Random Generation and Lookup Table Benchmarks

Benchmarks for random byte generation and lookup table operations across Rust and Python implementations.

Overview

Some of the most common operations in data processing are random generation and lookup tables. That's true not only for strings but for any data type, and StringZilla has been extensively used in Image Processing and Bioinformatics for those purposes.

Random Byte Generation

Intel Xeon4 Sapphire Rapids

Library	Short Words	Long Lines
Rust
getrandom::fill	0.03 GB/s	0.46 GB/s
rand_chacha::ChaCha20Rng	0.06 GB/s	2.00 GB/s
rand_xoshiro::Xoshiro128Plus	0.40 GB/s	4.03 GB/s
zeroize::zeroize	0.46 GB/s	4.98 GB/s
stringzilla::fill_random	1.01 GB/s	8.58 GB/s
Python
numpy.PCG64	0.01 GB/s	1.87 GB/s
numpy.Philox	0.01 GB/s	1.45 GB/s
pycryptodome.AES-CTR	0.01 GB/s	0.37 GB/s
stringzilla.random	0.11 GB/s	18.46 GB/s

Measured June 17, 2026.

Lookup Tables

Performing in-place lookups in a precomputed table of 256 bytes:

Intel Xeon4 Sapphire Rapids

Library	Short Words	Long Lines
Rust
serial code	0.47 GB/s	4.06 GB/s
stringzilla::lookup_inplace	0.42 GB/s	10.22 GB/s
Python
bytes.translate<new>	0.12 GB/s	2.68 GB/s
numpy.take<new>	0.01 GB/s	0.86 GB/s
opencv.LUT<new>	0.01 GB/s	2.00 GB/s
opencv.LUT<inplace>	0.01 GB/s	2.16 GB/s
stringzilla.translate<new>	0.09 GB/s	7.94 GB/s
stringzilla.translate<inplace>	0.07 GB/s	8.02 GB/s

Measured June 17, 2026.

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See README.md for dataset information and replication instructions.