fannkuch-redux benchmark | Computer Language Benchmarks Game

fannkuch-redux benchmark N=12

Each chart bar shows how many times slower, one ↓ fannkuch-redux program was, compared to the fastest program.

These are not the only programs that could be written. These are not the only compilers and interpreters. These are not the only programming languages.

Column × shows how many times more each program used compared to the benchmark program that used least.

×	Program Source Code	CPU secs	Elapsed secs	Memory KB	Code B	≈ CPU Load
		sort		sort	sort
1.0	C gcc #4	24.61	24.62	292	1183	1% 0% 0% 100%
1.0	C++ g++ #7	25.04	25.05	292	1150	1% 0% 0% 100%
1.6	Ada 2005 GNAT #3	39.29	39.32	1,652	2100	0% 1% 0% 100%
1.8	C gcc #3	44.77	44.79	288	567	0% 0% 0% 100%
2.0	C++ g++ #5	49.66	49.68	1,000	1440	0% 0% 0% 100%
2.1	C++ g++ #3	50.57	50.59	292	593	0% 0% 0% 100%
2.1	C gcc #2	50.77	50.79	332	1557	0% 0% 0% 100%
2.2	C++ g++	53.82	53.84	932	1059	0% 0% 0% 100%
2.3	Scala #2	56.56	56.58	20,472	1017	0% 1% 0% 100%
2.3	C gcc	56.93	56.96	292	508	0% 0% 0% 100%
2.3	Lisp SBCL #4	57.29	57.31	14,108	1518	0% 0% 0% 100%
2.3	ATS #2	57.80	57.82	288	1571	0% 0% 0% 100%
2.4	ATS	58.26	58.27	288	1306	0% 0% 0% 100%
2.5	Lisp SBCL #3	61.48	61.50	13,744	821	0% 0% 0% 100%
2.5	C++ g++ #4	61.86	61.89	1,004	1439	0% 0% 0% 100%
2.6	Haskell GHC #3	63.92	63.94	1,588	1153	0% 0% 0% 100%
2.7	Pascal Free Pascal	65.33	65.35	744	1018	0% 0% 0% 100%
2.8	Fortran Intel	68.30	68.32	260	590	0% 0% 0% 100%
2.8	C# Mono #2	69.18	69.19	13,548	564	0% 0% 0% 100%
2.8	Fortran Intel #3	69.69	69.71	7,580	1148	0% 0% 0% 100%
2.9	Java 7	71.26	71.28	13,936	1282	0% 0% 0% 100%
2.9	JavaScript V8 #3	71.57	71.60	5,508	539	1% 0% 0% 100%
3.0	OCaml #2	73.46	73.47	580	473	0% 0% 0% 100%
3.2	JavaScript V8 #2	78.42	78.46	5,484	472	0% 0% 0% 100%
3.2	Dart #2	78.75	78.77	6,264	538	0% 0% 0% 100%
3.4	Go	84.71	84.75	1,012	900	0% 0% 0% 100%
3.7	Haskell GHC #4	90.33	90.36	1,836	658	0% 0% 0% 100%
3.7	JavaScript V8	90.73	90.76	5,472	463	0% 0% 0% 100%
4.0	Scala	97.59	97.62	18,696	459	0% 1% 0% 100%
4.1	Lisp SBCL #2	101.92	101.95	3,828	513	0% 0% 0% 100%
4.2	Java 7 #2	102.74	102.77	14,420	514	0% 0% 0% 100%
4.2	OCaml #3	0.00	103.96	18,520	1017	0% 1% 0% 100%
4.6	F# Mono #2	113.62	113.64	15,148	548	0% 0% 0% 100%
4.7	C# Mono	116.60	116.62	13,520	520	0% 0% 0% 100%
5.4	OCaml #4	0.00	133.22	8,996	1004	0% 0% 0% 100%
5.4	C# Mono #3	134.00	134.03	14,340	1096	0% 0% 0% 100%
5.6	F# Mono #3	136.95	136.98	16,964	945	1% 0% 0% 100%
6.0	OCaml	146.98	147.01	576	524	0% 0% 0% 100%
11	Clojure #2	280.95	281.08	66,088	1088	0% 0% 0% 100%
12	Haskell GHC #2	289.09	289.19	3,160	808	0% 0% 0% 100%
13	Racket	5 min	5 min	15,332	649	0% 0% 0% 100%
13	F# Mono	5 min	5 min	15,152	551	0% 0% 0% 100%
15	Erlang HiPE	6 min	6 min	7,644	1038	0% 0% 0% 100%
22	Haskell GHC	9 min	9 min	3,164	553	0% 0% 0% 100%
24	Smalltalk VisualWorks	10 min	10 min	21,952	838	0% 0% 0% 100%
38	Ruby JRuby	15 min	15 min	583,116	384	0% 0% 0% 100%
57	Dart	23 min	23 min	5,980	531	0% 0% 0% 100%
87	Lua	35 min	35 min	820	462	0% 0% 0% 100%
99	Perl	40 min	40 min	1,464	457	0% 0% 0% 100%
110	PHP #2	45 min	45 min	2,476	441	0% 0% 0% 100%
117	PHP #3	47 min	47 min	10,132	1150	0% 0% 0% 100%
130	Python 3 #6	48 min	53 min	4,564	385	0% 0% 0% 100%
145	PHP	59 min	59 min	2,508	482	0% 0% 0% 100%
163	Ruby 2.0	1h 06 min	1h 06 min	4,864	384	0% 0% 0% 100%
434	C CINT	2h 57 min	2h 57 min	5,116	536	0% 0% 0% 100%
	Python 3 #2	Timed Out	1h 00 min		797
	Python 3	Timed Out	1h 00 min		1108
"wrong" (different) algorithm / less comparable programs
1.2	C++ g++ #2	29.11	29.13	988	2106
1.6	C++ g++ #6	38.22	38.24	292	894
1.7	Java 7 #3	41.34	41.35	15,688	1633
1.9	Lisp SBCL	46.66	46.68	18,528	1607

fannkuch-redux benchmark : Indexed-access to tiny integer-sequence

diff program output N = 7 with this output file to check your program is correct before contributing.

We are trying to show the performance of various programming language implementations - so we ask that contributed programs not only give the correct result, but also use the same algorithm to calculate that result.

For N = 7 programs should generate these permutations (40KB) - which, incidentally, seem to be in the same order as permutations generated by the Tompkins-Paige algorithm, see pages 150-151 Permutation Generation Methods Robert Sedgewick.

The fannkuch benchmark is defined by programs in Performing Lisp Analysis of the FANNKUCH Benchmark, Kenneth R. Anderson and Duane Rettig.

Each program should

Take a permutation of {1,...,n}, for example: {4,2,1,5,3}.
Take the first element, here 4, and reverse the order of the first 4 elements: {5,1,2,4,3}.
Repeat this until the first element is a 1, so flipping won't change anything more: {3,4,2,1,5}, {2,4,3,1,5}, {4,2,3,1,5}, {1,3,2,4,5}.
Count the number of flips, here 5.
Keep a checksum
- checksum = checksum + (if permutation_index is even then flips_count else -flips_count)
- checksum = checksum + (toggle_sign_-1_1 * flips_count)
Do this for all n! permutations, and record the maximum number of flips needed for any permutation.

The conjecture is that this maximum count is approximated by n*log(n) when n goes to infinity.

FANNKUCH is an abbreviation for the German word Pfannkuchen, or pancakes, in analogy to flipping pancakes.

Thanks to Oleg Mazurov for insisting on a checksum and providing this helpful description of the approach he took -

A common idea for parallel implementation is to divide all work (n! permutations) into chunks small enough to avoid load imbalance but large enough to keep overhead low. I set the number of chunks as a parameter (NCHUNKS = 150) from which I derive the size of a chunk (CHUNKSZ) and the actual number of chunks/tasks to be processed (NTASKS), which may be different from NCHUNKS because of rounding.
Task scheduling is trivial: threads will atomically get and increment the taskId variable to derive a range of permutation indices to work on:
```
    task = taskId.getAndIncrement();
    idxMin = task * CHUNKSZ;
    idxMax = min( idxMin + CHUNKSZ, n! );
```
Maximum flip counts and partial checksums can be computed for chunks in arbitrary order and recombined to generate the required result at the final step (CHUNKSZ must be even for adding partial checksums to be associative - I didn't enforce it in my submission).
Now I need to go from a permutation index to the permutation itself.
The predefined order in which all permutations are to be generated can be described as follows: to generate n! permutations of n arbitrary numbers, rotate the numbers left (from higher position to lower) n times, so that each number appears in the n-th position, and for each rotation recursively generate (n-1)! permutations of the first n-1 numbers whatever they are.
To optimize the process I use an intermediate data structure, count[], which keeps count of how many rotations have been done at every level. Apparently, count[0] is always 0, as there is only one element at that level, which can't be rotated; count[1] = 0..1 for two elements, count[2] = 0..2 for three elements, etc.
To generate next permutation I swap the first two elements and increase count[1]. If count[1] becomes greater than 1, I'm done with rotations at level 1 and need to "return" (as it would have been in the recursive implementation) to level 2. Now, I rotate 3 elements and increment count[2]. If it becomes greater than 2, I'm done with level 2 and need to go to level 3, etc.
It should be clear now how to generate a permutation and corresponding count[] array from an arbitrary index. Basically, count[k] = ( index % (k+1)! ) / k! is the number of rotations we need to perform on elements 0..k. Doing it in the descending order from n-1 to 1 gives us both the count[] array and the permutation.

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fannkuch-redux benchmark N=12

fannkuch-redux benchmark : Indexed-access to tiny integer-sequence

Ubuntu : Intel® Q6600® one core
Computer Language Benchmarks Game [[ Play ]]