For example, if the time is mostly spent in sqrt(), cos(), and sin(), that already is native code, and you won't get much faster. Well, until you profile the code under Traceview, that will be difficult to answer.
TESTOUT PC PRO 2.2.3 PRACTICE TEST ANDROID
Depending upon your tablet, you may not have a floating-point co-processor, and doing floating-point math on the CPU sans a floating-point co-processor is very very slow.ĭoes it help if I write the code in C and use Android NDK? In production code, you'd never do that, at least if you wanted to keep your job.įloating-point operations. Your micro-benchmark is chewing through ~3MB of heap space. Memory allocation and garbage collection. I suspect that if you ran this through Traceview, and looked at LogCat, you would find that your time is being spent in two areas: Hence, savvy programmers would use tools like Traceview to get a better sense of where their time is being taken. And, the only decent way to interpret micro-benchmarks is with micro-measurements. Micro-benchmarks only measure the performance of the micro-benchmark. Private static double abs_val_vec (double v) Public static double rot_mat(double v, double t) ("Time Taken ms: "+(-startD.getTime()+endD.getTime())) The benchmark code in Java: package mainpackage Has anyone had a similar experience? Any suggestion? Does it help if I write the code in C and use Android NDK? I didn't expect that the tablet would be close to PC - but I didn't expect it is ~150x slower either!! THE VERY VERY SAME FUNCTION takes 17000 ms. Coded in Java, it takes ~115ms for my old PC to finish the work. Has anyone compared the processing power of mobile devices with PC? I have a very simple matrix work.
0 kommentar(er)
