Markov text generation

Train a bigram Markov model on a 1 MiB corpus and emit 10,000 words.

Runtime · median per inner-loop window

median of 10 runs

Native Gocompiled

5.42 ms0.23×

Piko interpbytecode VM

23.7 msbaseline

CPython 3.13bytecode VM

27.2 ms1.14×

PyPy 7.3tracing JIT

9.49 ms0.40×

Ttengobytecode VM

62.8 ms2.65×

Sscriggobytecode VM

56.2 ms2.37×

Mmvmbytecode VM

62.7 ms2.64×

YyaegiAST walker

58.3 ms2.46×

Runner	N	Compile	Runtime	P95	Stddev	RSS	vs piko	Status
Native Gocompiled	10	181 ms	5.42 ms	6.07 ms	456 µs	68 MiB	0.23×	OK
Piko interpbytecode VM	10	1.05 ms	23.7 ms	24.7 ms	355 µs	255 MiB	1.00×	OK
CPython 3.13bytecode VM	10	354 µs	27.2 ms	27.6 ms	496 µs	n/a	1.14×	OK
PyPy 7.3tracing JIT	10	314 µs	9.49 ms	9.77 ms	128 µs	n/a	0.40×	OK
tengobytecode VM	10	239 µs	62.8 ms	72.2 ms	6.63 ms	451 MiB	2.65×	OK
scriggobytecode VM	10	413 µs	56.2 ms	74.4 ms	7.09 ms	428 MiB	2.37×	OK
mvmbytecode VM	10	334 µs	62.7 ms	79.5 ms	6.14 ms	66 MiB	2.64×	OK
yaegiAST walker	10	374 µs	58.3 ms	67.0 ms	9.31 ms	66 MiB	2.46×	OK

Workload & symmetry rules

Workload

Build a bigram model from a 1 MiB lorem corpus. Sample 10,000 tokens from a deterministic LCG-seeded RNG. Print a fingerprint of the output.

Source code