Posted 19 November 2012 - 06:01 PM
Nexus 7 32gb:
CPU Integer: 3507
CPU Float-point: 2525
2D Graphics: 296
3D Graphics: 1233
Database IO: 535
SD Card Write: (11.7MB/s) 117
SD Card Read: (31.1 MB/s) 194
CPU Frequency: 1300Mhz
LG Optimus G
CPU Integer: 4764
CPU Float-point: 3548
2D Graphics: 294
3D Graphics: 1249
Database IO: 555
SD Card Write: (29.3MB/s) 150
SD Card Read: (>50 MB/s) 197
CPU Frequency: 1512Mhz
I know the Optimus should run at 1.5Ghz, not sure why it's clocked down there.
Edit: Clocked down due to heat apparently, I amended the score with it at the proper clock rate.
Posted 10 March 2013 - 05:33 AM
Some time ago I posted a reply to this (twice) but it did not appear. This might be because I include links to my numerous benchmarks and results. Following is the text, without the links. All can be found on Googling (like just roylongbottom or with a name like Whetstone).
You might like my FREE, too many for non-nerds, benchmarks covering Windows, Linux and Android. For Android here are 14 single core benchmarks and 8 using multithreading. See following for details, downloads and results: Results are in MFLOPS, MIPS and MB/second, not meaningless numbers. Most use Native Code with a couple Java versions for comparison. Some are supplied in different flavours - Linpack via Java, native DP, native SP and native old HW.
Links to Android benchmarks
First set are the Classic Benchmarks that were the first programs that set standards of performance for computers. They are (original standard) Whetstone (Floating point benchmark for minicomputers), Dhrystone (Integer benchmark for UNIX systems), Livermore Loops (Numeric benchmark for supercomputers) and Linpack (Floating point benchmark for workstations). Then there are some that measure bus, cache and memory speeds.
These and others are available to run on PCs under Windows and Linux from:
Links to Linux benchmarks
A summary of all Classic Benchmark results are in:
Links to benchmark results (including cross platform)
and for old minicomputers and mainframes (Whetstone Benchmark History and Results
Posted 12 March 2013 - 06:59 AM
Cross Platform Benchmarks
The first cross platform program worthy of consideration is the Whetstone Benchmark. This appears to have been used by ARM in the 1990’s and later.
The original, written in Fortran, was the first general purpose benchmark that set industry standards of computer performance. It was released in 1972, based on research by Brian Wichmann, and produced by Harold Curnow. Later updates became my responsibility. The three of us were UK Government employees. Speed was measured in terms of Million Whetstone Instructions Per Second (MWIPS). Later, in order to identify compiler over-optimisation, I introduced speeds of individual tests, shown as MOPS or MFLOPS - Millions of Operations or Floating Point Operations Per Second. My results for all sorts of programming languages, mainly via Windows, Linux and Android are in the following.
Then Whetstone History and Results (mainframes, minicomputers, supercomputers 1970s to 1990s in:
I have produced two versions for Android. The first is an all Java variety (Java Whetstone.apk) and the second one has a Java front end with the benchmark using compiled C?C++ code (NativeWhetstone.apk). These can be downloaded from and need a Settings, Security optin to allow installation of non-Market applications.
Following are example results where #A is a Nexus 7, #B a Galaxy SIII (new version A9), QU-S4 Qualcomm Snapdragon S4 (some performance differences to ARM) with Atom and Core 2 PCs via Linux. Harmonic mean results are shown for MFLOPS, COS/EXP MOPS and Integer tests. Note: some CPUs do not appear to run at the specified MHz and other need a Setting to force maximum speed.
Func INT MWIPS
CPU MHz MWIPS MFLOPS MOPS MOPS per MHz
926EJ 800 14 3 0.3 5 0.02
v7-A8 1000 138 22 3.3 51 0.14
v7-A9 800 224 44 6.8 45 0.28
v7-A9 #A 1300 348 73 8.4 81 0.27
v7-A9 #B 1400 400 84 9.8 93 0.29
QU-S4 1500 487 81 13.5 127 0.32
Atom 1666 621 248 12.0 183 0.37
Core2 2400 1925 635 34.8 739 0.80
926EJ 800 31 11 0.4 88 0.04
v7-A8 1000 288 89 4.4 374 0.29
v7-A9 800 687 156 11.7 814 0.86
v7-A9 #A 1300 1115 259 18.6 1345 0.86
v7-A9 #B 1400 1334 301 24.9 1569 0.95
QU-S4 1500 1040 288 16.0 1590 0.69
Atom 1666 769 313 10.1 1080 0.46
Core 2 2400 2560 753 40.3 2181 1.07
At least on a per MHz basis, the latest ARM CPUs can be as fast as Intel processors.
Posted 12 March 2013 - 07:17 AM
I have a Snapdragon S4 in my phone, and I can confirm it's a powerhouse.
I find your last statement a bit misleading though, as you only go up to a Core2 processor and don't list what architecture it is. Conroe<Kentsfield<Yorkfield for example. You'll also find that clock for clock, the Sandy Bridge and Ivy Bridge pretty well blow Core2 out of the water, and Haswell coming up is expected to be a pretty nice increase as well.
Posted 12 March 2013 - 08:47 AM
> as you only go up to a Core2 processor and don't list what architecture it is. Conroe<Kentsfield<Yorkfield for example. You'll also find that clock for clock, the Sandy Bridge and Ivy Bridge pretty well blow Core2 out of the water<
The Whetstone benchmark does not depend on L2/L3 cache size and performance appears to be consistent across Core 2 and i7, at least on the Windows version shown in the following results. Other benchmarks vary according to CPUID. In this case Sandy Bridge seems to have sunk a bit. These results are from CPUs running at the Turbo Boost frequency.
Func INT MWIPS
CPU MHz MWIPS MFLOPS MOPS MOPS per MHz CPUID
Core 2 Duo 2400 2057 499 42.8 685 0.86 10676
Core i7 930 3066 2496 576 54.5 830 0.81 106A5
Core i7 860 3466 2790 653 61.5 910 0.80 106E5
Core i7 3930K 3800 3004 707 64.9 1122 0.79 206D7
Posted 12 March 2013 - 09:03 AM
Well that's rather interesting. Do you have any info on Ivy Bridge stuff? I imagine the trigate transistors and die shrink would make a difference.
Posted 12 March 2013 - 10:23 AM
I don’t have any Ivy Bridge results yet, only up to Sandy Bridge. For the latter see the following overnerdified results with %MIPS/MHz and %MFLOPS/MHz for CPU/L1. L2, L3 and RAM data for most of my single CPU benchmarks. The Core i5 is Sandy Bridge, not much different on these ratios.
At the start are CPUIDs and other things. Note differences on cache sizes, Bus GT/s and Mem GB/s, including some Ivy Bridge. Also note Celerons and Zeons with same CPUID as Core systems.
Posted 12 March 2013 - 12:48 PM
The second cross platform test is the Dhrystone Benchmark. This is a C integer only program and has been around since 1984. Speed was originally measured in Dhrystones per second. This was later changed to VAX MIPS by dividing Dhrystones per second by 1757, the DEC VAX 11/780 result, the latter being regarded as the first 1 MIPS minicomputer. This is perhaps ARM’s favourite benchmark, where they quote 2.5 MIPS/MHz for Cortex-A9. This is much better than with my benchmark but manufacturer’s compilers are inevitably tuned to over-optimise.
My benchmarks are Dhrystone2.apk and Dhry2Nopt.apk, downloadable from
Result on PCs can be found in:
The original Dhrystone 1 and 2 results are in
The latter includes 1996 ARM Risc CPU result, included in the following table. Results for PCs are also given, to show variation of using different compilers.
Dhrystone 2 Optimised
PC Windows MHz VAX MIPS Android Device MHz VAX MIPS
/Linux MIPS /MHz MIPS /MHz
Atom 1666 1828 1.1 926EJ Tablet 800 356 0.45
Core i5 2467M 2300 4752 2.1 v7-A9 V0x1 Tablet ## 800 962 1.20
Core 2 2400 6446 2.7 v7-A9 V0x2 Galaxy SII 1200 1491 1.24
Core i7 930 3066 8684 2.8 v7-A9 V0x3 Tablet ## 1500 1650 1.10
Core i7 860 3460 9978 2.9 v7-A9 V 0x2 Nexus 7 1300 1610 1.24
Core i7 3930K 3800 11197 2.9 v7-A9 V 0x3 Galaxy SIII 1400 1937 1.38
Core 2 32 bit 2400 8094 3.4 Old 1996 Acorn Risc
Core 2 64 bit 2400 12600 5.3
Linux ARM SA110 Acorn PC 200 204 1.02
Atom 32 bit 1666 2055 1.2 ARM SA110 Acorn PC 30 18.4 0.61
Atom 64 bit 1666 2704 1.6 ARM 3 Acorn 5000 33 13.8 0.42
Core 2 32 bit 2400 5852 2.4
Core 2 64 bit 2400 12265 5.1 ## not running at specified MHz
WANTED ANDROID RESULTS FOR OTHER CPUs
Posted 13 March 2013 - 06:41 AM
The Linpack Benchmark was produced from the "LINPACK" package of linear algebra routines. It became the primary benchmark for scientific applications, particularly under Unix, from the mid 1980's, with a slant towards supercomputer performance. The original double precision C version, used here, operates on 100x100 matrices. Performance is governed by an inner loop in function daxpy() with a linked triad dy[i] = dy[i] + da * dx[i], and is measured in Millions of Floating Point Operations Per Second (MFLOPS).
This benchmark, from GreenComputing” seems to be the most popular one for Android from Google Play. However, it is based on Java calculations and is extremely slow compared with other systems. Results for the original N=100 and others for MP systems can be found in:
and my results on PCs via Windows and Linux, plus Android devices are in:
I have five versions available that use one CPU core and are downloadable via:
LinpackJava.apk - all Java
Linpackv5.apk - compiled native code for old floating point instructions
Linpackv7.apk - compiled native code using later vfpv3 instructions
LinpackSP.apk - as v7 but using faster single precision floating point
NEON-Linpack.apk - using NEON SIMD vector instructions (single precision)
Example results are below. The benchmark uses L2 cache sized data, where the HTC One X with a Qualcomm Snapdragon S4 is faster than the ARM-A9 CPUs. Unlike Intel, single precision calculations are faster than double precision on the ARM CPUs but the main line Core processors are far superior.
Device ARM MHz V7 SP NEON Java V7 SIMD
Tablet 926EJ 800 6 10 N/A 2 0.01
Huawei u8800 v7-A8 800 80 0.10
Tablet v7-A9 800 101 129 256 33 0.13 0.32
HTC One X v7-A9 1500 171 0.11
Tablet v7-A9 1500 156 205 382 57 0.10 0.25
Asus TF700 v7-A9 1600 196 0.12
Nexus 7 v7-A9 1300 151 201 376 56 0.12 0.29
HTC One X QU-S4 1500 255 0.17
Galaxy SIII v7-A9 1400 184 236 454 57 0.13 0.32
Atom 1666 204 216 118 0.12
Core 2 2400 1288 901 0.54
Atom 1666 183 119 0.11
Core 2 2400 1315 551 0.55
Core i7 930 Turbo 3066 1765 0.58
Core i7 860 Turbo 3460 2004 0.58
Core i7 3930K Turbo 3800 2530 0.67
Core 2 64 bit 2400 1602 0.67
Core i7 3930K O'Clkd 4720 3928 0.83
Posted 13 March 2013 - 11:09 AM
How much faster is your phone than the $7M Cray 1 Supercomputer?
This original main benchmark for supercomputers was first introduced in 1970, initially comprising 14 kernels of numerical application, written in Fortran. This was increased to 24 kernels in the 1980s. Performance measurements are in terms of Millions of Floating Point Operations Per Second or MFLOPS. The kernels are executed three times with different double precision data array sizes. Following are overall MFLOPS results for various systems, Geometric Mean being the official average performance. [Reference - F.H. McMahon, The Livermore Fortran Kernels: A Computer Test Of The Numerical Performance Range, Lawrence Livermore National Laboratory, Livermore, California, UCRL-53745, December 1986]
---------------- MFLOPS ---------------
CPU MHz Maximum Average Geomean Harmean Minimum Date
CDC 7600 36.4 7.3 4.2 3.9 2.5 1.4 1974 *
Cray 1A 80 83.5 25.8 14.4 7.9 2.7 1980 *
Cray 1S 80 82.1 22.2 11.9 6.5 1.0 1985
CDC Cyber 205 50 146.9 36.4 14.6 5.0 0.6 1982 *
Cray 2 244 146.4 36.7 14.2 5.8 1.7 1985
Cray XMP1 105 187.8 61.3 31.5 15.6 3.6 1986
* Fewer than 24 Kernels, Date is year measured
The Android benchmark is LivermoreLoops.apk, available from:
Android results below show Geometric Mean speed up to 15 times Cray 1, but not as effective as desktop PCs. These kernels use L1 and L2 caches, penalising the Snapdragon with the smaller L1 cache (indicated on memory benchmarks).
------------- MFLOPS ------------
Device CPU MHz Max Average Geomean Harmean Min
Tablet 926EJ 800 10 6 5 5 2
Galaxy S v7-A8 1000 61 37 35 32 13
Tablet v7-A9 800 253 129 115 102 47
Nexus 7 v7-A9 1300 392 202 181 161 68
Tablet v7-A9 1500 397 208 186 165 75
HTC One S QU-S4 1500 418 228 194 161 65
Galaxy SIII v7-A9 1400 456 247 221 196 85
Netbook Atom 1666 465 212 185 157 50
Desktop Core 2 2400 2385 1038 806 582 161
Desktop i7 930 3066 4336 1574 1317 1105 410
QU-S4 = Qualcomm Snapdragon S4
Posted 01 April 2013 - 05:18 AM
The most noticeable performance differences between the various implementations of ARM processor cores are revealed via my memory benchmarks. The three of them all measure MegaBytes per second transferring from caches and RAM, with data sizes 16. 32. 64. 126. 256. and 512 KB, then 1, 4, 16 and 64 MB.
MemSpeed - Calculations are x[m]=x[m]+s*y[m] and x[m]=x[m]+y[m], using double and single precision floating point and x[m]=x[m]+s+y[m] and x[m]=x[m]+y[m] with integers.
BusSpeed - This benchmark reads data using AND instructions and is designed to identify transferring data in bursts over buses. The program starts by reading a word (4 bytes) with an address increment of 32 words (128 bytes) before reading another word. The increment is reduced by half on successive tests, until all data is read. Different burst sizes can apply to transfers from RAM and burst might or might not apply via caches.
RandMem - carries out four tests with serial and random read and read/write tests, using 32 bit integers. Serial and random address selections use the same complex indexing structure. Random access speeds are influenced by burst reading.
Further details, full results and download links are provided in:
As with other benchmarks, comparison of these ones can show that CPUs don’t always run at the specified MHz. They also differences between earlier and later Cortex-A9 processors and those using Qualcomm Snapdragon S4s.
The Nexus 7 and Galaxy SIII both have quad core Cortex-A9 CPUs, the SIII having a later version. There are claims that this has 128-bit internal buses, instead of 64-bit and is probably why speeds via L2 cache are up to 25% faster. The SIII also have dual channel RAM with serial data transfer speeds three times faster.
Comparing a HTC One X Snapdragon S4 with the SIII identifies significant differences. The former has a smaller L1 cache then, adjusting for a constant MHz, is slower than the Cortex-A9 on integer calculations and much slower on all sequential data transfers form RAM (not dual channel?). On the other hand, the S4 is up to twice as fast on floating point calculations with data from caches and can be faster on random/skipped sequential data transfers from RAM, due to different burst reading characteristics. Back to disadvantages, the S4 can suffer from severe degradation on reading random/skipped sequential data from caches due to apparent burst reading.
We could say that these benchmarks show that we can’t simply say that one phone or tablet is faster than another with a variation of technology. In this case, a SIII can be between half speed to 2.5 times faster than the S4.
Also tagged with one or more of these keywords: android, benchmark, list, google, smart, phone, test
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