webinar Series (http://developer.amd.com/) OR
  2. OpenCL Programming Webinar Series (http://www.eventsvc.com/)