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ANdroid
A few Benchmarks
Alberto Mancini - Francesca Tosi
15 Aprile 2014
HTML5Dev
alberto@jooink.com
http://github.com/jooink
http://www.jooink.com
http://jooink.blogspot.com
Itjustwasn'tready
Si riferiva a FB su mobile che era web based e stava facendo la prima intervista
“After the company’s continuous stock price drop” (ndr)
http://mashable.com/2012/09/11/mark-zuckerberg-post-ipo-interview/
fastbook
http://www.sencha.com/blog/the-making-of-fastbook-an-html5-love-story
fastbook, Sencha’s fb demo client
"... is really promising, but it is running in the “browser”.
When you make an “installable” app, you can only use WebView,
which is a second-class and slower browser."
It's a technology demo that shows what developers can do with HTML5 if they take the
right approach, and use the right frameworks and tools
(*)WebApps ● cached
● offline capable
● mobile-webapp-capable
● Homescreen-installed
● hosted app
● packaged app
https://developers.google.com/chrome/apps/
https://developer.mozilla.org/en-US/Apps/Build/installable_apps
secondo alcuni sono una liberazione dai marketplace (i.e. Google Play & AppStore)
secondo alcuni tra l’altro alcune tecnologie vengono ritardate dai produttori per non
perdere il potere che viene dai marketplace
GWT/MGWT Java to Javascript compiler
Emulated JRE
Widgets
Deferred Binding
Multiple Browsers
Compiled Resources
...
xché
una codebase e
tante 'incarnazioni'
dell'applicazione.
MarkerBasedAR
http://code.google.com/p/gwt-webgl/
http://code.google.com/p/gwt-nyartoolkit/
http://picshare.jooink.com
c.a. 25 fps
GWT-Boofcv GWT-Boofcv sample at:
http://goo.gl/3IJQgs
GWT-Boofcv GWT-Boofcv sample at:
http://goo.gl/3IJQgs
strategy
● native ?
● hybrid ?
● browser-based ?
Native,NDK
capire quanto possiamo guadagnare in
termini di performance basandoci su
differenti tecnologie
Applicazione
Android NDK
che usa
librerie native
Applicazione
Android Java
per Dalvik
Benchmarks
il medesimo algoritmo implementato con tecnologie
diverse eseguito sul medesimo dispositivo
Benchmark
In entrambe le applicazioni precedenti una
operazione semplice ma computazionalmente costosa
è fare la conversione in grayscale delle immagini
Rules RGB
jbyte[1024*1024*3]
Average on 1000 runs
gray
jbyte[1024*1024]
1
0.299*R+0.587*G+0.114*B → Y
2
3
Testdevice Galaxy Tab3
Intel Atom CPU Z2560
2 core - 1.60 GHz
Android 4.4.2
Architettura i686
Competitors Benchmarks on:
Java <byte>
NDK <jbyte>
IPP <Ipp8u>
Competitors Benchmarks on:
Java <byte>
NDK <jbyte>
IPP <Ipp8u>
Intel Integrated Performance Primitives
Competitors Benchmarks on:
Java <byte>
NDK <jbyte>
IPP <Ipp8u>
Chrome <Uint8Array>
Firefox <Uint8Array>
Aurora <Uint8Array>
Codice(Java) Java:
void compute(byte[] in, byte[] out) {
for(int i=0, j=0; i< out.length; i++, j+=3)
out[i] =
(byte)(0.299 * in[j] + 0.587 * in[j+1] + 0.114 * in[j+2]);
}
Using FP arithmetics !!
We cannot (at least without asm.js) coherce
numbers to integers in js so we chosen not to
optimize here.
Details(NDK)
https://developer.android.com/tools/sdk/ndk/index.html
La documentazione non e’ il punto di forza dell’
NDK
https://github.com/jooink/ndk-cpuid
● start the (intel) avd
● enter in the jni folder & execute 'ndk-build'
● from the CPUIDApp root dir execute 'ant debug'
● adb install -r bin/CPUIdApp-debug.apk
Codice(C,jni)
void JNICALL … jbyteArray in, jbyteArray out) {
jsize len_out = (*env)->GetArrayLength(env, out);
...
jbyte *body_out = (*env)->GetByteArrayElements(env, out, 0);
for(i=0, j=0; i< len_out; i++, j+=3)
body_out[i] = (jbyte)(0.299 * body_in[j] + … );
(*env)->ReleaseByteArrayElements(env, in, body_in, 0);
...
}
Details(IPP) Bundled in BeaconMountain beh … la ‘preview’
http://software.intel.com/en-us/intel-ipp
… ed avete la IPP per android x86 !!!
Scaricate la versione per linux ( la evaluation, or 199$ :( )
Installatela ( vi serve una macchina linux )
Copiate le librerie (*.a) e gli include (*.h)
(32bit statici non-threaded)
Codice(IPP)
… jni ...
IppiSize srcRoi = { 1024, 1024 };
Ipp8u* pSrc = body_in;
Ipp8u* pDst = body_out;
ippiRGBToGray_8u_C3C1R(pSrc ,1024, pDst, 1024, srcRoi);
… jni ...
Resultsnative
Resultsnative
min
max
avg
Codice(js)
var len_out = outb.length;
var i,j;
for(i=0, j=0; i< len_out; i++, j+=3)
outb[i] = (0.299 * inb[j] + 0.587 * inb[j+1] + 0.114 *inb[j+2]);
var buffer_in = new ArrayBuffer(size*3);
var buffer_out = new ArrayBuffer(size);
var inb = new Uint8Array(buffer_in);
var out = new Uint8Array(buffer_out);
Resultsjs
min
max
avg
Grazie Francesca Tosi
francesca@jooink.com
Alberto Mancini
alberto@jooink.com
Github: github.com/jooink/
www.jooink.com
jooink.blogspot.com
Details(HAXM) Bundled in BeaconMountain
http://software.intel.com/en-us/android/articles/intel-hardware-
accelerated-execution-manager
cambia la vita con le immagini x86
Attenzione pianta OSX, maverick
installare lo hotfix

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