Here we have a summary of entire human history of CPU progress, like a in museum.

Added Metadata:
Important          - 1  (whenever to show in GUI only "major" generations)
KEYWORD_CPU_ARCH_ID- INTEL_CORE2DUO_1GEN_CONROE (family name / generation)
Clockrate          - 2400 (max/turbo clockrate of a single-core)
TDP                - 65   (real power consumption of a chip, CPU or SoC, in watts)
  TDP NOTES: my TDP value can be lower or higher than marketing agenda by vendors...
      TDP may be higher due to turbo-boost, or lower due to thermal throttling on my PC.
      TDP by vendors is often a marketing trick, which does not count turbo or throttling.
  

KEYWORD_ARCH =
---
AMD_K5
AMD_K6
AMD_ATHLON_1GEN_SLOTA
AMD_ATHLON_2GEN_SOCKET
AMD_ATHLONXP
AMD_ATHLON64
AMD_PHENOM1
AMD_PHENOM2
AMD_E1
AMD_FX
AMD_ZEN1
AMD_ZEN1PLUS
AMD_ZEN2
AMD_ZEN3
AMD_ZEN4
AMD_ZEN5
INTEL_486
INTEL_PENTIUM1
INTEL_PENTIUM1_MMX
INTEL_PENTIUM2
INTEL_PENTIUM3_1GEN_KATMAI
INTEL_PENTIUM3_2GEN_COPPERMINE
INTEL_PENTIUM3_3GEN_TUALATIN
INTEL_PENTIUM4_1GEN_WILLAMETTE
INTEL_PENTIUM4_2GEN_NORTHWOOD
INTEL_PENTIUMM_1GEN_BANIAS
INTEL_PENTIUMM_2GEN_DOTHAN
INTEL_CORE1DUO
INTEL_CORE2DUO_1GEN_CONROE
INTEL_CORE2DUO_2GEN_WOLFDALE
INTEL_CORE_1GEN
INTEL_CORE_2GEN
INTEL_CORE_3GEN
INTEL_CORE_4GEN
INTEL_CORE_5GEN
INTEL_CORE_6GEN
INTEL_CORE_7GEN
INTEL_CORE_8GEN
INTEL_CORE_9GEN
INTEL_CORE_10GEN
INTEL_CORE_11GEN
INTEL_CORE_12GEN
INTEL_CORE_13GEN
INTEL_CORE_14GEN
APPLE_A7
APPLE_A8
APPLE_A9
APPLE_A10
APPLE_A11
APPLE_A12
APPLE_A13
APPLE_A14
APPLE_A15
APPLE_A16
APPLE_A17
APPLE_A18
APPLE_A19
APPLE_M1
APPLE_M2
APPLE_M3
APPLE_M4
APPLE_M5
APPLE_M6
RPI_1
RPI_2
RPI_3
RPI_4
RPI_5
RPI_6
VIA_C3
VIA_C7




Intel:
486
Pentium 1
Pentium 1-MMX
Pentium II
Pentium III
Pentium 4
Pentium M (Centrino)
Core 1 Duo *missing piece from my collection... the missing link between the older Pentium III / M and Core 2 Duo.
Core 2 Duo
---
Core i7 870  - 1st gen 
Core i7 2600 - 2nd gen <-- here we have a massive drop in optimized ratio. Why ? Because the base score is 
Core i7 3770 - 3rd gen *very similar to 2nd gen; missing.
Core i7 4770 - 4th gen
Core i7 5600U- 5th gen *missing from the hi-end desktop lineup; mostly mobile generation for laptops.
Core i7 6700 - 6th gen
Core i7 7700 - 7th gen
Core i7 8700 - 8th gen
Core i9 9900K- 9th gen
Core i9 10900K- 10th gen
Core i9 11900K- 11th gen
Core i9 12900K- 12th gen


Curious:
Core i7 870  - 1st gen 
Core i7 2600 - 2nd gen <-- here we have a massive drop in optimized ratio. 
Why ? Because the base score is so much higher. Basically it can predict
*and* execute speculative code so much better, that it is less dependent on code optimizations.
Meaning that

Intel Xeons are based on core-i7 design, either directly or indirectly.

Where is Core i7 5700 ? missing ... because 14nm factory was delayed, and so intel decided to skip
and went to 6th gen on the desktop.


---
Script goals for Gemini AI Pro: (CPU architecture analysis tool)
---
(uploaded several txt files as examples: 486DX2, Apple M3 Max, Pentium III 1000 MHz, and Core i9-14900K.)

We are building a CPU architecture analysis tool.

I have a lot of benchmark results of different CPUs. About 200 models in total. Results were acquired via an App called "EvoBench", which is a multi-core version of the ancient Livermore Loops benchmark, and also compatible with modern phones, ported to Android and IOS.
Here I have uploaded 3 results as examples, in plain text files. Take a look at those files, make sure you can understand their structure.
Note that some files have critical keywords, such as "Clockrate" and "TDP", and some are missing this critical information. You should skip the files that are missing this information.
Each result is devided into 24 Livermore Loops (aka kernels). And into Optimized and Non-Optimized results.

Your task:

Develop a python script, that reads all the result files (in *.txt format), analyzes CPU architectures, every single one of them, that have "Clockrate and TDP" values, strong points and weak points of each.
Analysis goals are as follows:

1. Python script must read all *.txt files from current directory, and skip files with missing "Clockrate and TDP" values, and output HTML files into the very same current directory, where the python script is run.
2. Normalize every CPU, single-core performance down to 1 GHz. so a CPU with a score with single-core score of 7000 running at 3500 MHz should have a normalized score of 2000/GHz. Do this both for overall single-core score, as well as per kernel (livermore loop).
3. Normalize every CPU, multi-core performance down to TDP levels, for every watt of TDP. For example: for a CPU X, that shows a 2000 multi-core score, and consumes 20 watts, it produces 100 multi-core score per watt. Do this both for overall score, as well as per kernel. Do this both for overall multi-core score, as well as per kernel (livermore loop).
4. Normalizing per 1 GHz for multi-core benchmark makes no sense, because modern CPUs have floating clock-rates, not fixed clocks, and they will throttle after a while, both desktops and laptops. TDP for a single-core is meanningless, because a single-core will not utilize a whole chip's TDP anyway, except for very ancient chips, that were designed as single-core.
5. ancient CPUs do have a fixed clock rates. Like Pentium 1/2/3/4/M. But they are typically single-core, and 32-bit. So easier to analyze those.
6. the script must be self contained, and not use any extra python modules from pip. Only the standard python library, that is included with python 3.
7. the script must work on both Windows, Mac and Linux systems.
8. the script must generate four HTML files as outputs, with 24 kernels each + overall score. 
8.1. HTML with single-core optimized results
8.2. HTML with multi-core optimized results
8.3. HTML with single-core non-optimized results
8.4. HTML with multi-core non-optimized results
9. The HTML files should build a table (spreadsheet), with color-coded columns.
Rows must be the CPU  name. Columns must be the normalized results (single-core / 1 GHz) and (Multi-core / TDP).
The color codes should be relative and go from red (worst case) to yellow and then to green (best case), one color per cell.
10. The columns must be separated into 2 parts via a some kind of vertical line or an empty cell :
Part A: overall score
Part B: per kernel score (1...24).
We will end up with 25 columns (overall score +24 kernels)  and about 200 rows (CPU models). Each cell must include both a human readable normalized value, black text font + background color code relative to other models, ranging from red to green. 
11. We are building a CPU architecture analysis tool, that can quickly show off strong and weak spots of every single CPU architecture that was ever benchmarked on the planet.
12. Color coded HTML is necessary for humans to be able to read it quickly. Humans are hardware coded to recognize color codes in a single cycle, split second, instead of reading 25 x 200 = 5000 values (or 5000 cycles or 5000 seconds).
13. HTML format must be very simple, static HTML4 style, so it is readable offline by ancient browsers, like Internet Explorer 6 and FireFox 1.0.

Do you have any major questions before you begin building the script ? Is the mission clear understood ?