For the first time Intel® VTune™ Amplifier 2014 for Systems brings the most important, core capability of determining the hotspot in the C/C++ portion of your application to most Android* devices on Intel® processors (including rooted, not-rooted devices and with or without version compatible device drivers), such as those available at http://software.intel.com/en-us/android/get-device. This article will concentrate on the options required to make this work on non-rooted devices.
We are proud to announce that the Android Zone here on the Intel Developer Zone has been completely redesigned, with a focused, simple approach and a fully responsive design, so it looks great on every device. Check it out, and tell everyone you know who makes Android apps!
I found that reduction algorithm from NVidia SDK works on HD Graphics 4400 but don't work on Intel CPU i5.
I've expected that Nvidia algorithm works everywhere OR work only on Nvidia hardware so that difference in behavior between CPU and GPU on SAME machine looks strange for me.
Reduction algorithm and C# + OpenCL.NET unit test are in attachments. Unit test fails on Intel CPU with size = 4.
What differences in kernel execution exists between CPU and GPU? How can I fix the problem?
A question from newbie.
I am trying to use a reduction algorithm from NVidia SDK. It works correctly on Nvidia Discrete GPU, Intel HD Graphics 4400, but don't work on Intel CPU (Haswell i5).
Reduction method source:
This article presents the advantages of developing embedded digital video surveillance systems to run on 4th generation Intel® Core™ processor with Intel® HD Graphics, in combination with the Intel® System Studio software development suite. While Intel® HD Graphics is useful for developing many types of computer vision functionalities in video management software; Intel® System Studio is an embedded application development suite that is useful in developing robust digital video surveillance applications.
hackTECH: Intel’s First Major League Hackathon
Code size optimization is a key factor, especially critical in embedded systems requiring code size reduction at the cost of application speed! Application developed for an embedded system is generally tuned for a particular processor with a finite memory size and hence memory is the main cost component of an embedded product. Directly impacting the memory requirement in an embedded system is the code size of the application, as reduced code size means lesser memory usage and lower cost of the product.