Harnessing GPU Power Beyond Machine Learning: A Data Processing Experiment | December 13 2025, 01:16

Torturing my supercomputer. Illustration that the GPU is not just for machine learning and some complex math.

My script takes a thick English dictionary (Webster) and multiplies it by 30, creating a list of 12 million words. Then, the algorithm looks through all 12 million words and replaces all the vowels with asterisks using regex. To add more load, a “word length” column is added, and then we take words longer than 10 letters and find the most frequent (top 5).

So, in Python this is

df[‘masked’] = df[‘text’].str.replace(r'[aeiou]’, ‘*’, regex=True)

df[‘len’] = df[‘masked’].str.len()

res = df[df[‘len’] > 10][‘masked’].value_counts().head(5)

and this code is executed first through the main processor, then through a GPU.

The main processor (I have the top-tier Intel i9 285k) completes this task in 24 seconds, while the Nvidia RTX 5090 does it in 0.51 seconds. That’s a 46 times difference!

[Pandas CPU] Top Patterns:

masked

s*r w. sc*tt. 23280

s*r t. br*wn*. 23220

j*r. t*yl*r. 16140

bl*ckst*n*. 10860

b***. & fl. 10830

Name: count, dtype: int64

[Pandas CPU] Computation Time: 23.5596 sec.

Transferring data to GPU…

Transfer complete in 1.16s

— Running Benchmark: cuDF GPU —

[cuDF GPU] Top Patterns:

masked

s*r w. sc*tt. 23280

s*r t. br*wn*. 23220

j*r. t*yl*r. 16140

bl*ckst*n*. 10860

b***. & fl. 10830

Name: count, dtype: int64

[cuDF GPU] Computation Time: 0.5108 sec.

TOTAL SPEEDUP: 46.12x