Files
data_mining_algorithms/docs/Benchmarking.md

86 lines
1.5 KiB
Markdown

# 10000/50/25 Benchmark
## One Core Utilisation
randomi.py
### Conrads Setup (HDD)
53.5 sec
### Conrads Setup (m.2 SSD)
63.29 sec
### Tillmanns Setup (HDD)
82.29 sec
## 4 Core Utilisation
randomi4c.py
### Conrads Setup (HDD)
17.71 sec
### Conrads Setup (m.2 SSD)
16.33 sec
## 8 Core Utilisation
randomiUnLi.py
### Conrads Setup (HDD)
12.58 sec
### Conrads Setup (m.2 SSD)
12.32 sec
## One Core Utilisation (Mk II)
randomi.py (Mk II)
### Conrads Setup (HDD)
54.45 sec
### 8 Core Utilisation (Mk II)
randomi.py (Mk II)
### Conrads Setup (HDD)
11.57 sec
### Tillmanns Setup (HDD)
16.30 sec
### 16 Core Utilisation (Mk II)
randomi.py (Mk II)
### Tillmanns Setup (HDD)
14.98 sec
### 16 Core Utilisation & Win Defender Folder exception(Mk II)
randomi.py (Mk II)
### Tillmanns Setup (RAMDISK)
7.47 sec
### Tillmanns Setup (HDD)
8.60 sec
## 32 Thread Utilisation & Win Defender Folder exception(Mk II)
randomi.py (Mk II)
### Tillmanns Setup (RAMDISK)
7.54 sec
### Tillmanns Setup (HDD)
7.77 sec
## Conclusions
- The limit for generating this benchmark seems to be at around 7.50 sec with the bottleneck being the CPU and the I/O system
- The generator randomI.py is pretty much as good as it needs to be at generating 10.000 entries in just about 8 seconds
- Further improvements could be:
- Generating string values
- Generating realistic data like adresses, names, phone numbers (...)
- Exporting the data into an SQL database instead of files