Understanding Android Benchmarks
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Transcript of Understanding Android Benchmarks
Understanding Android Benchmarks
“freedom” koan-sin tan [email protected]
OSDC.tw, Taipei Apr 11th, 2014
1
disclaimers
• many of the materials used in this slide deck are from the Internet and textbooks, e.g., many of the following materials are from “Computer Architecture: A Quantitative Approach,” 1st ~ 5th ed
• opinions expressed here are my personal one, don’t reflect my employer’s view
2
who am i
• did some networking and security research before
• working for a SoC company, recently on
• big.LITTLE scheduling and related stuff
• parallel construct evaluation
• run benchmarking from time to time
• for improving performance of our products, and
• know what our colleagues' progress
3
• Focusing on CPU and memory parts of benchmarks
• let’s ignore graphics (2d, 3d), storage I/O, etc.
4
Blackbox!
• google image search “benchmark”, you can find many of them are Android-related benchmarks
• Similar to recently Cross-Strait Trade in Services Agreement (TiSA), most benchmarks on Android platform are kinda blackbox
5
Is Apple A7 good?• When Apple released the new
iPhone 5s, you saw many technical blog showed some benchmarks for reviews they came up
• commonly used ones:
• GeekBench
• JavaScript benchmarks
• Some graphics benchmarks
• Why? Are they right ones? etc.
e.g., http://www.anandtech.com/show/7335/the-iphone-5s-review6
open blackbox7
Android Benchmarks
8
No, not improvement in this way9
http://www.anandtech.com/show/7384/state-of-cheating-in-android-benchmarks
Assuming there is not cheating, what we we
can do?
Outline
• Performance benchmark review
• Some Android benchmarks
• What we did and what still can be done
• Future
11
To quote what Prof. Raj Jain quoted
• Benchmark v. trans. To subject (a system) to a series of tests in order to obtain prearranged results not available on competitive systems
From: “The Devil’s DP Dictionary” S. Kelly-Bootle
12
Why benchmarking
• We did something good, let check if we did it right
• comparing with own previous results to see if we break anything
• We want to know how good our colleagues in other places are
13
What to report?
• Usually, what we mean by “benchmarking” is to measure performance
• What to report?
• intuitive answer: how many things we do in certain period of time
• yes, time. E.g., MIPS, MFLOPS, MiB/s, bps
14
MIPS and MFLOPS
• MIPS (Million Instruc0ons per Second), MFLOPS (Million Floa0ng-‐Point Opera0ons per Second)
• All instruc0ons are not created equal – CISC machine instruc0ons usually accomplish a lot more than those of RISC machines, comparing the instruc0ons of a CISC machine and a RISC machine is similar to comparing La0n and Greek
15
MIPS and what’s wrong with them
• MIPS is instruc0on set dependent, making it difficult to compare MIPS of one computers with different ISA
• MIPS varies between programs on the same computers; and most importantly,
• MIPS can vary inversely to performance –w/ hardware FP, generally, MIPS is smaller
16
MFLOPS and what’s wrong with them
• Applied only to programs with floa0ng-‐point opera0ons
• Opera0ons instead of instruc0ons, but s0ll –floa0ng-‐point instruc0ons are different on machines different ISAs
–Fast and slow floa0ng-‐point opera0ons • Possible solu0on: weight and source code level count –ADD, SUB, COMPARE : 1 –DIVIDE, SQRT: 2 –EXP, SIN: 4
17
• The best choice of benchmarks to measure performance is real applica0ons
18
Problema0c benchmarks
• Kernel: small, key pieces of real applica0ons, e.g., linpack
• Toy programs: 100-‐line programs from beginning programming assignments, e.g., quicksort
• Synthe0c benchmarks: fake programs invented to try to match the profile and behavior of really applica0ons, e.g., Dhrystone
19
Why they are disreputed?
• Small, fit in cache • Obsolete instruc0on mix • Uncontrolled source code • Prone to compiler tricks • Short run0mes on modern machines • Single-‐number performance characteriza0on with a single benchmark
• Difficult to reproduce results (short run0me and low-‐precision UNIX 0mer)
20
Dhrystone
• Source –hhp://homepages.cwi.nl/~steven/dry.c
• < 1000 LoC –Size of CA15 binary compiled with bionic
• Instruc0ons: ~ 14 KiB
text data bss dec
13918 467 10266 24660
21
Whetstone
• Dhrystone is a pun on Whetstone
• Source code: hhp://www.netlib.org/benchmark/whetstone.c
Test MFLOPS MOPS ms
N1 float 119.78 0.16
N2 float 171.98 0.78 N3 if 154.25 0.67 N4 fixpt 397.48 0.79 N5 cos 19.08 4.36 N6 float 84.22 6.41 N7 equal 86.84 2.13 N8 exp 5.95 6.26 MWIPS 463.97 21.55
22
More on Synthe0c benchmarks• The best known examples of synthe0c benchmarks are Whetstone and Dhrystone
• Problems: – Compiler and hardware op0miza0ons can ar0ficially inflate performance of these benchmarks but not of real programs
– The other side of the coin is that because these benchmarks are not natural programs, they don’t reward op0miza0ons of behaviors that occur in real programs
• Examples: – Op0mizing compilers can discard 25% of the Dhrystone code; examples include loops that are only executed once, making the loop overhead instruc0ons unnecessary
– Most Whetstone floa0ng-‐point loops execute small numbers of 0mes or include calls inside the loop. These characteris0cs are different from many real programs
– Some more discussion in 1st edi0on of the textbook
23
LINPACK
• LINPACK: a floa0ng point benchmark from the manual of LINPACK library
• Source –hhp://www.netlib.org/benchmark/linpackc –hhp://www.netlib.org/benchmark/linpackc.new
• 883 LoC –Size of CA15 binary compiled with bionic
• Instruc0ons: ~ 13 KiBtext data bss dec
12670 408 0 1308624
25
CoreMark (1/2)
• CoreMark is a benchmark that aims to measure the performance of central processing units (CPU) used in embedded systems. It was developed in 2009 by Shay Gal-‐On at EEMBC and is intended to become an industry standard, replacing the an0quated Dhrystone benchmark
• The code is wrihen in C code and contains implementa0ons of the following algorithms: – Linked list processing. –Matrix (mathema0cs) manipula0on (common matrix opera0ons), – state machine (determine if an input stream contains valid numbers), and
– CRC • from wikipedia
26
CoreMark (2/2)
name LoC
core_list_join.c 496
core_matrix.c 308
core_stat.c 277
core_util.c 210
• CoreMark vs. Dhrystone –Repor0ng rule –Use of library calls, e.g., malloc() is avoided
–CRC to make sure data are corrected
• However, CoreMark is a kernel + synthe0c benchmark, s0ll quite small footprint
text data bss dec
18632 456 20 1910827
So?
• Too overcome the danger of placing eggs in one basket, collec0ons of benchmark applica0ons, called benchmark suites, are popular measure of performance of processors with variety of applica0ons
• Standard Performance Evalua0on Corpora0on (SPEC)
28
29
Why CPU2000 in 2010s?
• Why ARM s0cks with SPEC CPU2000 instead of CPU2006 –1999 q4 results, earliest available CPU2000 results (hhp://www.spec.org/cpu2000/results/res1999q4/) • CINT2000 base: 133 – 424 • CFP2000 base: 126 – 514
–2005 Opteron 144, 1.8 GHz • 1,440 (CA15 1.9 GHz reported nVidia is 1,168)
–CPU2006 requires much more DRAM, 1 GiB DRAM is not enough
name CA9 CA7 CA15 Krait
SPECint 200 356 320 537 326
SPECfp 2000 298 236 567 350
All normalized to 1.0 GHz
30
SPEC numbers from Quan0ta0ve Approach 5th Edi0on
31
How long does SPEC CPU2000 take?
• About 1 hrs to compile • Run0me: Sum of base run0me mul0plied by 3 – E.g., 1.7 GHz CA15, (2256+3229) x 3 = 16,455 s ~= 4.57 hr
– For 1.0 GHz: 4.57 x 1.7 = 7.77 hr
– For CA7 assuming twice slower: 7.77 * 2 = 15.54 hr
BenchmarkReference Base BaseTime Runtime Ratio
164.gzip 1400 215 652175.vpr 1400 198 707176.gcc 1100 94.8 1161181.mcf 1800 266 677186.crafty 1000 118 850197.parser 1800 291 619252.eon 1300 87.8 1480253.perlbmk 1800 172 1045254.gap 1100 107 1026255.vortex 1900 211 899256.bzip2 1500 203 740300.twolf 3000 399 752SPECint_base2000 2256 854
BenchmarkReference Base BaseTime Runtime Ratio
68.wupwise 1600 162 991
171.swim 3100 389 797
172.mgrid 1800 339 532
173.applu 2100 241 870
177.mesa 1400 112 1254
178.galgel 2900 201 1444
179.art 2600 195 1332
183.equake 1300 157 828
187.facerec 1900 183 1036
188.ammp 2200 353 623
189.lucas 2000 134 1491
191.fma3d 2100 212 988
200.sixtrack 1100 241 456
301.apsi 2600 310 839
SPECfp_base2000 435 3229 909.6
32
Figure 1.16 SPEC2006 programs and the evolu0on of the SPEC benchmarks over 0me, with integer programs above the line and floa0ng-‐point programs below the line. Of the 12 SPEC2006 integer programs, 9 are wrihen in C, and the rest in C++. For the floa0ng-‐point programs, the split is 6 in Fortran, 4 in C++, 3 in C, and 4 in mixed C and Fortran. The figure shows all 70 of the programs in the 1989, 1992, 1995, 2000, and 2006 releases. The benchmark descrip0ons on the les are for SPEC2006 only and do not apply to earlier versions. Programs in the same row from different genera0ons of SPEC are generally not related; for example, fpppp is not a CFD code like bwaves. Gcc is the senior ci0zen of the group. Only 3 integer programs and 3 floa0ng-‐point programs survived three or more genera0ons. Note that all the floa0ng-‐point programs are new for SPEC2006. Although a few are carried over from genera0on to genera0on, the version of the program changes and either the input or the size of the benchmark is osen changed to increase its running 0me and to avoid perturba0on in measurement or domina0on of the execu0on 0me by some factor other than CPU 0me.
33
EEMBC• Embedded Microprocessor Benchmark Consor0um (EEMBC): 41 kernels used to predict performance of different embedded applica0ons: – Automo0ve/industrial – Consumer – Networking – Office automa0on – Telecommunica0on
• 3rd edi0on showed some EEMBC results, 4th edi0on changed the mind • Unmodified performance and “full-‐fury” performance • Kernel, repor0ng op0ons
– Not a good predictor of rela0ve performance of different embedded computers
34
Report benchmark results
• Reproducible –Machine configura0on (Hardware, sosware (OS, compiler etc.))
• Summarizing results – You should not add different numbers
• Some use weighted average –Ra0o, compare with a reference machine
• Geometric ra1o – The geometric mean of the ra0os is the same as the ra0os of geometric means
– The ra0o of the geometric means is equal to the geometric mean of the performance ra0os
35
Geometric mean
36
• Fallacy: Benchmarks remain valid indefinitely –Ability to resist “benchmark engineering” or “benchmarke0ng”
–gcc is the only survivor from SPEC89 • Almost 70% of all programs from SPEC2000 or earlier were dropped from the next release
37
Other benchmarks
• Stream –To test memory bandwidth –It also tests floa0ng-‐point performance –Op0ons of floa0ng-‐point (double, 8 bytes) array
• copy, scale, add, triad
• lmbench –Micro benchmark to measure sosware/hardware overhead from sosware perspec0ve
– lmbench paper (1996), hhp://www.bitmover.com/lmbench/lmbench-‐usenix.pdf
name kernel bytes/iter FLOPS/iter
COPY a(i) = b(i) 16 0
SCALE a(i) = q*b(i) 16 1
SUM a(i) = b(i) + c(i) 24 1
TRIAD a(i) = b(i) + q*c(i) 24 2
38
Stream 5.10
for (k=0; k<NTIMES; k++) { times[0][k] = mysecond(); for (j=0; j<STREAM_ARRAY_SIZE; j++) c[j] = a[j]; times[0][k] = mysecond() - times[0][k]; times[1][k] = mysecond(); for (j=0; j<STREAM_ARRAY_SIZE; j++) b[j] = scalar*c[j]; times[1][k] = mysecond() - times[1][k]; times[2][k] = mysecond(); for (j=0; j<STREAM_ARRAY_SIZE; j++) c[j] = a[j]+b[j]; times[2][k] = mysecond() - times[2][k]; times[3][k] = mysecond(); for (j=0; j<STREAM_ARRAY_SIZE; j++) a[j] = b[j]+scalar*c[j]; times[3][k] = mysecond() - times[3][k]; }
39
lmbench
• lmbench is a micro-‐benchmark suite designed to focus ahen0on on the basic building blocks of many common system applica0ons, such as databases, simula0ons, sosware development, and networking
40
Parallel? Let’s look at other SPEC benchmarks
• SPECapc for 3ds Max™ 2011, performance evalua0on sosware for systems running Autodesk 3ds Max 2011.
• SPECapcSM for Lightwave 3D 9.6, performance evalua0on sosware for systems running NewTek LightWave 3D v9.6 sosware.
• SPECjbb2005, evaluates the performance of server side Java by emula0ng a three-‐0er client/server system (with emphasis on the middle 0er).
• SPECjEnterprise2010, a mul0-‐0er benchmark for measuring the performance of Java 2 Enterprise Edi0on (J2EE) technology-‐based applica0on servers.
• SPECjms2007, Java Message Service performance
• SPECjvm2008, measuring basic Java performance of a Java Run0me Environment on a wide variety of both client and server systems.
• SPECapc, performance of several 3D-‐intensive popular applica0ons on a given system
• SPEC MPI2007, for evalua0ng performance of parallel systems using MPI (Message Passing Interface) applica0ons.
• SPEC OMP2001 V3.2, for evalua0ng performance of parallel systems using OpenMP (hhp://www.openmp.org) applica0ons.
• SPECpower_ssj2008, evaluates the energy efficiency of server systems.
• SPECsfs2008, File server throughput and response 0me suppor0ng both NFS and CIFS protocol access
• SPECsip_Infrastructure2011, SIP server performance
• SPECviewperf 11, performance of an OpenGL 3D graphics system, tested with various rendering tasks from real applica0ons
• SPECvirt_sc2010 ("SPECvirt"), evaluates the performance of datacenter servers used in virtualized server consolida0on
41
PARSEC• The Princeton Applica0on Repository for Shared-‐Memory Computers (PARSEC) is a benchmark suite composed of mul0threaded programs. The suite focuses on emerging workloads and was designed to be representa0ve of next-‐genera0on shared-‐memory programs for chip-‐mul0processors
• Didn’t really use it yet • hhp://parsec.cs.princeton.edu/
Workload
Parallelization Model
Pthreads OpenMP Intel TBB
blackscholes Yes Yes Yes
bodytrack Yes Yes Yes
canneal Yes No No
dedup Yes No No
facesim Yes No No
ferret Yes No No
fluidanimate Yes No Yes
freqmine No Yes No
raytrace Yes No No
streamcluster Yes No Yes
swaptions Yes No Yes
vips Yes No No
x264 Yes No No
42
Are Dhrystone usefully?
• Yes, if you know the limitation of them
• Don't do marketing as those benchmarks mean real user perceived performance
43
iPhone'5s' iPhone'5s'32,bit' CA15' CA7' Krait'400'DMIPS/MHz' 7.47'' 5.70'' 2.71'' 1.67'' 2.46''
0.00''1.00''2.00''3.00''4.00''5.00''6.00''7.00''8.00''
DMIPS/MHz)
A7 Dhrystone44
iPhone'5s' iPhone'5s'32,bit' 'CA15' CA7' Krait'400'
MFLOPS/GHz' 722' 723' 449' 119' 299'
0'
100'
200'
300'
400'
500'
600'
700'
800'
MFLOPS/GHz+
A7 linpack MFLOPS45
iPhone'5s' iPhone'5s'32,bit' CA15' CA7' Krait'400'CoreMark/MHz' 5.72'' 4.45'' 3.67'' 2.46'' 3.30''
0.00''
1.00''
2.00''
3.00''
4.00''
5.00''
6.00''
7.00''
CoreMark/MHz+
A7 CoreMark46
Different items
• Example, GeekBench 3
• Arithmetic mean with different weight? How?
• Good properties of geometric mean
47
Source code
• So far what we talked about are all software with source code available, either publicly/freely, e.g., Dhrystone or little amount of $, e.g., SPEC CPU
48
• Benchmark scores/results usually depend on compiler, complier flags, processors, and systems
49
Outline
• Performance benchmark review
• Some Android benchmarks
• What we did and what still can be done
• Future
50
Back to Android
• What kinds of Benchmarks are available, or used to compare performance
• Apps with native benchmarks: Antutu, GeekBench
• Java apps, e.g., Quadrant
• Hybrid: with both native and Java, e.g., AndEBench and CF-Bench
• We also use SPEC CPU2000 and other stuff internally
51
Ars Technica ListarrayOfPackageInfo[0] = new PackageInfo("com.aurorasoftworks.quadrant.ui.standard", false); arrayOfPackageInfo[1] = new PackageInfo("com.aurorasoftworks.quadrant.ui.advanced", false); arrayOfPackageInfo[2] = new PackageInfo("com.aurorasoftworks.quadrant.ui.professional", false); arrayOfPackageInfo[3] = new PackageInfo("com.redlicense.benchmark.sqlite", false); arrayOfPackageInfo[4] = new PackageInfo("com.antutu.ABenchMark", false); arrayOfPackageInfo[5] = new PackageInfo("com.greenecomputing.linpack", false); arrayOfPackageInfo[6] = new PackageInfo("com.greenecomputing.linpackpro", false); arrayOfPackageInfo[7] = new PackageInfo("com.glbenchmark.glbenchmark27", false); arrayOfPackageInfo[8] = new PackageInfo("com.glbenchmark.glbenchmark25", false); arrayOfPackageInfo[9] = new PackageInfo("com.glbenchmark.glbenchmark21", false); arrayOfPackageInfo[10] = new PackageInfo("ca.primatelabs.geekbench2", false); arrayOfPackageInfo[11] = new PackageInfo("com.eembc.coremark", false); arrayOfPackageInfo[12] = new PackageInfo("com.flexycore.caffeinemark", false); arrayOfPackageInfo[13] = new PackageInfo("eu.chainfire.cfbench", false); arrayOfPackageInfo[14] = new PackageInfo("gr.androiddev.BenchmarkPi", false); arrayOfPackageInfo[15] = new PackageInfo("com.smartbench.twelve", false); arrayOfPackageInfo[16] = new PackageInfo("com.passmark.pt_mobile", false); arrayOfPackageInfo[17] = new PackageInfo("se.nena.nenamark2", false); arrayOfPackageInfo[18] = new PackageInfo("com.samsung.benchmarks", false); arrayOfPackageInfo[19] = new PackageInfo("com.samsung.benchmarks:db", false); arrayOfPackageInfo[20] = new PackageInfo("com.samsung.benchmarks:es1", false); arrayOfPackageInfo[21] = new PackageInfo("com.samsung.benchmarks:es2", false); arrayOfPackageInfo[22] = new PackageInfo("com.samsung.benchmarks:g2d", false); arrayOfPackageInfo[23] = new PackageInfo("com.samsung.benchmarks:fs", false); arrayOfPackageInfo[24] = new PackageInfo("com.samsung.benchmarks:ks", false); arrayOfPackageInfo[25] = new PackageInfo("com.samsung.benchmarks:cpu !!CPU and Memory related: Quadrant, Antutu, linpack, GeekBench, AndEBench (coremark), CaffeineMark, Pi, PassMark, Samsung’s benchmark
52
Antutu 3.x• CPU: integer, floating point
• memory: RAM
• Graphics: 2D, 3D
• I/O: Database, SD read, SD write
!
!
• What are you benchmarking
• What's you workload
• How to calculate scores
53
What on earth are they doing?
• Actually no public available information
• But, with good enough background knowledge and proper tools (we’ll talk about these later), we can figure it out
• It turns out most of them are from the BYTE nbench (http://en.wikipedia.org/wiki/NBench)
54
AnTuTu 3.x CPU and Memory Tests
nbench item Used by Antutu Antutu part
Antutu percentage on progress bar Order nbench category
NUMERIC SORT yes Integer 27% 4 integer
STRING SORT yes RAM 1% 1 memory
BITFIELD yes RAM 1% 2 memory
FP EMULATION no
FOURIER yes floating 47% 7 floating point
ASSIGNMENT yes RAM 8% 3 memory
IDEA yes Integer 27% 5 integer
HUFFMAN yes Integer 34% 6 integer
NEURAL NET no
LU DECOMPOSITION no
55
More close look▪ RAM
– String sort: • string Heap sort: StrHeapSort() • MoveMemory() à memmove()
– Bit Field: • Bit field test: DoBitops()
– Assignment: • Task Assignment test: DoAssignment()
▪ Integer – Numeric sort:
• Numeric heap sort: NumHeapSort() – IDEA:
• IDEA encryption and decryption: cipher_idea() – Huffman:
• Huffman encoding
▪ Floating point: – Fourier:
• Fourier transform: pow(), sin(), cos()
56
for(i=top; i>0; --i) !{ !
"strsift(optrarray,strarray,numstrings,0,i); !!
"/* temp = string[0] */!"tlen=*strarray; !"MoveMemory((farvoid *)&temp[0], /* Perform exchange */ !" "(farvoid *)strarray, !" "(unsigned long)(tlen+1)); !
!!
"/* string[0]=string[i] */!"tlen=*(strarray+*(optrarray+i)); !"stradjust(optrarray,strarray,numstrings,0,tlen); !"MoveMemory((farvoid *)strarray, !" "(farvoid *)(strarray+*(optrarray+i)), !" "(unsigned long)(tlen+1)); !
!"/* string[i]=temp */!"tlen=temp[0]; !"stradjust(optrarray,strarray,numstrings,i,tlen); !"MoveMemory((farvoid *)(strarray+*(optrarray+i)), !" "(farvoid *)&temp[0], !" "(unsigned long)(tlen+1)); !
!}
String Sort in NBench• Sorts an array of strings
of arbitrary length
• Test memory movement performance
• Non-sequential performance of cache, with added burden that moves are byte-wide and can occur on odd address boundaries
57
Bit field in NBench• Executes 3 bit manipulation functions
• Exercises "bit twiddling“ performance. Travels through memory bit-by-bit in a sequential fashion; different from sorts in that data is merely altered in place
• Operations:
• Set: OR 1
• Clear: AND 0
• Toggle: XOR
• Set, clear: ToggleBitRun()
• Toggle: FlipBitRun()
static void ToggleBitRun(farulong *bitmap, /* Bitmap */ ulong bit_addr, /* Address of bits to set */ ulong nbits, /* # of bits to set/clr */ uint val) /* 1 or 0 */ { unsigned long bindex; /* Index into array */ unsigned long bitnumb; /* Bit number */ !while(nbits--) { #ifdef LONG64 bindex=bit_addr>>6; /* Index is number /64 */ bitnumb=bit_addr % 64; /* Bit number in word */ #else bindex=bit_addr>>5; /* Index is number /32 */ bitnumb=bit_addr % 32; /* bit number in word */ #endif if(val) bitmap[bindex]|=(1L<<bitnumb); else bitmap[bindex]&=~(1L<<bitnumb); bit_addr++; } return; }
58
Assignment in NBench• The test moves through
large integer arrays in both row-wise and column-wise fashion. Cache/memory with good sequential performance should see a boost (memory is altered in place -- no moving as in a sort operation)
• Yes, basically, sequential array assignment with some kind of table look-ups
/* ** Step through rows. For each one that is not currently ** assigned, see if the row has only one zero in it. If so, ** mark that as an assigned row/col. Eliminate other zeros ** in the same column. */ for(i=0;i<ASSIGNROWS;i++) { numzeros=0; for(j=0;j<ASSIGNCOLS;j++) if(tableau[i][j]==0L) if(assignedtableau[i][j]==0) { numzeros++; selected=j; } if(numzeros==1) { numassigns++; totnumassigns++; assignedtableau[i][selected]=1; for(k=0;k<ASSIGNROWS;k++) if((k!=i) && (tableau[k][selected]==0)) assignedtableau[k][selected]=2; } }
59
Numeric Sort in NBench
• Sorts an array of long integers with heap sort
• Generic integer performance. Should exercise non-sequential performance of cache (or memory if cache is less than 8K). Moves 32-bit longs at a time, so 16-bit processors will be at a disadvantage
static void NumHeapSort(farlong *array, ulong bottom, /* Lower bound */ ulong top) /* Upper bound */ { ulong temp; /* Used to exchange elements */ ulong i; /* Loop index */ !/* ** First, build a heap in the array */ for(i=(top/2L); i>0; --i) NumSift(array,i,top); !/* ** Repeatedly extract maximum from heap and place it at the ** end of the array. When we get done, we'll have a sorted ** array. */ for(i=top; i>0; --i) { NumSift(array,bottom,i); temp=*array; /* Perform exchange */ *array=*(array+i); *(array+i)=temp; } return;
60
static void cipher_idea(u16 in[4], !" "u16 out[4], !" "register IDEAkey Z) !
{ !register u16 x1, x2, x3, x4, t1, t2; !/* register u16 t16; !register u16 t32; */!int r=ROUNDS; !!x1=*in++; !x2=*in++; !x3=*in++; !x4=*in; !!do { !
"MUL(x1,*Z++); !"x2+=*Z++; !"x3+=*Z++; !"MUL(x4,*Z++); !
!"t2=x1^x3; !"MUL(t2,*Z++); !"t1=t2+(x2^x4); !"MUL(t1,*Z++); !"t2=t1+t2; !
!"x1^=t1; !"x4^=t2; !
!"t2^=x2; !"x2=x3^t1; !"x3=t2; !
} while(--r); !MUL(x1,*Z++); !*out++=x1; !*out++=x3+*Z++; !*out++=x2+*Z++; !MUL(x4,*Z); !*out=x4; !return; !}
IDEA Encryption in NBench
• IDEA: a new block cipher when nbench was in development
• Moves through data sequentially in 16-bit chunks
61
Huffman in NBench
• Everybody knows Huffman code, right?
• A combination of byte operations, bit twiddling, and overall integer manipulation
..... /* ** Huffman tree built...compress the plaintext */ bitoffset=0L; /* Initialize bit offset */ for(i=0;i<arraysize;i++) { c=(int)plaintext[i]; /* Fetch character */ /* ** Build a bit string for byte c */ bitstringlen=0; while(hufftree[c].parent!=-2) { if(hufftree[hufftree[c].parent].left==c) bitstring[bitstringlen]='0'; else bitstring[bitstringlen]='1'; c=hufftree[c].parent; bitstringlen++; } .....
62
Fourier in NBench
• No, not FFT,
• Good measure of transcendental and trigonometric performance of FPU. Little array activity, so this test should not be dependent of cache or memory architecture
static double thefunction(double x, /* Independent variable */!" "double omegan, /* Omega * term */!
" "int select) /* Choose term */!{ !/* !
** Use select to pick which function we call. !*/ !
switch(select) !{ !
"case 0: return(pow(x+(double)1.0,x)); !
"case 1: return(pow(x+(double)1.0,x) * cos(omegan * x)); !"case 2: return(pow(x+(double)1.0,x) * sin(omegan * x)); !
}
63
Neural Net in NBench
• A robust algorithm for solving linear equations
• Small-array floating-point test heavily dependent on the exponential function; less dependent on overall FPU performance
64
LU Decomposition in NBench
• LU Decomposition
• Yes, the LU decomposition you learned in linear algebra
• A floating-point test that moves through arrays in both row-wise and column-wise fashion. Exercises only fundamental math operations (+, -, *, /)
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GeekBench• A cross-platform one
• The only publicly available one we could use to compare Android, iOS, and other platforms
• Quite clearly described test items
• http://support.primatelabs.com/kb/geekbench/geekbench-3-benchmarks
• Explaining how to interpret results
• http://support.primatelabs.com/kb/geekbench/interpreting-geekbench-3-scores
• Source code available if you pay
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Vellamo
• HTML5
• Metal: Dhrystone, Linpack, Branch-K, Stream 5.9, RamJam, Storage
• some are well-known; some are written by Quic?
• Anyway, all of them are described at http://www.quicinc.com/vellamo/test-descriptions/
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CFBench
• Used by some people, ‘cause
• Test both Java and native version
• its author is quite active in xda developer forum
• Some problems
• no good description of tests
• some code is wrong, e.g.,
• its Native Memory Read test is not testing memory read, ‘cause malloc()ed array is not initialized
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Outline
• Performance benchmark review
• Some Android benchmarks
• What we did and what still can be done
• Future
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How do we improve benchmark
performance
70
• In the good old days, we have source code, we compile and run benchmark programs
• In current Android ecosystem
• Usually we don’t have source
• Profiling: oprofile, perf, DS-5
• profiling sometimes doesn’t report real bottleneck function, e.g., static functions usually are inlined and don’t have symbol in shipped binaries
• binutils: nm, readelf, objdump, gdb
• Improving libraries, e.g., libc and libm, and runtime system, e.g., JIT of Dalvik, used by those benchmarks
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Antutu 3.x
• memmove() in bionic --> bcopy() in C
• rewrite with NEON assembly code
• pow(), sin(), cos() in C
• rewrite them with assembly
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bcopy() in bionic
• MoveMemory() in nbench -> memmove() in bionic -> bcopy() in bionic
• memcpy() assembly in bionic and there are processor specific ones (CA9, CA15, Krait). NEON (vector load/store) helps
• not for bcopy()
in bionic/libc/bionic/memmove.c !void *memmove(void *dst, const void *src, size_t n) { const char *p = src; char *q = dst; /* We can use the optimized memcpy if the source and destination * don't overlap. */ if (__builtin_expect(((q < p) && ((size_t)(p - q) >= n)) || ((p < q) && ((size_t)(q - p) >= n)), 1)) { return memcpy(dst, src, n); } else { bcopy(src, dst, n); return dst; } }
in bionic/libc/string/bcopy.c /* * Copy a block of memory, handling overlap. * This is the routine that actually implements * (the portable versions of) bcopy, memcpy, and memmove. */ #ifdef MEMCOPY void * memcpy(void *dst0, const void *src0, size_t length) #else #ifdef MEMMOVE void * memmove(void *dst0, const void *src0, size_t length) #else void bcopy(const void *src0, void *dst0, size_t length) #endif #endif { .....
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Antutu 3.x
• For people with source code
• Selection of toolchain and compiler options may cause huge difference, e.g., bit field
• Some version of x86 binary for Antutu 3.x was compiled with Intel, bit-by-bit operations turned in word-wide (32-bit) operations, and the speed up is about 70x faster
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Stream copy usually turned into memcpy()
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remote gdb1. get /system/bin/app_process and /system/bin/linker of the target system and necessary
shared libraries, e.g., /data/data/eu.chainfire.cfbench/lib/libCFBench.so
• adb pull /system/bin/app_process!
• adb pull /system/bin/linker lib/armeabi-v7a/!
• adb pull /data/data/eu.chainfire.cfbench/lib/libCFBench.so lib/armeabi-v7a/!
2. arm-linux-gnueabi-gdb ./app_process
3. on the target device, attach gdbserver to the running process you wanna debug
• ./gdbserver --attach :5039 3484
4. set shared library search path
• (gdb) set solib-search-path /Users/freedom/tmp/cfbench/lib/armeabi-v7a
5. ‘adb forward tcp:5039 tcp:5039’ and set remote target
• (gdb) target remote :5039
6. you can set breakpoints, print backtrace, disassemble, etc.
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• (gdb) b Java_eu_chainfire_cfbench_BenchNative_benchMemReadAligned
• (gdb) disassemble
Dump of assembler code for function Java_eu_chainfire_cfbench_BenchNative_benchMemReadAligned: 0x74b65848 <+0>: stmdb sp!, {r4, r5, r6, r7, r8, r9, r10, lr} => 0x74b6584c <+4>: bl 0x74b654ac <loadLib> 0x74b65850 <+8>: mov.w r0, #1048576 ; 0x100000 0x74b65854 <+12>: blx 0x74b65358 0x74b65858 <+16>: movs r6, #0 0x74b6585a <+18>: movw r9, #9999 ; 0x270f 0x74b6585e <+22>: mov r8, r0 0x74b65860 <+24>: bl 0x74b6547c <getTickCount> 0x74b65864 <+28>: add.w r5, r8, #1048576 ; 0x100000 0x74b65868 <+32>: mov r10, r0 0x74b6586a <+34>: mov r3, r8 0x74b6586c <+36>: ldr.w r2, [r3], #4 0x74b65870 <+40>: cmp r3, r5 0x74b65872 <+42>: add r4, r2 0x74b65874 <+44>: bne.n 0x74b6586c <Java_eu_chainfire_cfbench_BenchNative_benchMemReadAligned+36> 0x74b65876 <+46>: bl 0x74b6547c <getTickCount> 0x74b6587a <+50>: adds r6, #1 0x74b6587c <+52>: rsb r7, r10, r0 0x74b65880 <+56>: cmp r7, r9
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Quadrant
• Written in Java
• CPU: Not really testing CPU
• Memory: profiling shows that memcpy() is heavily in used
• What can we do
• optimized JIT part of DVM
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What other possible ways?
• binary translation during
• installation time
• run time
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Wrap-up
• Popular CPU and Memory benchmarks on Android mostly don’t reflect real CPU performance
• We know CPU performance != System performance != user-perceived performance
• There is always room for improvement
80
So?
81
Recent progress
• EEMBC’s AndEBench 2.0 is under development (http://www.eembc.org/press/pressrelease/130128.html)
• Qualcomm asked BDTi to develop new benchmark (http://www.qualcomm.com/media/blog/2013/08/16/mobile-benchmarking-turning-corner-user-experience).
• Samsung with other vendors launched MobileBench Consortium last year
• Antutu is still growing
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Thanks!
廣告• MediaTek joined
linaro.org last month
• linaro.org is a NPO working on open source Linux/Android related stuff for ARM-based SoCs
• So MTK is getting more open recently
• And, it’s looking for open source engineers
• Talk to guys at MTK booth or me
• There are more non-open source jobs
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backup
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Some References to Understand Performance Benchmark
• Raj Jain, “The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling”, Wiley, 1991
• Quantitative Approach
• A good SPEC introduction article, http://mrob.com/pub/comp/benchmarks/spec.html
• Kaivalya M. Dixit, “Overview of the SPEC Benchmarks,” http://people.cs.uchicago.edu/~chliu/doc/benchmark/chapter9.pdf
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Basic system parameters
------------------------------------------------------------------------------
Host OS Description Mhz tlb cache mem scal
pages line par load
bytes
--------- ------------- ----------------------- ---- ----- ----- ------ ----
localhost Linux 3.4.5-g armv7l-linux-gnu 1696 7 64 4.4700 1
!Processor, Processes - times in microseconds - smaller is better
------------------------------------------------------------------------------
Host OS Mhz null null open slct sig sig fork exec sh
call I/O stat clos TCP inst hndl proc proc proc
--------- ------------- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ----
localhost Linux 3.4.5-g 1696 0.49 0.67 2.54 5.95 8.52 0.67 5.05 876. 1668 4654
!Basic integer operations - times in nanoseconds - smaller is better
-------------------------------------------------------------------
Host OS intgr intgr intgr intgr intgr
bit add mul div mod
--------- ------------- ------ ------ ------ ------ ------
localhost Linux 3.4.5-g 1.0700 0.1100 3.4000 90.5 14.8
!Basic float operations - times in nanoseconds - smaller is better
-----------------------------------------------------------------
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Context switching - times in microseconds - smaller is better
-------------------------------------------------------------------------
Host OS 2p/0K 2p/16K 2p/64K 8p/16K 8p/64K 16p/16K 16p/64K
ctxsw ctxsw ctxsw ctxsw ctxsw ctxsw ctxsw
--------- ------------- ------ ------ ------ ------ ------ ------- -------
localhost Linux 3.4.5-g 8.9700 4.9000 6.1400 12.3 7.68000 57.6
!*Local* Communication latencies in microseconds - smaller is better
---------------------------------------------------------------------
Host OS 2p/0K Pipe AF UDP RPC/ TCP RPC/ TCP
ctxsw UNIX UDP TCP conn
--------- ------------- ----- ----- ---- ----- ----- ----- ----- ----
localhost Linux 3.4.5-g 8.970 17.6 23.9 47.5 71.3 357.
!File & VM system latencies in microseconds - smaller is better
-------------------------------------------------------------------------------
Host OS 0K File 10K File Mmap Prot Page 100fd
Create Delete Create Delete Latency Fault Fault selct
--------- ------------- ------ ------ ------ ------ ------- ----- ------- -----
localhost Linux 3.4.5-g 700.0 1.259 2.55270 3.048
!*Local* Communication bandwidths in MB/s - bigger is better
-----------------------------------------------------------------------------
Host OS Pipe AF TCP File Mmap Bcopy Bcopy Mem Mem
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PARSEC content• Blackscholes This applica0on is an Intel RMS benchmark. It calculates the prices for a por|olio of European op0ons
analy0cally with the Black-‐Scholes par1al differen1al equa1on (PDE). There is no closed-‐form expression for the Black-‐Scholes equa0on and as such it must be computed numerically.
• Bodytrack This computer vision applica0on is an Intel RMS workload which tracks a human body with mul1ple cameras through an image sequence. This benchmark was included due to the increasing significance of computer vision algorithms in areas such as video surveillance, character anima0on and computer interfaces.
• Canneal This kernel was developed by Princeton University. It uses cache-‐aware simulated annealing (SA) to minimize the rou1ng cost of a chip design. Canneal uses fine-‐grained parallelism with a lock-‐free algorithm and a very aggressive synchroniza0on strategy that is based on data race recovery instead of avoidance.
• Dedup This kernel was developed by Princeton University. It compresses a data stream with a combina1on of global and local compression that is called 'deduplica1on'. The kernel uses a pipelined programming model to mimic real-‐world implementa0ons. The reason for the inclusion of this kernel is that deduplica0on has become a mainstream method for new-‐genera0on backup storage systems.
• Facesim This Intel RMS applica0on was originally developed by Stanford University. It computes a visually realis1c anima1on of the modeled face by simula1ng the underlying physics. The workload was included in the benchmark suite because an increasing number of anima0ons employ physical simula0on to create more realis0c effects.
• Ferret This applica0on is based on the Ferret toolkit which is used for content-‐based similarity search. It was developed by Princeton University. The reason for the inclusion in the benchmark suite is that it represents emerging next-‐genera0on search engines for non-‐text document data types. In the benchmark, we have configured the Ferret toolkit for image similarity search. Ferret is parallelized using the pipeline model.
89
PARSEC content• Fluidanimate This Intel RMS applica0on uses an extension of the Smoothed Par0cle Hydrodynamics (SPH) method to
simulate an incompressible fluid for interac1ve anima1on purposes. It was included in the PARSEC benchmark suite because of the increasing significance of physics simula0ons for anima0ons.
• Freqmine This applica0on employs an array-‐based version of the FP-‐growth (Frequent PaMern-‐growth) method for Frequent Itemset Mining (FIMI). It is an Intel RMS benchmark which was originally developed by Concordia University. Freqmine was included in the PARSEC benchmark suite because of the increasing use of data mining techniques.
• Raytrace The Intel RMS applica0on uses a version of the raytracing method that would typically be employed for real-‐0me anima0ons such as computer games. It is op0mized for speed rather than realism. The computa0onal complexity of the algorithm depends on the resolu0on of the output image and the scene.
• Streamcluster This RMS kernel was developed by Princeton University and solves the online clustering problem. Streamcluster was included in the PARSEC benchmark suite because of the importance of data mining algorithms and the prevalence of problems with streaming characteris0cs.
• Swap1ons The applica0on is an Intel RMS workload which uses the Heath-‐Jarrow-‐Morton (HJM) framework to price a porRolio of swap1ons. Swap0ons employs Monte Carlo (MC) simula0on to compute the prices.
• Vips This applica0on is based on the VASARI Image Processing System (VIPS) which was originally developed through several projects funded by European Union (EU) grants. The benchmark version is derived from a print on demand service that is offered at the Na0onal Gallery of London, which is also the current maintainer of the system. The benchmark includes fundamental image opera0ons such as an affine transforma0on and a convolu0on.
• X264
90