The Relationship Between CPU Frequency And Performance. Critique, Also Answer Qs?

The Relationship Between CPU Frequency And Performance. Critique, Also Answer Qs?


The Relationship Between Processor Frequency and Performance.



Abstract


Those who work in the computer science field are always asking themselves one question, “how can I get more performance?” To satisfy these performance addicts and to stay profitable, Central Processing Unit (CPU) manufacturers have to ask themselves, “Are we manufacturing these CPUs in the most efficient manner?” Efficiency equates to money, and that’s why it’s important to understand the fundamentals of microprocessors and what laws govern them.

A CPU plays a pivotal role in anyone’s computer system. It’s what makes the computer tick, and it’s one of the most common culprits if a system is acting sluggish. The intention of this project was to find out what kind of relationship frequency and performance had, and if there is any way one could utilize this relationship to one’s advantage. The hypothesis theorized that the relationship between frequency (speed, measured in megahertz) and performance (as measured by benchmarking applications) would be absolutely linear.

The experiment was conducted in a relatively simple manner. A modern microprocessor was clocked at different frequencies, tested for performance with several benchmarking applications, and then it was rebooted, and the process was repeated for other frequencies up to 2400 mhz. Results were recorded after each step. The frequencies covered were, 1000 mhz, 1100 mhz, 1200 mhz, 1300 mhz, 1400 mhz, 1500 mhz, 1600 mhz, 1700 mhz, 1900 mhz, 2000 mhz, 2100 mhz, 2200 mhz, 2300 mhz, and 2400 mhz. This supplied a very wide range of frequencies, and was sure to provided an adequate range of data.

To see which model fit best, the exponential and linear models were made for each respective benchmark, then the r value, also called the correlation coefficient, was calculated. The Correlation coefficient shows how strongly a model actually fits the data. The results were quite controversial. While there were some benchmarks that favored a linear model, there were some benchmarks that in fact more closely modeled an exponential. While there may have been experimental errors, none could account for the consistent results.

While it may be tempting, it is impossible to draw any concrete conclusions from this data alone. That doesn’t make this experiment useless, in fact, quite the opposite. It has opened up a new subject of discussion, and opened the door to future testing. Hopefully, with more expansive and controlled tests, the correlation may finally be figured out once and for all.





Introduction

One of the demons plaguing computing is performance. Microprocessor manufacturers strive to release the best possible product, in a cutthroat race to the top, claiming the performance crown. But just what forces act upon this performance-oriented race? This experiment intends to determine whether a microprocessor’s frequency, as measured in megahertz (mhz), has a linear, or exponential relationship to performance, as measured by benchmarking applications.

Wikipedia.com defines a Central Processing Unit (CPU) as, “the part of a computer that interprets and carries out the instructions contained in the software.” Simply put, the CPU is the heart of the modern computer. When all parts of a CPU are on a single Intergrated Circuit (IC), it is referred to as a microprocessor. A CPU gets its clock speed from a simple equation, Front Side Bus (FSB) times Multiplier equals resulting frequency. (http://en.wikipedia.org/wiki/CPU)

While this may sound very complex, in reality it is not. Very simply, a CPU takes in raw instructions and data from the other parts of the computer, manipulates the data and runs processes as directed by other components, and outputs results. Whenever anything occurs on one’s system, the CPU has computed it. Almost no information is passed on to the user without first being manipulated by the CPU.

One of the main concerns of chip makers is clock speed. Clock speed is also dubbed frequency or operating speed and it is measured in megahertz. The faster the clock speed, the more MIPS, or “Millions Of Instructions Per Second”, a CPU can execute. CPUs will perform several different functions on data it obtains from the rest of the computer, and depending on desired output, it will manipulate it accordingly. Increase the frequency and it will be able to fetch more data at a time, and complete the computations faster.

If chip manufacturers are able to understand the relationship between frequency and performance, then they may be able to estimate future limitations and ceilings ahead of time, and engineer ways to overcome them.

Gordon Moore predicted the density of microprocessors will double every eighteen months. More chip density means smaller circuits in a smaller amount of space, allowing for even faster frequencies, thereby, more performance. This being an exponential equation, one may wonder of limitations to this law—and many people have. There are constant debates and contradicting articles on the Internet disputing the credibility of the law being applicable forever, and some in defense of it. Surely there must be some point where the equation can’t take any more—some point at which the chip manufacturers are unable to stuff any more transistors in an already tightly packed space. While that battle rages on, there are yet other frontiers, which need to be conquered. If there is a limit to how much we are able to get out of microprocessors, how will be make them more efficient, and faster? (http://www.intel.com/research/silicon/mooreslaw.htm)

There are many optimizations that chip manufacturers released to help increase performance as well—SSE, special instruction sets, larger on-die cache, the list goes on and on. The bottom line is they are not only blindly increasing megahertz. The question arises, disregarding the other exponential relationships in computing, is there a possibility that there is an exponential relationship occurring with the same chip that is clocked at different frequencies?

Thus, this experiment was formed. The hypothesis was that the relationship between processor frequency and resulting performance must be a linear relationship. Logic would tell one that the faster clocked the processor, the more instructions it will be able to perform. As you double the speed of the processor, you double the raw performance.








Experimental Design

The experiment was set up to be fairly easy, as any benchmarking would be energy consuming to the computer, not the researcher. The experiment required a test bed system. An AMD AthlonXP 1700+ Processor, an Epox 8rda+ motherboard, 2x256MB pc3200 Kingston RAM, an ATI Radeon 9800 Pro, two Western Digital 120GB 7200RPM drives, one self built case, and one self built watercooling system were used in this experiment. Watercooling was chosen for the fact that in such a system ambient temperature has the least effect on the temperature of the processor, thus eliminating one of the variables.

The benchmarks that were chosen were, Sisoft Sandra 2005, Pifast, Prime95, and ScienceMark2005. These benchmarks were chosen for their diversity in the way they stressed the microprocessor—Sisoft Sandra had an arithmetic as well as a multimedia mode, Pifast uses a method to benchmark how long the micrprocessor takes to calculate millions of digits of pi (For more on the method used for calculation of Pi see Appendix A), Prime95 used equations to find prime numbers, and ScienceMark fed the microprocessor equations to solve to gauge how long it took the microprocessor to solve several scientific equations. All of these benchmarking applications tax the CPU in a controlled way; they feed the CPU data, then record the amount of time it takes for it to output a result.

In preparation for the experiment, the system had all the startup applications disabled via msconfig (a system configuration utility), so there was nothing to call upon the CPU and impact results. To further eliminate any variables, the Vcore (voltage given to the CPU during use) was kept the same throughout the tests. FSB (Front Side Bus) was also kept at 200 MHZ. The first multiplier was booted at 5, giving the chip a 1000 MHZ clock speed. The system was then booted into Windows XP, at which point a program called Free Ram Optimizer XP was executed. To make sure all the systems had similar access to the same amount of RAM, Free Ram Optimizer XP was used to clear up 400 MB of system memory. Upon completion, Sisoft Sandra was loaded, and the Arithmetic and multimedia benchmarks were completed (see appendix D for screenshot). Afterwards, once again, Free Ram Optimizer XP was used before the next benchmark. Next, a set .bat file was used to execute Pifast (see Appendix C for a screenshot of Pifast in action), and have it calculate 33554432 digits of pi (see Appendix B for the contents of the .bat file). Afterwards, Free Ram Optimizer XP was executed to clear the memory of any lingering, unused information (see appendix E for screenshot). Prime95 was then opened, and benchmark mode was enabled. Prime95 recorded the times it took to calculate certain prime numbers at different settings (see appendix F for screenshot). Prime95 was then closed, and Free Ram Optimizer XP was opened for one last time. Science Mark 2005 was then opened, at which time the Molecular Dynamics program was loaded (see appendix G for screenshot). The standard model was rendered, and the program automatically recorded the time it took to render. Then Primordia (a sub application of Science Mark 2005) was run, which simulated different conditions upon Aluminum (see appendix H for screenshot). The last benchmark contained within Science Mark 2005 was a Cipher benchmark (see appendix I for screenshot). The computer recorded how long it took to cipher using default settings. Upon completion, the system was rebooted, and the multiplier was changed to 5.5, resulting in a 1100 MHZ frequency. This process was repeated, in intervals of .5 multiplier additions, until the frequency of 2400 MHz was obtained. At which point, the limitations of the equipment were prevalent, and the computer would not boot higher than that frequency. The only limitation that this method presented was the fact that there was no 9.0 multiplier enabled on the test motherboard, therefore it was impossible to test at 1800 MHZ. A listing of the procedure used while testing, a quick field reference, is listed in Appendix K.












Results
For a full listing of results put in data tables see appendix V.

All results were recorded, and statistical analysis was done upon them to find out the most appropriate model. Included with all results are two statistical model lines, one linear, and one exponential. Also calculated was the correlation coefficient (r) of the exponential and linear data sets to the original data. Appendix L shows the first set of data, Prime 95 Best Times (in ms) at all the tested frequencies.

Appendix M shows the results for the Pifast calculations, along with exponential and linear models. Appendix N represents the time taken by the different frequencies to do the Molecular Dynamics model, as well as corresponding statistical models. Appendix O shows the primordia calculation time for the varying frequencies. Appendix P shows the time taken to finish the Cipher routine. Appendix Q shows the amount of bandwidth yielded by the cipher routine. Appendix R shows the Sisoft Million Instructions Per Second (MIPS) benchmark results. This is a very basic means of measuring the raw amount of data a CPU can handle. Appendix S represents the Million Floating Point Operations Per Second the frequencies were able to churn out. Appendix T and U show the result of the multimedia benchmark, in IT per second.




Discussion

The hypothesis was that the performance would follow a completely linear relationship. As confusing as it may sound the results were very inconclusive. In some of the tests, such as cipher time, primordial time, Molecular Dynamics time, and Prime 95 time, the exponential model fit much better than the linear, while still not absolutely perfect. The r value tells us which model has a stronger relationship to the original data, and for all these the r value was closer to one with the exponential rather than the linear, indicating a strong exponential relationship. Every other benchmark that was encountered, the linear model fit perfectly, while the exponential model was quite inaccurate.

While the linear results seem logical, and perfectly fit, it is almost inexplicable why, in some tests, an exponential model fit better. One hypothesis might be the way in which the benchmarks were formed. The benchmarks that fit the linear model were more geared towards theoretical performance, while the other benchmarks were real world situations. This is definitely an open door to the future for more research as to why a CPU might behave exponentially in real world tests, while adhering to linear performance in theoretical benchmarking. Further research on the topic could lead CPU manufacturers to more efficient processors that get more done with less effort.

Unfortunately, as all experiments do, there were multiple possibilities for sources of error in this project. The most obvious error is an errant process running, taking precedence over the benchmarking program, and delaying, thus skewing, the results of the benchmark. Windows isn’t necessarily the best platform to run these tests, as there are many objects always running in the background other than the bare operating system, which may have been a source of error.

Another error may have been temperature. Despite best efforts to minimize the impact of varying temperatures with a watercooling system, the ambient temperature did fluctuate. Microprocessors operate at peak efficiency at cooler temperatures, therefore if it was colder within the room in which one test was conducted, it may yield slightly better performance than another test.

One other source of error may have come from the network. The system was plugged into the network at the time of benchmarking. Often, computers will receive ping requests from other computers, and normal networking duties need to be carried out. When a computer receives a ping request it is mandated to respond, and that response may have taken cycles away from the benchmark, which was running at the time.

If repeated this experiment would need a lot more expanding. Perhaps a better system than using “Free Ram Optimizer XP” to optimize the memory after every benchmark would be to restart the system after every benchmark, giving a similarly fresh system every time. Another factor to consider would be the limited scope of tests available. If expanded upon, a wide variety of tests should be used, and perhaps the research should be conducted in an environment-controlled facility. Another limitation was the CPU itself. Only one microprocessor was available at the time, the AMD Athlon XP. If more research were to be conducted, it may be beneficial to include many different CPU architectures to be tested, such as the AMD 64 bit, or the Intel P4.





















Bibliography

xgourdon (2004). Algorithm used by PiFast. Retrieved November 20, 2004, from the World Wide Web: http://numbers.computation.free.fr/Constants/PiProgram/pifast.html

Science Mark 2 Team (2004) Benchmark HowTo’s. Retrieved November 21, 2004, from the World Wide Web: http://www.sciencemark.org

Nick Tredennick & Brion Shimamoto (2004) The death of microprocessors. Retrieved November 05, 2004, from the World Wide Web: http://www.embedded.com

Online Author (2004) Moore’s Law. Retrieved December 01, 2004, from the World Wide Web: http://www.intel.com/research/silicon/mooreslaw.htm

Online Author (2004) CPUs. Retrieved November 25, 2004, from the World Wide Web: http://www.wikipedia.org

Gordon Moore (2003) No Exponential is Forever … but We Can Delay ‘Forever’. Retrieved November 28, 2004, from the World Wide Web: ftp://download.intel.com/research/silicon/Gordon_Moore_ISSCC_021003.pdf



APPENDICIES


Appendix A

Algorithm used by PiFast
The program implements a Brent binary splitting method together with an efficient cache handling hermitian FFT to multiply big integers (NTT with several primes is used for huge computations). To compute p, it is based on the Chudnovsky formula
426880 _____ض10005
p = هn ³ 0 (6n)!(545140134n+13591409) (n!)3(3n)!(-640320)3n ,

which adds roughly 14 decimal digits by term.
PiFast also proposes a second method for verification, based on a Ramanujan formula
1 p = 2ض2 هn ³ 0 (4n)!(1103+26390n) 44n(n!)4 994n+2 ,

which adds roughly 8 decimal digits by term.
My experience is that a careful implementation of these techniques seems better than any other approaches (like AGM based formulaes for example) for reachable number of digits.
PiFast implements the computation of E with two formulas, namely
e = ¥ هn = 0 1 n! and e = ( ¥ هn = 0 (-1)n n! )-1.

From version 4.0, PiFast permits to compute a large family of user defined constants from linear combination of general hypergeometric series. The algorithm also uses binary splitting, which generalizes well to hypergeometric series.
(http://numbers.computation.free.fr/Constants/PiProgram/pifast.html)


Appendix B

The .bat file contained these simple parameters:
PiFast, version 4.3 (fix 1) (Copyright 1999-2003 Xavier Gourdon)
http://numbers.computation.free.fr/Constants/PiProgram/pifast.html
Menu :
[0] Compute Pi with Chudnovsky method (Fastest)
[1] Compute Pi with Ramanujan method
[2] Compute E by the exponential series exp(1)
[3] Compute E by the exponential series 1/exp(-1)
[4] Compute Sqrt(2) (useful for testing)
[5] Define your constant with hypergeometric series
[6] Compute a user constant from a .pifast file
[7] Decompress a result file
[8] Check a compress result Pi file
Enter your choice : 0

Choose your computation mode :
[0] standard mode (no disk memory used)
[1] basic disk memory mode (for big computations)
[2] advanced disk memory mode (for huge computations)
Enter your choice : 0

Number of decimal digits : 32M
(33554432 digits)
Possible FFT modes, with approximate needed memory :

FFT Size=4096 k, Mem=230640 K (Fastest mode)
FFT Size=2048 k, Mem=148720 K (Time: Fastest mode * 1.1)
FFT Size=1024 k, Mem=107360 K (Time: Fastest mode * 1.3)
FFT Size= 512 k, Mem=86880 K (Time: Fastest mode * 1.7)
FFT Size= 256 k, Mem=76440 K (Time: Fastest mode * 2.7)
FFT Size= 128 k, Mem=71320 K (Time: Fastest mode * 4.7)
...

Enter FFT Size in k :4096

Compressed output (also useful to specify output format) ? [0=No, 1=Yes] : 0

Basically, the .bat file just specified the fastest way to compute pi, told it to use no disk memory (as that would be another bottleneck and possible variable), compute 33554432 digits, and to use 230,640 K of memory.



Appendix C

Screenshot of pifast’s completed computation with CPU information provided by WCPUID on top.
Appendix D

Screenshot of Sisoft Sandra Arithmetic Benchmark.










Appendix E

Screenshot of Free Ram Optimizer XP

Appendix F


A picture of Prime95 Benchmarking.
Appendix G


A Screenshot of the molecular dynamics program in action.



Appendix H
A shot of the primordial simulation.
Appendix I
Screenshot of cipher in action.
Appendix K
Variable issues:
Temperature
Program Error
Network computation


Procedure:


Boot At specified frequency.


Use "Free Ram Optimizer XP" Free up 300.00 MB of memory before benchmark load

WCPUID ON SIDE OF SCREEN!

---
Open Sisoft Sandra
Run Sisoft Sandra CPU Arithmetic Benchmark

SCREENSHOT! File Name: SSSCPUABENCH$frequency$$DATE$.PNG

run Sisoft Sandra CPU Multi-Media Benchmark

SCREENSHOT! File Name: SSSCPUMMBENCH$frequency$$DATE$.PNG
+++++++++MOVE FILES TO F:\Benchmarks WITH APPROPRIATE AREA SAVED+++++++++

---

Use "Free Ram Optimizer XP" Free up 300.00 MB of memory before benchmark load

---

run f:\program files\pifast\PIFAST32M.bat

SCREENSHOT! COMPUTATION OK... File Name: PIFAST$frequency$$DATE$.PNG
CHANGE LOG FILE NAME! SAVE AS: PIFAST$frequency$$DATE$.TXT
+++++++++MOVE FILES TO F:\Benchmarks WITH APPROPRIATE AREA SAVED+++++++++

---

Use "Free Ram Optimizer XP" Free up 300.00 MB of memory before benchmark load

---

Run CPU Right Mark
Open prime95
Benchmark, make sure log is there

+++++++++MOVE FILES TO F:\Benchmarks WITH APPROPRIATE AREA SAVED+++++++++

---

Open Science Mark

run Molecular Dynamics Simluation: Defualt settings, 140 K Temperature

Run primordia Simulation: Aluminum

Run Cipher Benchmark

CHANGE LOG FILE NAME! SAVE AS: SM$frequency$$date$.txt
CONFIRM THAT ALL RESULTS ARE CONTAINED.

+++++++++MOVE FILES TO F:\Benchmarks WITH APPROPRIATE AREA SAVED+++++++++

Rest of the apendicies.

Okay, anyhow. That was my computer science project. Let me know what you guys think, and I REALLY need some suggestions/explanations as to why the more synthetic benchmarks were linear, but others were exponential, or so it seemed. I've got to present this on sunday, so I'd much appreciate any help or criticism I could get. Oh, as always, feel free to quote, redistribute, or whatever to my paper, as long as you give myself, Rami Saikali, credit.

Thanks,

-Excelsior

All I can say is..... Wow.

It's not that long. Main paper is only 8 pages double spaced, four single spaced. Also, the last line in the main paper about P4 and a64 being different ARCHITECTURE... just ignore it, I know they're both x86, just I needed a filler word and I couldn't find it at the time. It'll be changed most likely.

Holy crap man thats nice, I am way to lazy to read all that. Also I am sure you covered this in there but from what I have read some CPUs like the presscot dont perform as well at low speeds, at higher speed the perform better though. Due to things like cache and fsb so its not just the clock freuquency. Any way nice work on papaer and good luck getting someone to read all that.

I REALLY need some suggestions/explanations as to why the more synthetic benchmarks were linear, but others were exponential, or so it seemed.

Click to expand...

I didn't read all of your stuff, but it seems like usually this is do to the cpu cache size and the data set the benchmark uses.


IMHO performance is proportional to frequency. And it's also proportional to efficiency (efficiency being measured by the amount of work done per clock cycle). Too many people want it to be ONLY one or the other, and then declare the side who's engineering teams favored the other to be wrong.

If Performance P = (Frequency F)(Efficiency E), then you are going to make up for a low F or a low E by boosting the other one. Surprisingly, this is exactly what Intel and AMD do. Intel went with a high-F/low-E design, and AMD went with a high-E/low-F design. They both yield about the same performance.

Like I said, I haven't read your paper, but if your paper tries to tackle this subject I hope it's coming out to the same answer.

Please attach a doc version in a zip to your post, and I will take a look over the entire paper, then offer any advice I might have.

STICKY!!
I dont have time to read it right now...but damn!

Its pretty big, maybe to big for a thumbtake to support. Just messing yea this may be a cool sticky.

I.M.O.G. said:

Please attach a doc version in a zip to your post, and I will take a look over the entire paper, then offer any advice I might have.

Click to expand...

<3 you and anyone else willing to take even a few minutse to try to help

I'll upload and attach the .doc files, as well as one .xls with the graphs/data tables in a second hopefully.

Essentially, I knew there are quite a few Relationships in computing.. like moore's law which is exponential. So I wanted to know if the relationship between frequency in mhz, and performance, measured by benchmarks was linear or exponential. And in a lot I got mixed results. That's kinda where I left off. Some were REALLY linear, almost to a tee. And some had a VERY strong leaning towards exponential. This intrigued me, as I thought it'd be strictly linear.

holy crap dude.

i jsut read all of that. and man its nuts. but its GREAT. this should not only prove a very interesting thread, but also feel that this is also sticky material. not sure which area exactly, but actual data on overclocking, and the difference with performace compaired to speed is great data man.

honestly, i'm not sure why some of the tests are different. as said before, on die cache and the way and how much the program uses can cause a huge difference in benchies.

my friend has a laptop, his cpu's frequency is lower than mine, but he has 2 megs of L2 cahce. I on the hand have 512k of cache. he can do a WU on boinc faster than i can.

Man, i really hope this project would end up with some answers. because this alone is propbably the best question i have ever seen on these boards, that had to do with overclocking. and causes everyone to think.

sorry i'm no real help on this. but i will definetly keep updated on this thread.

Okay, all done archiving (.rar) I can put it in .zip as well if you want...

Anyhow, the files in it are as follows:

1. Main Paper
2. Appendix A-K.doc
3. Appendix L-T (graphs).doc
4. Raw Data.xls

Then the rest is extra, as well as the fact that I included the working directory with ALL pifast logs in thier entirety, as well as TONS of screenshots that I never incorperated into the paper due to lenght issues.

http://excelsior.zerobrains.com/Computer Science.rar

There she is. It should be finished uploading in appproximately three minutes, then you can view it.

Just kind of commentary on usefullness.. While it might just seem kinda fun at first, it might actually be useful for predicting performance on a particular application. If it was possible to further tests, one could make a database of fucntions to predict performance of a particular CPU, paired with a particur FSB, with a particular size of memory. Right now via these graphs, I could tell you the performance of that chip on frequencies in excess of 4000 mhz, if it was possible for it to be reached, within 5%. That's the power of extrapolation and being able to read between the lines/interpert data. If it was possible to make a large function, having it be different for each benchmark... Say the function for time it takes to finish prime95 at 2048k FFt. The factors that'd go in at first are just 512 MB Ram, fsb, , frequency. However if we were able to gather more information, we could find out how less ram, and more fsb affected the performance, and make a function to compensate for this, letting uis predict performance for any frequency witht he same cpu, any memory size, and any fsb. Obviously within reason, there will be some error due to bottlenecks in different chipsets, etcetera, however usually these differences are negligible (<5%), and it might be very useful/accurate to develop such methods.

It also somewhat shows the power of overclocking. It's nice to see performance kind of laid it right in front of your face. The math behind it is definitely fun as well. If you're doing one of the benchmarks that followed a linear path, technically all you'd have to do was boot up at one frequency, say 1000 mhz, then again at another, say 1500, and I could make a graph that would tell you the performance of said benchmark at 2400, or beyond within a reasonable error.

Ij ust find it all kind of fun, I'd really like some critique though

While your methods and procedures are sound, I think you need to work more on getting the "control" portion of the testing into smaller parameters. (No networking, level temperatures for the cpu through whatever means necessary to achieve, etc) so that your resuts have less of a chance to be berated in any way possible. Having an absolute in each test would be paramount in a higher level thesis. (end of constructive critism)

Other than that I give major kudos to you and look forward to seeing more out of you in the future. I am fwd this to my son to read at his school.

Pretty good. I have one question though. For one test that you did, tell me at what cpu speed would double that speed give a statistically insignificant boost in performance? That could be the absolute performance limit of the other hardware. To better investigate you could run an FX-55 at 3Ghz on a 128mb single stick of ddr200 pc1600 at 3-4-4-8 timings. I like your project, but I don't think you let something limit the cpu performance like you should. If your ram and motherboard were good enough, you would not have experienced any non-linear performance changes. For all the test benchmarks you used, you would have gotten all non-linear performance changes if your data had a broad enough range.

Remove said:

While your methods and procedures are sound, I think you need to work more on getting the "control" portion of the testing into smaller parameters. (No networking, level temperatures for the cpu through whatever means necessary to achieve, etc) so that your resuts have less of a chance to be berated in any way possible. Having an absolute in each test would be paramount in a higher level thesis.

Other than that I give major kudos to you and look forward to seeing more out of you in the future. I am fwd this to my son to read at his school.

Click to expand...

Aye, I know. Experimental Design was lacking. I identified literally dozens of variables. However, most of my results were very consisetent and didn't show signs of variation. Pifast however did incur the most variation, most likely due to memory issues. If I am able to repeat this most definitely I won't be running more than one benchmark on each bootup. The machine will be isolated, and while I did use watercooling to attempt to normalize the temperature, it will also be in a more temperature controlled evnironent. Pretty much It'd be a much longer procedure. Boot at frequency benchmark a single benchmark, reboot. Repeat for all benchmarks, then all frequencies. That'd be ideal. As well as including different CPUs like AMD64, Intel P4, as well as possibly Dual processors.

Quailane said:

Pretty good. I have one question though. For one test that you did, tell me at what cpu speed would double that speed give a statistically insignificant boost in performance? That could be the absolute performance limit of the other hardware. To better investigate you could run an FX-55 at 3Ghz on a 128mb single stick of ddr200 pc1600 at 3-4-4-8 timings. I like your project, but I don't think you let something limit the cpu performance like you should. If your ram and motherboard were good enough, you would not have experienced any non-linear performance changes. For all the test benchmarks you used, you would have gotten all non-linear performance changes if your data had a broad enough range.

Click to expand...

Sure, and this is one of the things the test wanted to check out. Which benchmarks gain NO performance increace past a certain point, and when does other parts of your system become a big hinderance in performance, and when does adding more MHZ just become not-beneficial in the long run. Interesting thing I wanted more elaboration on was that synthetic benchmarks remained linear, while some real-world(ish) benchmarks showed signs of exponential regression. This would suggests that it might help numbers to throw tons more frequency out there, it might not continue helping performance on a lienar front.

and when does adding more MHZ just become not-beneficial in the long run.

Click to expand...

All things being equal, shouldn't the answer be "never"?

It's just another factor of performance. It should never hurt to take increase it, but real-world issues like heat give you a short-term limit. But assuming heat isn't a problem going to a certain clock speed, then it is always going to be a good thing because IPC and bandwidth of the various components of the system won't suffer for it.

This would suggests that it might help numbers to throw tons more frequency out there, it might not continue helping performance on a lienar front.

Click to expand...

I think you can only extrapolate this if you can divine an equation expressing the performance of the cpu. The fact that it is sitting in a system that has a hard drive and ram and things like that in it, and that you are running benchmarks that might get bottlenecked by those things, doesn't help.

Excelsior said:

Okay, all done archiving (.rar) I can put it in .zip as well if you want...

Anyhow, the files in it are as follows:

1. Main Paper
2. Appendix A-K.doc
3. Appendix L-T (graphs).doc
4. Raw Data.xls

Then the rest is extra, as well as the fact that I included the working directory with ALL pifast logs in thier entirety, as well as TONS of screenshots that I never incorperated into the paper due to lenght issues.

http://excelsior.zerobrains.com/Computer Science.rar

There she is. It should be finished uploading in appproximately three minutes, then you can view it.

Click to expand...

I will take a look tonight if I get a chance and let you know what I think.