
8 8888888888    8 8888     ,o888888o.    8 8888888888   b.             8 8888888 8888888888 8 888888888o.    8 8888 `8.`8888.      ,8' 
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8 8888          8 8888 ,8 8888       `8. 8 8888         Y88888o.       8       8 8888       8 8888     `88   8 8888   `8.`8888.  ,8'   
8 8888          8 8888 88 8888           8 8888         .`Y888888o.    8       8 8888       8 8888     ,88   8 8888    `8.`8888.,8'    
8 888888888888  8 8888 88 8888           8 888888888888 8o. `Y888888o. 8       8 8888       8 8888.   ,88'   8 8888     `8.`88888'     
8 8888          8 8888 88 8888           8 8888         8`Y8o. `Y88888o8       8 8888       8 888888888P'    8 8888     .88.`8888.     
8 8888          8 8888 88 8888   8888888 8 8888         8   `Y8o. `Y8888       8 8888       8 8888`8b        8 8888    .8'`8.`8888.    
8 8888          8 8888 `8 8888       .8' 8 8888         8      `Y8o. `Y8       8 8888       8 8888 `8b.      8 8888   .8'  `8.`8888.   
8 8888          8 8888    8888     ,88'  8 8888         8         `Y8o.`       8 8888       8 8888   `8b.    8 8888  .8'    `8.`8888.  
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i7-5775C

3.3 GHz

Load AVG:

0.45

0.55

0.41

root

44k

226 GiB

IO%

Used: 39%

88.8 GiB

Free: 61%

137 GiB

Swap

Used: 48%

2.37 GiB

Free: 52%

2.56 GiB

Media2

IO%

Used: 48%

2.67 GiB

Free: 52%

2.56 GiB

Status:

Sleeping

Elapsed:

00:01:23

IO/R:

20.2 kiB

IO/W:

4.00 kiB

Memory: 0.0%

..........

::::::

4.68 MiB

CMD

./bin/eigentrix

Download

1.30 Kib/s

Tops:

Total:

346 Bytes/s

Tops:

Total:

(10.4 Kibps)

(152 Mibps)

896 GiB

(2.70 Kibps)

(1.59 Mibps)

486 GiB

Upload

Pid:

1186798

999834

474394

3144881

1639

1211129

1186800

3145432

1186781

3145189

1210131

7378

1000249

7266

3145058

2123456

7654321

9876543

4567890

8765432

6543210

5432109

Program:

mpv

node

python3

firefox

Xorg

tracker-extract

java

Web Content

python3

Web Content

btop

compiz

node

terminator

Web Content

chrome

vlc

emacs

gedit

htop

bash

nano

Command:

mpv --geometry=1462

/home/gnm/.vscode-s

python3 /home/gnm/b

/usr/lib/firefox/fi

/usr/lib/xorg/Xorg

/usr/libexec/tracke

java -Dswing.default

/usr/lib/firefox/fi

/usr/bin/python3 /h

/usr/lib/firefox/fi

./bin/btop

compiz

/home/gnm/.vscode-s

/usr/bin/python3 /u

/usr/lib/firefox/fi

/usr/lib/chrome/ch

vlc /media/videos

emacs /usr/share

gedit /tmp/docs

htop

/bin/bash

nano /etc/hosts

Thread:

140

User:

gnm

root

gnm

guest

admin

testuser

root

user123

developer

system

MemB:

547M

308M

796M

757M

79M

41M

621M

206M

76M

314M

114M

585M

56M

234M

129M

87M

343M

220M

432M

300M

98M

Cpu%:

......19.0

......18.5

......0.5

......1.0

......2.5

......0.5

......0.0

......1.0

......0.0

......5.5

......0.5

......2.0

......4.0

......3.0

......6.0

......1.5

......8.5

......7.0

eigentrix@dev:root/home/aboutus$ls

Predict GPU failure with 95% accuracy at least four days before they occur

Monitor GPU performance trends and anomalies to gain early insights into potential risks before they escalate
Leverage data trends to make informed decisions about system performance and manage hardware lifecycles more effectively
Detect early warning signs to better manage GPU health, optimizing usage and potentially extending its lifespan

Expand the lifespan of the GPU card by up to 20% by helping manage the way it deals with the load

Adjust GPU workload distribution dynamically to prevent overloading, ensuring balanced usage and reducing wear on components
Implement strategies to regulate temperature fluctuations, preventing overheating and prolonging the GPU's overall health
Identify periods of excessive resource consumption and adjust performance settings, preventing unnecessary strain and enhancing long-term durability

Optimise load management to improve task completion time by an average of 25%

Adjust the distribution of workloads based on task priority and complexity, ensuring critical tasks are completed faster while maintaining efficiency
Fine-tune resource usage to allocate the right amount of processing power for each task, reducing bottlenecks and improving overall performance
Continuously monitor and adjust workloads in real-time, ensuring optimal distribution across available resources and reducing idle times for quicker task completion


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╠════════════════════════════════════════════════════════════════════════════════════════╣
║       ❄️❄️❄️  ██████████████████████████████ GPU █████████████████████████████  ❄️❄️❄️       ║
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║      ⚡██████████████████████████████████████████████████████████████████████████⚡      ║
╚════════════════════════════════════════════════════════════════════════════════════════╝


                                                                                                                                                               
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