CPU Performance Data¶

The data set consists of 209 CPU's, and dates from 1982. It includes brand, model and 6 key test metrics, as well as BYTE magazine’s Published Relative Performance benchmark, PRP.
It was a secret how much a feature was affecting the benchmark, and some insight into this would have been a competitive advantage.
The ERP, Estimated Relative Performance, was calculated by Ein-Dor and Feldmesser in Tel Aviv about 1987. They used linear regression and recorded a 34% average deviation from actual values.

Relative CPU Performance Data¶

Source: UCI Machine Learning repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).

vendor name: 30 (adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec, dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson, microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, sratus, wang)
Model Name: many unique symbols
MYCT: machine cycle time in nanoseconds (integer)
MMIN: minimum main memory in kilobytes (integer)
MMAX: maximum main memory in kilobytes (integer)
CACH: cache memory in kilobytes (integer)
CHMIN: minimum channels in units (integer)
CHMAX: maximum channels in units (integer)
PRP: published relative performance (integer)
ERP: estimated relative performance from the original article (integer)
The Estimated Benchmark Performance ERP was calculated by Prof. Ein-Dor of the Tel Aviv university and a colleague around 1988 on an Amdahl 470v/7 CPU.

First, let's load the dataset and look what features are collected for the CPU's:

	Vendor	Model	MYCT	MMIN	MMAX	CACH	CHMIN	CHMAX	PRP	ERP
0	adviser	32/60	125	256	6000	256	16	128	198	199
1	amdahl	470v/7	29	8000	32000	32	8	32	269	253
2	amdahl	470v/7a	29	8000	32000	32	8	32	220	253
3	amdahl	470v/7b	29	8000	32000	32	8	32	172	253
4	amdahl	470v/7c	29	8000	16000	32	8	16	132	132

In the 1982 landscape, Amdahl delivers 9 CPU's of the premium class.

	MYCT	MMIN	MMAX	CACH	CHMIN	CHMAX	PRP
0	125	256	6000	256	16	128	198
1	29	8000	32000	32	8	32	269
2	29	8000	32000	32	8	32	220
3	29	8000	32000	32	8	32	172
4	29	8000	16000	32	8	16	132

calculate RMSE for the estimates by the Prof. Ein-Dor¶

The original ERP vs BYTE's PRP metrics, reported on UCI, were measured as mean deviation percentage. This is not standard today, so later authors have chosen to use RMSE.

Darragh Hanley: "Best Linear Regression RMSE : 68.35" (2015)
Johannes Ledolter: "RMSE : 69.35" with Leave one out Cross Validation. (Data Mining and Business Analytics with R)

Apparently, the RMSE results of later preformed linear regressions came not close to the accuracy of the original Linear Regression, as found in the dataset field ERP (Estimated relative performance).

Mean Absolute Error: 24.330143540669855
Mean Squared Error: 1737.3349282296651
Root Mean Squared Error: 41.68134988492653

manual split¶

to isolate the ERP and PRP values, before splitting

	MYCT	MMIN	MMAX	CACH	CHMIN	CHMAX
10	400	1000	3000	0	1	2
14	350	64	64	0	1	4
15	200	512	16000	0	4	32
17	143	512	5000	0	7	32
18	143	1000	2000	0	5	16
...	...	...	...	...	...	...
202	180	262	4000	0	1	3
203	180	512	4000	0	1	3
204	124	1000	8000	0	1	8
206	125	2000	8000	0	2	14
208	480	1000	4000	0	0	0

69 rows × 6 columns

	MYCT	MMIN	MMAX	CACH
122	1500	768	1000	0
123	1500	768	2000	0
124	800	768	2000	0
207	480	512	8000	32
208	480	1000	4000	0

feature engineering 1¶

	MYCT	MMIN	MMAX	CACH	CHMIN	CHMAX	PRP
0	125	256	6000	256	16	128	198
1	29	8000	32000	32	8	32	269
2	29	8000	32000	32	8	32	220
3	29	8000	32000	32	8	32	172
4	29	8000	16000	32	8	16	132

feat engineering was no success, so I'll use KFold methode...

auto split¶

test_size=0.22, random_state=42

	MYCT	MMIN	MMAX	CACH	CHMIN	CHMAX
count	209.000000	209.000000	209.000000	209.000000	209.000000	209.000000
mean	203.822967	2867.980861	11796.153110	25.205742	4.698565	18.267943
std	260.262926	3878.742758	11726.564377	40.628722	6.816274	25.997318
min	17.000000	64.000000	64.000000	0.000000	0.000000	0.000000
25%	50.000000	768.000000	4000.000000	0.000000	1.000000	5.000000
50%	110.000000	2000.000000	8000.000000	8.000000	2.000000	8.000000
75%	225.000000	4000.000000	16000.000000	32.000000	6.000000	24.000000
max	1500.000000	32000.000000	64000.000000	256.000000	52.000000	176.000000

CPU Performance Data¶

Exploration¶

Relative CPU Performance Data¶

Load the data¶

calculate RMSE for the estimates by the Prof. Ein-Dor¶

manual split¶

feature engineering 1¶

auto split¶

Cycles per time unit vs. cache size¶

Mean, sd and outliers of the 6 features.¶