The most commonly used binary search variant was first published by Hermann Bottenbruch in 1962 and hasn't notably changed since. Below I'll describe several novel variants with improved performance. The most notable variant, the monobound binary search, executes two to four times faster than the standard binary search on arrays smaller than 1 million 32 bit integers.

A source code implementation in C is available in the binary_search.c file which also contains a bench marking routine. A graph with performance results is included at the bottom of this page. Keep in mind performance will vary depending on hardware and compiler optimizations.

I'll briefly describe each variant and notable optimizations below, followed by some performance graphs.

Deferred Detection of Equality

By skipping the detection of equality until the binary search has finished (which does not allow for early termination) each loop contains 1 key check, 1 integer check and 2 integer assignments. This is pretty much the standard algorithm that has been used since 1962.

Pointer Optimizations

You can get another 10% performance boost by using pointer operations. I forgo such optimizations in the C implementation to keep things as readable as possible.

Unsigned Integer Optimization

You can get a further performance boost by using unsigned instead of signed integers.

Stability

All the implementations in binary_search.c should be stable. If you search an array containing the elements [1][4][7][7][7][9] and you search for the number 7, it should return the right most index. This is needed if you want to use a binary search in a stable sorting algorithm. The binary search being stable shouldn't notably slow down performance.

Zero length array

All the implementations in binary_search.c should correctly handle the case where the search function is called with 0 as the array length.

Compilation

For the monobound binary search variant to perform well the source code must be compiled with the -O1, -O2, or -O3 optimization flag.

Boundless Binary Search

The boundless binary search is faster than the standard binary search since the loop contains 1 key check, 1 integer check, and (on average) 1.5 integer assignments. The performance gain will vary depending on various factors, but should be around 20% when comparing 32 bit integers.

Doubletapped Binary Search

When you get to the end of a binary search and there are 2 elements left it takes exactly 2 if checks to finish. By doing two equality checks at the end you can finish up in either 1 or 2 if checks. Subsequently, on average, the doubletapped binary search performs slightly fewer key checks.

Monobound Binary Search

The monobound binary search is similar to the boundless binary search but uses an extra variable to simplify calculations and performs slightly more keychecks. It's up to 60% faster than the standard binary search when comparing 32 bit integers. On small arrays the performance difference is even greater.

The performance gain is due to dynamic loop unrolling, which the traditional binary search (by trying to minimize the number of key checks) does not allow. Loop unrolling in turn allows various other potential optimizations at the compiler and cpu level.

Tripletapped Binary Search

When you get to the end of a binary search and there are 3 elements left it takes 2.5 if checks to finish. The monobound binary search, however, takes 3 if checks. Subsequently the tripletapped variant performs 3 equality checks at the end with early termination, resulting in slightly fewer key checks and if the data aligns properly, slightly improved performance.

Quaternary Binary Search

The dynamic unrolling of loops is often limited to 16 iterations. By narrowing down the search range by a fourth each loop, instead of a half, it takes 16 iteriations to search 4294967296 elements, instead of 65536. This optimizations slows things down slightly for smaller arrays, but can give a notable gain on larger arrays.

Monobound Interpolated Binary Search

When you have an even distribution you can make an educated guess as to the location of the index. Due to the expense of the initial check and exponential search, the interpolated binary search is unlikely to outperform other binary searches on arrays with less than 1000 elements. When the distribution is uneven performance will drop, but not significantly.

A practical application for an interpolated binary search would be looking up authorization keys.

Adaptive Binary Search

The adaptive binary search is optimized for repeated binary searches on the same array. When it observes a pattern it switches from a binary search to an exponential search. Unlike the interpolated search the adaptive search works on uneven distributions as well.

A practical application for an adaptive binary search would be accessing a unicode lookup table.

Small array benchmark graph

The following benchmark was on WSL 2 gcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04). The source code was compiled using gcc -O3 binary-search.c. Each test was ran 1,000 times with the time (in seconds) reported of the best run.

The graph below shows the execution speed on arrays with 1, 2, 4, 8, 16, 32, 64, and 128 elements on an Intel i3 quad-core processor.

data table

Name	Items	Hits	Misses	Checks	Time
linear_search	1	806	9194	10000	0.000029
standard_binary_search	1	806	9194	10000	0.000031
monobound_binary_search	1	806	9194	10000	0.000033
linear_search	2	1034	8966	19495	0.000039
standard_binary_search	2	1034	8966	20000	0.000074
monobound_binary_search	2	1034	8966	20000	0.000036
linear_search	4	775	9225	38862	0.000046
standard_binary_search	4	775	9225	30000	0.000122
monobound_binary_search	4	775	9225	30000	0.000041
linear_search	8	822	9178	77133	0.000064
standard_binary_search	8	822	9178	40000	0.000177
monobound_binary_search	8	822	9178	40000	0.000050
linear_search	16	1141	8859	151154	0.000116
standard_binary_search	16	1141	8859	50000	0.000219
monobound_binary_search	16	1141	8859	50000	0.000064
linear_search	32	1145	8855	302324	0.000218
standard_binary_search	32	1145	8855	60000	0.000270
monobound_binary_search	32	1145	8855	60000	0.000074
linear_search	64	1096	8904	605248	0.000409
standard_binary_search	64	1096	8904	70000	0.000321
monobound_binary_search	64	1096	8904	70000	0.000084
linear_search	128	1046	8954	1214120	0.000749
standard_binary_search	128	1046	8954	80000	0.000386
monobound_binary_search	128	1046	8954	80000	0.000097

Large array benchmark graph

The following benchmark was on WSL 2 gcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04). The source code was compiled using gcc -O3 binary-search.c. Each test was ran 10,000 times with the time (in seconds) reported of the best run.

The graph below shows the execution speed on arrays with 10, 100, 1000, 10000, 100000, and 1000000 elements on an Intel i3 quad-core processor.

data table

Name	Items	Hits	Misses	Checks	Time
standard_binary_search	10	910	9090	43646	0.000182
boundless_binary_search	10	910	9090	43646	0.000156
monobound_binary_search	10	910	9090	50000	0.000060
monobound_interpolated_search	10	910	9090	64027	0.000203
standard_binary_search	100	1047	8953	77085	0.000361
boundless_binary_search	100	1047	8953	77085	0.000292
monobound_binary_search	100	1047	8953	80000	0.000096
monobound_interpolated_search	100	1047	8953	92421	0.000234
standard_binary_search	1000	1041	8959	109808	0.000610
boundless_binary_search	1000	1041	8959	109808	0.000489
monobound_binary_search	1000	1041	8959	110000	0.000137
monobound_interpolated_search	1000	1041	8959	108509	0.000147
standard_binary_search	10000	1024	8976	143580	0.000804
boundless_binary_search	10000	1024	8976	143580	0.000651
monobound_binary_search	10000	1024	8976	150000	0.000204
monobound_interpolated_search	10000	1024	8976	109353	0.000202
standard_binary_search	100000	1040	8960	176860	0.001087
boundless_binary_search	100000	1040	8960	176860	0.000903
monobound_binary_search	100000	1040	8960	180000	0.000360
monobound_interpolated_search	100000	1040	8960	123144	0.000290
standard_binary_search	1000000	993	9007	209529	0.001570
boundless_binary_search	1000000	993	9007	209529	0.001369
monobound_binary_search	1000000	993	9007	210000	0.000691
monobound_interpolated_search	1000000	993	9007	124870	0.000374

monobound_bsearch() vs bsearch()

The following benchmark was on WSL 2 gcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04). The source code was compiled using gcc -O3 monobound_bsearch.c. Each test was ran 1,000 times with the time (in seconds) reported of the best run.

The graph below shows the execution speed on arrays with 10, 100, 1K, 10K, 100K, 1M, and 10M elements on an Intel i3 quad-core processor. The bsearch function is the one provided by stdlib.h.

data table

Name	Items	Hits	Misses	Checks	Time
monobound	10	930	9070	48149	0.000136
bsearch	10	930	9070	34677	0.000202
monobound	100	1103	8897	77539	0.000189
bsearch	100	1103	8897	66470	0.000410
monobound	1000	1033	8967	107845	0.000265
bsearch	1000	1033	8967	98703	0.000623
monobound	10000	1033	8967	147232	0.000357
bsearch	10000	1033	8967	132342	0.000820
monobound	100000	1014	8986	177576	0.000539
bsearch	100000	1014	8986	165785	0.001111
monobound	1000000	998	9002	207938	0.001124
bsearch	1000000	998	9002	198443	0.001603
monobound	10000000	974	9026	247324	0.002641
bsearch	10000000	974	9026	232174	0.003784