We know how excited a lot of you are to write your own computer vision applications with OpenCV (you haven’t heard of the Open Source Computer Vision library? Read more here - http://opencv.org). We’re pretty excited too, because OpenCV gives us a great opportunity to show off how awesome Tegra is. Our new OpenCV for Tegra is a fully-compatible, backend implementation of OpenCV for Android, but yields 2-20x faster processing on Tegra 3-based devices.
By now, you've probably heard about the amazing specs (and price!) on the new Google Nexus 7 (if you haven't, go here and don't come back until you're done reading about it! :-). Yes, it's amazing, but what's even MORE amazing is that you can use your Nexus 7 to deploy and debug your very own native Android applications.
With Google IO kicking off, we have a lot of announcements coming, but nothing makes us codeslingers more excited than smart tools that let you get your work done faster. To that end, I'm proud to present the latest powertool from NVIDIA: Nsight Tegra, Visual Studio Edition. We're excited because this tool brings one of the most powerful tools ever built, Microsoft Visual Studio, together with the flexible and popular Android platform.
In the mobile game development world, developer studios are discovering that the Tegra chipset offers a huge array of visual features that cannot be found on other mobile platforms. When developers create games designed to take advantage of Tegra features, they want to use every tool at their disposal to tune the performance of their game. PerfHUD ES is one type of tool that lets developers get the most out of their Tegra-based hardware.
We've just completed a succesful day training developers to debug and profile CUDA using NVIDIA Nsight for Visual Studio. If you missed the training today, join us Wednesday at GTC to learn about the powerful CUDA debugging features of Nsight that enable developers to quickly spot bugs or the comprehensive set of performance analysis tools provided by Nsight that allow developers to identify system level optimization opportunities as well as expensive and inefficient CUDA kernels.