Performance¶

The table below describes the environment the performance benchmarks were run in.

Component	Specification
CPU	Intel Core i9-12900H
CPU Frequency Scaling Governor	powersave
RAM	16 GiB DDR4
Kernel	Linux 6.1.0 (64-bit)
Operating System	Debian 12 “bookworm”
Operating System Libraries	GNU C Library 2.36, GNU C++ Library 12.2.0
Python Interpreter	CPython 3.11.2
Python Interpreter Libraries	Jupyter 1.1.1, pysorteddict 0.12.1

The key type chosen was float, since it is easy to generate floating-point numbers uniformly distributed in the unit interval. Comparing two floats is straightforward (as opposed to comparing, say, two strs—if their lengths are different, they may introduce noise in the benchmarks). Before every benchmark, the random number generator was seeded with π, a nothing-up-my-sleeve number.

There is an extra step required when using float keys: the check for NaN. Hence, the performance will be marginally worse than if int keys were used.

The performance benchmarking code is in a Jupyter notebook in the GitHub repository. It contains everything required to run the benchmarks.

Overview¶

The average execution times of some expressions are tabulated against the lengths of the sorted dictionaries used.

Expression	10²	10³	10⁴	10⁵	10⁶	10⁷
`0.00 in d`	36.8 ns	49.6 ns	65.0 ns	83.0 ns	93.2 ns	105 ns
`0.33 in d`	46.3 ns	60.5 ns	67.4 ns	82.9 ns	98.7 ns	112 ns
`0.67 in d`	40.4 ns	57.0 ns	67.6 ns	77.1 ns	97.2 ns	113 ns
`1.00 in d`	31.7 ns	58.9 ns	62.9 ns	82.3 ns	90.4 ns	111 ns
`set_del(d, keys_33)`	4.10 μs	5.08 μs	6.06 μs	6.96 μs	8.02 μs	9.89 μs
`set_del(d, keys_67)`	8.50 μs	10.3 μs	12.7 μs	15.9 μs	21.7 μs	31.5 μs
`set_del(d, keys_100)`	13.0 μs	16.1 μs	22.6 μs	30.2 μs	42.6 μs	62.8 μs
`for _ in d: pass`	857 ns	8.73 μs	132 μs	2.65 ms	117 ms	1.32 s
`for _ in reversed(d): pass`	1.12 μs	11.6 μs	160 μs	3.02 ms	119 ms	1.40 s

Details¶

Membership Check¶

The numbers 0.00, 0.33, 0.67 and 1.00 are spaced equally in the range spanned by the keys, but are absent in the sorted dictionaries constructed using the seeded random number generator described above. Hence, a search for them in any of those sorted dictionaries will not terminate permaturely.

Insertion and Deletion¶

Inserting or deleting an item into or from a sorted dictionary changes its length. Hence, benchmarks which only insert or only delete items cannot be said to have been performed on a sorted dictionary of a particular length. Therefore, the strategy chosen was:

generate a list of random floats;
insert all of them into the sorted dictionary; and
delete all of them from the sorted dictionary in order of insertion.

Only the last two steps (defined in a function set_del) were timed. After these, ideally, the sorted dictionary should return to the original state, allowing it to be used for the next round of timing. In practice, it is likely to be in a different state because of rebalancing operations. But that change of state can be assumed to simulate the real-world effects of insertions and deletions, so this is a sound strategy.

This benchmark was repeated for three different lengths of the list of random floats: 33, 67 and 100.

Iteration¶