Missed visiting ArrayFire at GTC this year? We've got you covered! You can now check out the recordings of all our GTC 2015 talks and tutorials at your own convenience. Learn about accelerating your code from the best in the business.
Peter Andreas Entschev
This session will cover real-time feature tracking and object recognition in high resolution videos using GPUs and productive software libraries including ArrayFire. Feature tracking and object recognition are computer vision problems that have challenged researchers for decades. Over the last 15 years, numerous approaches were proposed to solve these problems, some of the most important being SIFT, SURF and ORB. Traditionally, these approaches are so computationally complex that processing more than a few frames per second is impossible. Using an NVIDIA K20 GPU with ORB, we are able to process more than 30 frames per second on images in the order of 10000x10000 pixels. Multiple quality and timing benchmarks will be presented, covering some of the most robust feature tracking methods.
In this session we will explore a new approach for counting triangles in networks that partitions the work at multiple parallel granularties. This new approach is highly scalable and is appropriate for both sparse and dense networks.
New to GPU computing and don't know where to start? Are you an expert in GPU computing and tired of writing your kernels from scratch? If that is the case, this tutorial is perfect for you! In today's tutorial we will introduce you to a simple and intuitive API that will make GPU computing highly productive and stress free. Last November, the ArrayFire library was open-source and made available to the general public. In today's talk we will walk you through the installation process, introduce the ArrayFire API, and show several applications that have been developed using the API.
Peter Andreas Entschev
Analyzing a massive data set? Need fast results? Need computer vision algorithms? Not sure when and where to start? The answer is here and now! In this tutorial we will give you the tools to bring your favorite computer vision algorithm to life. In this tutorial we will go over key challenges for implementing computer vision and machine learning algorithms on the GPU. We will walk you through several computer vision algorithms for the GPU (ORB, Fast, SIFT) and give you the hands experience to implement you own algorithms.
Looking for a simplified way to program machine learning algorithms? This tutorial will give you hands on experience implementing Deep Belief Networks using ArrayFire and other CUDA tools. Learn the best practices for implementing parallel versions of popular algorithms on GPUs. Instead of reinventing the wheel, you will learn where to find and how to use excellent versions of these algorithms already available in CUDA and ArrayFire libraries. You will walk away equipped with the best tools and knowledge for implementing accelerated machine learning algorithms.
Embedded systems are ubiquitous in today's world. The Tegra K1 is a front-runner for low-power efficient high performance embedded systems. In this tutorial we will share with you our experience in developing applications for the Tegra K1. We will show how the ArrayFire library and its simple API can be used to deploy stable applications for the Tegra K1 in a quick manner. Specifically, we will cover the zero-copy capabilities of CUDA and show how ArrayFire leverages that capability to efficiently run a very large number of algorithms in real-time. Using ArrayFire's graphic package it is easy to create stunning visuals using Tegra's OpenGL 4.4 capability, this too will be discussed in today's tutorial.