ref: ea0adff840477394d0be542d80c491b5cd021754 go-yenc/testdata/benchmarks/README.md -rw-r--r-- 2.5 KiB
ea0adff8 — Moritz Poldrack added patch for pointer implementation 1 year, 9 days ago


This is the lab. Here, various algorithms are competing for the crown of highest encoding speed.

The benchmarks are performed using Go's integrated benchmarks and Hyperfine. The categories are: raw speed and data throughput.


  • naive implementation
  • Lookup Table
    • Slice containing struct
    • Hashmap with byte-key
  • Bootleg SIMD (do it with a 32/64-bit integer and split it up)

#not yet participating

  • Bitwise Operations
  • SIMD
  • io.Writer implementation

#Raw Speed


Raw speed is calculated by running the benchmark 100 times and taking the average. This is done to account for variations in CPU Usage as this test is completed pretty quick.

Algorithm ns/Op Escaped ns/Op Unescaped ns/Op (exp. avg.)¹ nth fastest
naive 2.40 2.39 2.39 1
lookup-table 2.51 2.51 2.51 2
hashmap 21.05 20.99 20.99 4
bootleg-simd 13.95 8.48 8.57 3

¹) assuming random distribution of bytes and that 4/256 bytes have to be escaped.

#Data Throughput


Data Throughput is calculated by running the encoding function on a set of randomly generated data which is compiled into the program.

Algorithm Duration Byte Throughput nth fastest Speed relative to naive
naive 3.933 1073741824 260.36 MiB/s 2 1.00
lookup-table 3.300 1073741824 310.30 MiB/s 1 1.19
hashmap 35.236 1073741824 29.0612 MiB/s 4 0.11
bootleg-simd 19.144 1073741824 53.4893 MiB/s 3 0.21

Variations in speed may be due to changes in the input dataset and fluctuations in computer activity.

#additional notes

To ensure maximum comparability, all fields are updated every time the benchmark is run. This way they should give an estimate even when run on a different system.