Description
This benchmark was produced by Jack Dongarra from the "LINPACK" package of linear algebra routines. It became the primary benchmark for scientific applications from the mid 1980's with a slant towards supercomputer performance.
The original version was produced in Fortran but a "C" version appeared later. The standard "C" version operates on 100x100 matrices in double precision with rolled/unrolled and single/double precision options. The pre-compiled versions are double precision, rolled, optimised and non-optimised.
These can be found in BenchNT.zip which also contains the source code, providing further explanatory comments.
DOS versions are available in DosTests.zip and those to run via OS/2 in OS2Tests.zip.
Then there is My Main Page for other PC benchmarks and results.
The benchmark has also been compiled with Microsoft 32 bit and 64 bit compilers that generate SSE and SSE2 instructions for floating point. The original 2006 64 bit version indicated poor performance on Core 2 Duo CPUs but this was corrected using a later compiler in 2009 - see
Vista64.htm. Compiled codes (2006 and 2009 versions) are in
Win64.zip with source code in NewSource.zip. See also Win64.htm.
Performance rating is in terms of Millions of Floating Point Operations Per Second (MFLOPS).
Linpack Reference - Jack Dongarra, Performance of Various Computers Using Standard Linear Algebra Software in a Fortran Environment from
Here - PDF file including numerous results for minicomputers, workstations, mainframes and supercomputers.
Results
The following is a sample of results. Performance tends to be proportional to CPU MHz for a
given type of processor but is also affected by cache size and speed. There can also be variations
probably depending on where the data happens to be stored in cache. Details of cache sizes and range
of CPU MHz can be found in CPUSpeed.htm.
Results include those from DOS and Windows compilations that produce very similar speed measurements. Some SSE2 and OS/2 results are included at the bottom of the table,
then sample results from a later Microsoft compiler, particularly to include an Intel Atom based tablet, using Windows 10.
Other results are for the same code ported to 32-Bit and 64-Bit Linux using the supplied GCC compiler (all free software) - see
linux benchmarks.htm
and download benchmark execution files, source code, compile and run instructions in
classic_benchmarks.tar.gz.
Using Windows the file downloaded wrongly as classic_benchmarks.tar.tar but was fine when renamed classic_benchmarks.tar.gz. Results are shown separately
below.
A 2013 version of GCC has the option to compile for AVX1, a more recent addition to the Intel instruction set. This can potentially double maximum SSE/SSE2 speeds. Results are included
below,
and certainly show much improved performance. The AVX version of the benchmark is included in
AVX_benchmarks.tar.gz.
Further details are in
Linux AVX benchmarks.htm.
Windows PC Normal Results
Double Precision 100x100 compiled at 32 bits
Opt No opt
CPU MHz MFLOPS MFLOPS
AMD 80386 40 0.53 0.36
80486 DX2 66 2.63 1.74
AMD 5X86 100 3.34 2.24
Pentium 75 7.56 4.04
Cyrix P150 120 10.08 8.75
Cyrix PP166 133 11.53 8.33
Pentium 100 12.07 5.40
IBM 6x86 150 12.87 8.29
Pentium 133 17.05 5.60
Pentium 166 19.89 6.86
Cyrix PR233 188 19.98 11.88
Pentium 200 22.80 8.10
AMD K6 200 22.84 11.39
Pentium MMX 200 23.53 8.75
AMD K62 500 45.79 26.44
Pentium II 300 47.74 18.25
Pentium Pro 200 48.50 10.72
Pentium III 450 61.52 26.51
Pentium II 450 61.56 26.47
Apple G3 700 63.30 28.58
AMD K63 450 65.20 28.55
Celeron A 300 79.65 19.24
Pentium III 600 84.18 35.81
Celeron A 450 119.59 28.84
Athlon 500 180.79 39.70
Atom 1600 183.01 89.19
Pentium IIIE 600 185.22 59.43
Duron 600 225.06 34.81
Pentium III 1000 316.67 55.52
Atom Z8300 1840 366.59 154.54
Athlon Tbird 1000 372.69 81.11
Duron 1000 374.05 57.88
PIII Tualatin 1200 380.08 128.79
Pentium 4 1700 382.00 131.59
Pentium 4 1900 533.93 107.17
Celeron M 1295 539.76 123.59
Athlon 4 1600 585.74 103.42
P4 Xeon 2200 599.24 123.69
Pentium 4E 3000 630.30 165.01
Ath4 Barton 1800 659.57 117.29
Turion 64 M 1900 697.32 123.69
Opteron 1991 753.08 131.89
Athlon XP 2080 764.03 136.05
Pentium M 1862 834.29 181.05
Pentium 4 3066 840.27 174.64
Athlon XP 2338 859.43 153.21
Athlon 64 2150 811.86 142.80
Athlon 64 2211 838.22 145.60
Core 2 Duo M 1830 997.68 111.41
Pentium 4 3678 1017.01 209.01
Core i5 2467M @@@@ 1064.70 315.46
Celeron C2 M 2000 1092.56 121.25
Core 2 Duo 1 CP 2400 1315.42 195.13
Phenom II 3000 1412.83 244.43
Core i7 930 **** 1764.75 428.00
Core i7 860 #### 2004.31 381.97
Core i7 3930K &&&& 2529.73 746.01
Core i7 4820K $$$1 2671.15 892.04
Core i7 4820K $$$2 2684.05 895.54
Core i7 3930K OC 3112.94 926.92
#### Rated as 2800 MHz but running at up
to 3460 MHz using Turbo Boost
**** Rated as 2800 MHz but running at up
to 3066 MHz using Turbo Boost
@@@@ Rated as 1600 MHz running at up
to 2300 MHz using Turbo Boost
&&&& Rated as 3200 MHz but running at up
to 3800 MHz OC OverClocked ~4720 MHz
$$$1 Rated as 3700 MHz but running at up
to 3900 MHz using Turbo Boost
$$$2 Performance not Balanced Power
Setting for 3900 MHz
M = Mobile CPU
To Start
Windows 32/64 Bit SSE2 Results
Double Precision 100x100 compiled at 32 and 64 bits
Opt
CPU MHz MFLOPS
Celeron M 32b 1295 499.90
Pentium 4 32b 1900 677.67
Turion 64M 32b 1900 835.82
Pentium 4E 32b 3000 912.78
Athlon 64 32b 2211 1013.68
Athlon 64 64b 2211 1043.56
Athlon 64 64b 2211 1090.52 ##
Core2 DuoM 32b 1830 1119.38
CeleronC2M 32b 2000 1221.49
Core 2 Duo 32b 2400 1479.78
Core 2 Duo 64b 2400 823.10
Core 2 Duo 64b 2400 1602.35 ##
Phenom II 64b 3000 850.45
Phenom II 32b 3000 1713.22
Phenom II 64b 3000 1905.19 ##
Core i7 4820K 32b $$$1 3388.59 ##
Core i7 4820K 32b $$$2 3405.54 ##
Core i7 4820K 64b $$$1 3526.84 ##
Core i7 4820K 64b $$$2 3556.70 ##
Core i7 3930K 64b &&&& 3927.74 ##
## 2009 compilation
&&&& i7-3930K Overclocked see above
$$$$ i7-4820K see above
Later MS Compilers Version 18.00
Opt
CPU MHz MFLOPS
Atom Z8300 32b 1840 615.80
Atom Z8300 64b 1840 638.75
Core2 DuoM 32b 1830 1114.58
Core 2 Duo 32b 2400 1484.14
Core 2 Duo 64b 2400 1573.34
Phenom II 32b 3000 1876.82
Phenom II 64b 3000 2033.44
Core i7 4820K 32b $$$1 3453.72
Core i7 4820K 64b $$$1 3603.86
To Start
Linux 32/64 Bit Results
Double Precision 100x100 compiled at 32 and 64 bits
Opt No opt
CPU MHz MFLOPS MFLOPS
Atom N455 32b Ub 1666 196 94
Atom N455 64b Ub 1666 226 89
Core 2 Mob 32b Ub 1830 983 307
Athlon 64 32b Ub 2211 936 231
Athlon 64 64b Ub 2211 1118 221
Core 2 Duo 32b Ub 2400 1288 404
Core 2 Duo 64b Ub 2400 1577 378
Phenom II 32b Ub 3000 1464 411
Phenom II 64b Ub 3000 1887 411
Phenom II 64b Fe 3000 1872 407
Core i7 930 64b Ub **** 2265 511
Core i7 4820K 32b Ub $$$1 2534 988
Core i7 4820K 64b Ub $$$1 3672 900
Core i7 4820K AVX Ub $$$12 5413 935
Ub = Ubuntu Linux, Fe = Fedora Linux
**** Rated as 2800 MHz but running at up to
3066 MHz using Turbo Boost
$$$1 Rated as 3700 MHz but running at up to
3900 MHz, using Turbo Boost
$$$12 As $$$1, but compiled with GCC 4.8.2 that
produces AVX SIMD insructions.
To Start
OS/2 Results
Opt No opt
CPU MHz MFLOPS MFLOPS
IBM 80486BL 100 0.56 0.50
80486 DX2 66 2.65 2.00
80486 75 2.84 1.83
Cyrix P150 120 10.08 8.75
Pentium Pro 150 39.33 14.30
Pentium Pro 166 43.96 15.93
Pentium Pro 200 46.69 18.71
To Start
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Android and Raspberry Pi Versions
Later conversions were varieties to run on Android tablets and phones on ARM CPUs. Most use a Java front end for starting and displaying results, with the compiled C code for calculations.
Download:
Linpackv5.apk
Linpackv7.apk
LinpackSP.apk
LinpackDP2.apk
LinpackSP2.apk
LinpackJava.apk.
and install in the usual way for such devices.
The v7 program, and single precision (SP) variety are compiled for newer hardware than the v5 version. Then there is a benchmark with all Java code. See -
android benchmarks.htm,
Android Native ARM-Intel Benchmarks.htm
and
Android 64 Bit Benchmarks.htm
The former includes LinpackJava.apk results from PCs via Android x86,
details are here.
Latest are modified to use NEON SIMD functions that that carry out four arithmetic operations simultaneously.
Download:
www.roylongbottom.org.uk/NEON-Linpack.apk
May 2015 - It was found that the NEON SIMD benchmark could be compiled to produce Intel SSE instructions, for the
Android Native ARM-Intel Benchmarks.
The benchmark can be downloaded the following. It automatically selects the target CPU at 32 bits or 64 bits from ARM, Intel or MIPS.
www.roylongbottom.org.uk/NEON-Linpacki.apk
Latest benchmarks were compiled and run on a Raspberry Pi that uses ARM CPUs and Linux. See
Raspberry Pi Benchmarks.htm
and download from
Raspberry_Pi_Benchmarks.zip.
After this, the benchmarks were recompiled to use native code on Intel processor based Android devices.
Then updated, inculding Raspberry Pi 2 and 3, exising benchmarks and new version from a later compiler.
Benchmarks and source codes for 64 bit Linux are in
Rpi3-64-Bit-Benchmarks.tar.gz.
Double Precision and Single Precision (SP) 100x100
v7/v5 DP v5 DP
CPU MHz Android MFLOPS MFLOPS
ARM 926EJ 800 2.2 5.7 5.6
ARM v7-A8 800 2.3.5 80.2
ARM v7-A9 800 2.3.4 101.4 10.6
ARM v7-A5 1500 C1 4.4.2 121.5
ARM v7-A9 1300a 4.1.2 151.1 17.1
ARM v7-A9 1500 4.0.3 171.4
ARM v7-A9 1500a 4.0.3 155.5 16.9
Atom Z3745C 1866 4.4.2 168.2 59.4
ARM v7-A9 1400 4.0.4 184.4 19.9
ARM v7-A9 1600 4.0.3 196.5
QUAL-S4 1500 4.0.3 254.9
Atom Z3745D 1866 4.4.2 282.3
Atom Z3745I 1866 4.4.2 362.6
QUAL-800 2150 4.4.3 389.5 35.4
ARM v7-A15 2000b 4.2.2 459.2 28.8
QUAL-800I 2150 4.4.3 629.9
ARM v7-A15I 2000b 4.2.2 826.4
ARM v8-A53 1300 5.0.2 21.4
v7 SP Java
CPU MHz Android MFLOPS MFLOPS
ARM 926EJ 800 2.2 9.6 2.3
ARM v7-A9 800 2.3.4 129.1 33.3
ARM v7-A9 1300a 4.1.2 201.3 56.4
ARM v7-A9 1300a 5.0.2 200.0 90.9
ARM v7-A9 1500a 4.0.3 204.6 56.9
ARM v7-A9 1400 4.0.4 235.5 57.0
Atom Z3745C 1866 4.4.2 296.3 252.5
Atom Z3745I 1866 4.4.2 408.9
QUAL-800 2150 4.4.3 752.0 340.4
QUAL-800I 2150 4.4.3 790.8
ARM v7-A15 2000b 4.2.2 803.0 143.1
ARM v7-A15I 2000b 4.2.2 952.9
ARM v8-A53 1300 5.0.2 86.1
ARM v8-A53 1300 5.1 90.9
ARM v8-A53 1500 6.0.1 28.3
Atom Ax86 1666 2.2.1 15.7
Core 2 Ax86 2400 2.2.1 53.3
DP SP
CPU MHz Android MFLOPS MFLOPS
ARM v8-A53 1300 5.0.2 156.7 184.1
ARM v8-A53I 1300 5.0.2 172.3 180.6
ARM v8-A53I 1300 5.1 178.0 187.0
ARM v8-A53I 1500 6.0.1 207.6 218.0
Atom Z8300& 1840 6.0.1 632.6 682.1
Core i7& 3900 6.0.1 3442.0 1839.0
64 Bit Version
ARM v8-A53I 1300 5.0.2 340.2 482.4
ARM v8-A53I 1300 5.1 347.6 492.8
Qual-810I 2000 5.0.2 1277.8
Atom Z8300& 1840 6.0.1 875.8 1473.2
Core i7& 3900 6.0.1 5152.9 3950.3
NEON SP
CPU MHz Android MFLOPS
ARM v7-A9 800 2.3.4 255.8
ARM v7-A9 1300a 4.1.2 376.0
ARM v7-A9 1500a 4.0.3 382.5
ARM v8-A53I 1300 5.0.2 407.1
ARM v8-A53I 1300 5.1 421.9
Atom Z3745 1866 4.4.2 443.4
ARM v7-A9 1400 4.0.4 454.2
Atom Z3745I 1866 4.4.2 900.2 SSE
Atom Z8300& 1840 6.0.1 1000.0
QUAL-800 2150 4.4.3 1250.1
QUAL-800I 2150 4.4.3 1325.0
ARM v7-A15 2000b 4.2.2 1334.9
ARM v7-A15I 2000b 4.2.2 1411.9
Qual-810 2000 5.0.2 1446.4
Core i7& 3900 6.0.1 3717.4
64 Bit Version
ARM v8-A53I 1300 5.0.2 505.2
ARM v8-A53I 1300 5.1 520.8
Raspberry Pi DP SP
CPU MHz Linux MFLOPS MFLOPS
ARM 1176 700 3.6.11 42 58
ARM 1176 1000 3.6.11 68 88
Raspberry Pi 2
ARM V7A 900 3.18.5 120 156
ARM V7A 1000 3.18.5 134 175
gcc 4.8
ARM V7A 900 3.18.5 154 156
ARM V7A 1000 3.18.5 169 176
Raspberry Pi 3
ARM v8-A53 1200 4.1.19 176 190
gcc 4.8
ARM v8-A53 1200 4.1.19 180 194
Raspberry Pi 3, 64 Bit
OpenSuse
ARM v8-A53 1200 4.4.36 348 494
Gentoo
ARM v8-A53 1200 4.10.0 343 482
Raspberry Pi 2 & 3 gcc 4.8 NEON SP
CPU MHz Android MFLOPS
ARM V7A 900 3.18.5 300
ARM v8-A53 1200 4.1.19 486
Raspberry Pi 3, 64 Bit
OpenSuse
ARM v8-A53 1200 4.4.36 530
Gentoo
ARM v8-A53 1200 4.10.0 521
v7 fast FPU used when available
Original GCC 4.6
I Later GCC 4.8
Native NEON version GCC 4.8
CPU running at a 1200, b 1700
Ax86 Android x86 - Slow JIT Compiler?
Atom Z3745I Native Intel/ARM version
ARM v7-A15I also Native NEON version
Atom Z3745C using Intel to ARM conversion
Atom Z3745D as C but new compiler gcc 4.8
QUAL = Qualcomm CPU
C1 = ODROID-C1 board
Atom Z8300& Core i7& Androd via REMIX for PC
NEON Core i7& original Intel to ARM conversion
To Start
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MultiThreading Versions
This version uses mainly the same C programming code as the single precision floating point NEON compilation above. It is run run on 100x100, 500x500 and 1000x1000 matrices using 0, 1, 2 and 4 separate threads.
Virtually the same programming code was used to produce execution files for ARM devices/Android, PCs/Linux and PCs/Windows.
The latter two were compiled to use SSE instructions, that can carry out up to four operations simultaneously. Again see
android neon benchmarks.htm .
Download:
www.roylongbottom.org.uk/NEON-Linpack-MP.apk
A new version, that automatically selects the target CPU at 32 bits or 64 bits from ARM, Intel or MIPS, can be downloaded from:
www.roylongbottom.org.uk/NEON-Linpacki-MP.apk
Slight changes were made to the original version to allow a higher level of parallelism. The initial 100x100 Linpack benchmark was only of use for measuring performance of single processor systems. The one for shared memory multiple processor systems is a 1000x1000 variety. The programming code for this is the same as 100x100, except users are allowed to use their own linear equation solver.
This program can run much slower using multiple threads due to the overhead of creating and closing threads too frequently. Using data size N x N, approximately 0.67 x N x N x N floating point calculations are carried out but, with this program, there are N breaks for handling threads (can someone do better?). Results show that overheads from these are generally constant executing up to four threads. Results below include overheads of using a single thread at 100x100 (microseconds difference from no threads / 100).
In most cases, overheads (per N) increase with larger arrays, as performance becomes more dependent on higher level caches or RAM data transfers.
Results below show no performance gains due to multiprocessing on the ARM based devices. Constant performance of the Netbook, with no threads, suggests a CPU speed limitation, then gains with two threads due to Hyperthreading. The Core 2 Duo shows the best improvement using two threads. At least the Phenom shows some gain with four threads, using Linux but not via Windows.
Particularly with multithreading, it is important to verify that calculations produce the same numeric results, irrespective of the number of threads used. This benchmark checks for that and reports if there are errors. These results are shown below and are the same on Android, Linux and Windows. For completion, numeric results and a sample of performance are provided for a double precision compilation.
At some point in time, a Java version of the Linpack benchmark rearranged the order of initial data (function matgen) and this changed the numeric results. These are shown below.
Single Precision MFLOPS
100x100, 500x500, 1000x1000, 0, 1, 2, 4 Threads
A1 Quad Core 1.86 GHz Intel Atom Z3745, Android 4.4
Original
Threads None 1 2 4
N 100 452.39 21.00 23.48 17.48
N 500 663.38 275.56 88.66 312.71
N 1000 617.04 380.60 191.26 195.61
A1 ARM-Intel
N 100 971.71 37.72 36.36 39.66
N 500 1311.37 488.73 487.85 488.98
N 1000 945.97 727.85 737.95 742.34
T21 Qualcomm Snapdragon 800 2150 MHz, Android 4.4.4
Original
Threads None 1 2 4
N 100 1311.08 12.38 12.93 15.05
N 500 2271.56 344.04 419.52 381.73
N 1000 837.30 540.99 523.52 564.87
T21 ARM-Intel
N 100 1308.07 14.89 11.77 11.63
N 500 2341.17 407.96 481.02 415.12
N 1000 901.21 551.80 566.77 564.31
T22, Quad Core ARM Cortex-A53 1300 MHz, Android 5.0.2
32 bit
Threads None 1 2 4
N 100 460.74 22.35 23.16 23.82
N 500 480.63 336.52 339.94 303.66
N 1000 470.02 405.86 403.01 405.98
T22 64 bit
N 100 548.67 27.70 33.93 37.00
N 500 470.04 285.95 297.79 301.67
N 1000 519.02 441.84 443.47 441.91
T22, Quad Core ARM Cortex-A53 1300 MHz, Android 5.1
32 bit
Threads None 1 2 4
N 100 478.29 22.91 26.14 24.45
N 500 526.25 349.09 343.33 350.01
N 1000 488.62 420.83 416.43 415.80
T22 64 bit
N 100 573.90 34.43 26.00 41.28
N 500 607.89 389.67 353.51 322.91
N 1000 541.80 449.28 461.96 461.27
T11 Samsung EXYNOS 5250 2.0 GHz Cortex-A15, Android 4.2.2
Threads None 1 2 4
N 100 1399.82 54.86 55.31 54.66
N 500 1154.21 434.16 434.06 436.97
N 1000 571.26 482.57 487.25 485.80
N 100 1 Thread overheads 120 microseconds
MHz measured at 1700
A1 ARM-Intel
N 100 1497.90 61.13 63.13 61.87
N 500 1399.10 491.49 489.29 494.69
N 1000 586.14 499.00 504.97 497.49
P11 Galaxy SIII, Quad Cortex-A9 1.4 GHz, Android 4.0.4
Threads None 1 2 4
N 100 455.90 42.37 41.76 37.32
N 500 395.16 326.43 321.82 309.55
N 1000 355.77 322.98 323.71 322.24
N 100 1 Thread overheads 147 microseconds
T7 Nexus 7 Quad 1300 MHz Cortex-A9, Android 4.1.2
Threads None 1 2 4
N 100 413.47 45.95 48.22 48.34
N 500 253.08 187.51 189.69 189.94
N 1000 148.76 135.49 136.08 136.17
N 100 1 Thread overheads 133 microseconds
MHz measured at 1200
Netbook 1.6 GHz Atom, 64 Bit Linux
Threads None 1 2 4
N 100 263.60 48.45 55.02 35.99
N 500 258.06 228.34 293.27 248.43
N 1000 252.72 259.02 362.24 213.40
N 100 1 Thread overheads 116 microseconds
Desktop 2.4 Ghz Core 2 Duo, 64 Bit Linux
Threads None 1 2 4
N 100 1666.02 287.94 200.82 134.17
N 500 1908.89 1422.59 1902.42 1507.04
N 1000 1921.33 1624.31 2606.09 2306.14
N 100 1 Thread overheads 20 microseconds
DeskTop 3.0 GHz Quad Core Phenom II, 64 Bit Linux
Threads None 1 2 4
N 100 1924.69 279.90 206.19 141.13
N 500 2059.73 1333.07 1510.81 1247.76
N 1000 2074.59 1682.34 2314.57 2478.78
N 100 1 Thread overheads 21 microseconds
DeskTop 3.0 GHz Quad Core Phenom II, 64 Bit Windows 7
Threads None 1 2 4
N 100 1961.90 103.26 56.87 32.03
N 500 1596.83 894.22 767.35 537.39
N 1000 1646.96 1261.64 1588.28 1337.33
N 100 1 Thread overheads 63 microseconds
Single Precision Numeric Results
NR=norm resid RE=resid MA=machep X0=x[0]-1 XN=x[n-1]-1
N 100 500 1000
NR 1.60 3.96 11.32
RE 3.80277634e-05 4.72068787e-04 2.70068645e-03
MA 1.19209290e-07 1.19209290e-07 1.19209290e-07
X0 -1.38282776e-05 5.26905060e-05 1.62243843e-04
XN -7.51018524e-06 3.26633453e-05 -6.65783882e-05
Double Precision MFLOPS
Desktop 2.4 Ghz Core 2 Duo, 64 Bit Linux
Threads None 1 2 4
N 100 1509.25 271.92 195.22 130.55
N 500 1697.29 1295.56 1753.95 1374.19
N 1000 980.78 892.38 1036.67 945.15
N 100 1 Thread overheads 21 microseconds
Double Precision Numeric Results
N 100 500 1000
NR 1.67 5.76 9.50
RE 7.41628980e-14 1.27986510e-12 4.22017976e-12
MA 2.22044605e-16 2.22044605e-16 2.22044605e-16
X0 -1.49880108e-14 5.59552404e-14 1.09912079e-13
XN -1.89848137e-14 3.39728246e-14 5.08926234e-13
Revised Matgen Results Single Precision
N 100 500 1000
NR 1.76 5.32 11.48
RE 4.18424606e-05 6.34193420e-04 2.73901224e-03
MA 1.19209290e-07 1.19209290e-07 1.19209290e-07
X0 -1.37090683e-06 3.00407410e-05 -4.81605530e-05
XN -1.13248825e-06 -2.25305557e-05 8.94069672e-06
Revised Matgen Results Double Precision
N 100 500 1000
NR 1.43 5.17 10.10
RE 6.33937347e-14 1.14730447e-12 4.48374671e-12
MA 2.22044605e-16 2.22044605e-16 2.22044605e-16
X0 -2.55351296e-15 -3.64153152e-14 -1.59761093e-13
XN 6.21724894e-15 6.72795153e-14 -4.12780921e-13
To Start
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Roy Longbottom February 2017
The Internet Home for my PC Benchmarks is via the link
Roy Longbottom's PC Benchmark Collection
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