Does super pi 1.4 mod really faster than super pi 1.5 mod?

After i heard many of my friends said

"xxoooxoxoxoxo 1.5mod is slower than 1.4mod"
"???blahanablah 1.4mod is faster than 1.5mod"
"banana and banana 1.4mod and 1.5mod is the same"

i gave a try today

FX60 ES handpick
G.Skill 2GBHZ handpick
DFi SLi-DR cap mod handpick bios 510-1
EVGA 7800GTX KO SLi stock
PC P&C 1KW
Tower 120
windows xp SP1

both super pi ran at realtime

1st test
super pi 1.5mod with core 0 started 1st
super pi 1.4mod with core 1 started 2nd

http://premium1.uploadit.org/guess20...compare001.JPG

http://premium1.uploadit.org/guess20...compare002.JPG

2nd test
super pi 1.5mod with core 1 started 2nd
super pi 1.4mod with core 0 started 1st

http://premium1.uploadit.org/guess20...compare003.JPG

http://premium1.uploadit.org/guess20...compare004.JPG

sry the 2nd A64 i forgot to press the refresh


well.... from this... i think maybe... maybe the 1.5mod is a little bit slower than 1.4mod about 2~3sec.
i am not sure why it made difference.does the code is a bit slower. maybe the time refresh is faster than 1.4mod. as we can see, the repeating decimal is not appear too frequenly. that is why i think 1.5mod is slower than 1.4mod

that's what me and eva2000 are seeing........2-3 seconds on 32M runs :confused:

i wonder if japanese letters/numbers refresh quicker :p:

Quote:

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Originally Posted by dinos22

that's what me and eva2000 are seeing........2-3 seconds on 32M runs :confused:

i wonder if japanese letters/numbers refresh quicker :p:

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lol i love ur sig~

jhehehe

oh no no more sig video lol

That is strange, I better stick to 1.4 :D

yeah for me most of time 32M, v1.4 is faster than v1.5

Going after my 32M personal best time which was done with 2x 512MB XMS3500C2v1.1 BH-5 but this time with 2x 512MB OCZ PC3200 Gold UTT BH-5

--------------------------------------------------------------
:: 2x 512MB Corsair XMS3500C2v1.1 BH-5 ::
10x315HTT = 3150Mhz
166 divider
262mhz 2-2-2-5 7-14 2223 at 3.48v vdimm in bios
:: Super Pi v1.4 mod
32M = 22m 56.875s old/current personal best
32M = 22m 57.859s

vs


--------------------------------------------------------------
:: 2x 512MB OCZ PC3200 Gold UTT BH-5 ::
10x315HTT = 3150Mhz
166 divider
262mhz 2-2-2-5 7-14/16 2223 at 3.54v vdimm in bios
:: Super Pi v1.4 mod
8M = 4m 48.765s
32M = 22m 57.984s

:: Super Pi v1.5 XS
262mhz 2-2-2-5 7-16 2223 at 3.54v vdimm in bios
8M = 4m 48.516s new personal best 8M time
32M = 22m 56.890s 0.015s behind my old 32M PB heh






--------------------------------------------------------------

mmm... 1.5 added bout 6(ish) seconds on to my time :(

I took a closer look to 1M and made a statistical analysis...;)

System:
CPU: Opteron 144 @ 333 x 9
MoBo: DFI NF4 Ultra-D with 702-1
RAM: OCZ VX @ 250 MHz @ 1.5-2-2-4
OS: WXP SP2

Test:
- start System
- start 1M and run it once
- run 1M 10 times in a row an take results
- analyse data

Software:
- Minitab 14

First you have to perform a normality test to see, if your data-set is normally distributed. This is important to choose the right method for the comparison.

1.4 Mod:
http://oc-x.de/PICS/1M_14_Normal.jpg

P-value > 0,05 => normal distribution
Mean: 27,602 s
StDev: 0,026

1.5 Mod:
http://oc-x.de/PICS/1M_15_Normal.jpg
P-value > 0,05 => normal distribution
Mean: 27,605 s
StDev: 0,018

That means, we can use a so called 2-sample-t-test to compare both data sets, confidence interval set to 99 %:

First a individual plot for the data, second a box-plot:
http://oc-x.de/PICS/1M_Individual.jpg
http://oc-x.de/PICS/1M_Boxplot.jpg

And here in printed version, the test was set to not equal, which is our hypothesis:

http://oc-x.de/PICS/1M_2_sample_t.jpg

Conclusion

With this high p-value in this case (0,783 - needs to be smaller then 0,05 to keep the hypothesis) we can reject the hypothesis from a statistical standpoint with a savety of 99 %, NO difference between the 1.4 and the 1.5 Version for 1M.

1.4 is sometimes faster, but with a bigger variance compared to the 1.5-Version.

I will repeat this with 25 datapoints each and also for 32M...;)

can you tell us more about the timings you used.....whether you used auto or set values

Same OS and same settings(240x10 2/2/2/0)

1.4 version=34.409
1.5 version=34.469

Who? Me?

Set most at manuel, but didn´t change...

1T-1.5-2-2-4-12-16-3-2-1-3-... and the rest I forgot, but can post this evening...;)

I just saw, that I have a big spread in the results, even after a reboot.

I think it make sence to use more datapoints at 1M, 25 should be enough...

This version is slower at my pc also.. fugger must have bananed something when he changed the program :--)

For my FX-57 SuperPi 1.1M is faster than 1.4 & 1.5.
1M (SuperPi 1.1 , FX57 @ 3.7GHz) - 24s (sometimes 25s)
1M (SuperPi 1.4 & 1.5 , FX57 @ 3.7GHz) - 25.xxx (always)

Good work guys! Very interesting, I think I'll try some comparisons as well!

Denny did you run them seperatly? If you run both at the same time the time varies a bit ;)

http://afireinside.evilspork.net/AFI...h/285032M2.JPG

http://afireinside.evilspork.net/AFI...h/295016M2.JPG

Thought I'd give it a go as well but for the sake of time I only did 1M's.
SPi 1.1 only showed whole numbers, all the same.
SPi 1.4 gave 26.422s (average of 5 runs)
SPi 1.5 gave 26.431s (ave. 5 runs)
After deleting dat files I reran:
SPi 1.4 gave 26.406s (ave. of 5 runs)
SPi 1.5 gave 26.431s (ave. of 5 runs)

So, I guess I know what I'll be using for 1M. Should be similar for 32M?

Ok, some more comparisons...

@HWL a discusson came up, if it´s accurate enough to run them afterwards or only after a restart.

So i compared this for 1.4 Mod...;)

First test run was done like this:
- start computer
- run 2 x 16k (was a recommadation from somebody from HWL to give more accurate results)
- run 1M and pic result
- and restart

mean: 27,652 s

standard deviation: 0,069 s

http://oc-x.de/PICS/1M_14_Normal_res.jpg

http://oc-x.de/PICS/I-MR_14_restart.jpg

Then I tried i 10 times in a row, means:
- start computer
- run once 1M
- then 10 times in row an pic result

mean: 27,586 s

standard deviation: 0,020 s

http://oc-x.de/PICS/1M_14_Normal_line.jpg

http://oc-x.de/PICS/I-MR_14_line.jpg

Here the results compared in box-plots:

http://oc-x.de/PICS/1M_Boxplot_line_res.jpg

That means, the variation for method one ist bigger, even with a higher mean...;)

Holy cow stummerwinter! Impressive results you have there...I wish I could understand it all as easily as you do, back to school for me?
Could you give more explanation with your conclusions please?

i really think you need to use results with at least 8M runs....rather than 1M.....too much variation there....

I was curious how 1M would turn out between the different versions as I am about 32M. Repeatability is another point of interest and desireable I would think.
There must be so many variables with this, perhaps some of which are particular to an individuals system but I wonder if most peoples results would be similar in terms of which version of SPi produces faster times for any given test?

@tarrcm: Ok, first some background informations...

I joined a 6 Sigma-training at my job last year, so no way to learn that in school...;)

6 Sigma as a methody to analyze in a strucered why a process (whatever you like) and improve it to a level of maximun 3,4 defects in a 1.0000.000 pieces (whatever).

With help of statistical tools (I used here to analyze Minitab 14) I took a closer look here to the variances of SuperPi.

Coming to the point, every process (here running SuperPi) has a variation. And I tried to figure out, how big this variation is.

For example take the 10 times 1.5 MOD XS run:

I run it 10 times, gave me 10 results.

The average (mean) was 27,605 s (sum up all results and dived it by 10 ;) ). 1 standard deviation (equals to 1 Sigma) was 0,018 s

That means, from statistcal standpoint based on these ten values:

- with a probability of 95 % (1 Sigma) you will get 27,605 s ± 0,018 s at further measurements

- with a probability of 99,9996 % (6 Sigma) you will get 27,605 s ± 0,648 s at further measurements


The original Ide was to have a closer look to the topic, which Pi-Version is faster (first results) and if there ist a difference running Pi in a row (without restarts) or restart it for every run.


Conclusion:

to 1) no difference between 1.4 and 1.5 at 1M

to 2) the variation is smaler when you run 1M in a row instead of restarting the system (in this case, running first 2 x 16k)


@dinos: The variation I measured was very small...;)

But you are right, would be interesting doing it for 8M or 32M...;)

I´ll start with 8M...:D

Thanks stummerwinter! I think I'll let you do the analysis & I'll buy the beer :toast:

8M is allready in progress...;)

Beer from America?

Only this one: RAMSTEIN wheet beer

Befor the germans here kick my ass i need to say, that this guy learnd his business in Germany at Erdinger. He made his brewmaster there, really a very good beer!

I had the pleasure during my time in Wayne / NJ in 2003 tasting it.

did this a while ago on Sempron @2.5GHz:

Attachment 45728

so no issues here. :)

Quote:

---

Originally Posted by stummerwinter

8M is allready in progress...;)

Beer from America?

Only this one: RAMSTEIN wheet beer

Befor the germans here kick my ass i need to say, that this guy learnd his business in Germany at Erdinger. He made his brewmaster there, really a very good beer!

I had the pleasure during my time in Wayne / NJ in 2003 tasting it.

---

Not exactly...I ment Germany, I'll go there :D
I'll keep my eyes open for that! The best I've had to date was Kulmbacher.

So, took a while (was bussy with my DICE-test), but have now 8M-results ready...;)

Same system, like posted before...

Test performing: 1 x 1M, then 10 x 8M in a rowe and take results

First, normality tests again:

8M 1.4 Mod

http://oc-x.de/PICS/8M_14_Normal.jpg

Normaly distributed!

8M 1.5 Mod

http://oc-x.de/PICS/8M_15_Normal.jpg

Normaly distributed!

Comparison:

http://oc-x.de/PICS/8M_Individual.jpg

http://oc-x.de/PICS/8M_Boxplot.jpg

Result:

This time, 1.4 Mod is slightly faster then the 1.5 Mod version!

Two-sample T for 8M 14 Mod vs 8M 15 Mod

N Mean StDev SE Mean
8M 14 Mod 10 304,899 0,134 0,042
8M 15 Mod 10 305,010 0,152 0,048


Difference = mu (8M 14 Mod) - mu (8M 15 Mod)
Estimate for difference: -0,111000
99% CI for difference: (-0,296757; 0,074757)
T-Test of difference = 0 (vs not =): T-Value = -1,73 P-Value = 0,101 DF = 17

At leat, it´s just 0,1 s at 8M...;)

32M is in preparation...;)