Correct performance analysis of an application is absolutely vital to optimize the performance on any architecture. A previous article describes several metrics recommended for a basic analysis of your application on the Intel® Xeon Phi™ coprocessor.
With Android L, the virtual machine compiler will move to ART (Android RunTime) and only ART for 64 bit. So what do we know about ART vs Dalvik and what does it mean for App code?
Intel has a vested interest in helping developers provide Android applications that run well (or even best) on Intel architecture. While Intel is working at the community level - optimizing Dalvik Java, V8 engine, and Bionic C; contributing to the code base; and providing releases with both 32 bit and 64-bit Kernels for IA; they are also creating new tools to help Android developers. Many of these focus on improving performance beyond that available with the default ARM translation layer for x86: libhoudini
Intel® Math Kernel Library includes powerful and versatile random number generators that have been optimized to take full advantage of Intel® Advanced Vector Extensions 2 (aka Intel® AVX2) introduced with the Haswell CPUs.
The lecture given here is the first part in the “Introduction to Parallel Programming” video series. This part endeavors to define parallel computing, explain why parallel computing is becoming mainstream, and explain why explicit parallel programming is necessary. This part sets the tone for the other 11 parts in the series.
Running time: 9:51