meteor-contest benchmark N=2,098

Each chart bar shows how many times slower, one ↓ meteor-contest 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.

    sort sortsort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0PyPy 2 #2 0.870.8884,6441485  6% 100% 5% 1%
1.0PyPy 2 #2 0.890.9084,2441485  9% 0% 100% 2%
1.0PyPy 2 #2 0.880.9084,6961485  8% 5% 8% 99%
1.1PyPy 2 0.950.9584,5521579  8% 2% 100% 1%
1.1PyPy 2 0.940.9684,3961579  36% 27% 4% 45%
1.1PyPy 3 0.980.9976,5481540  9% 6% 7% 100%
1.1PyPy 3 0.990.9976,9561540  8% 8% 7% 100%
1.1PyPy 2 0.931.0084,5761579  98% 4% 2% 4%
1.1PyPy 3 #2 0.981.0076,2281443  10% 6% 12% 98%
1.2PyPy 3 1.001.0176,4201540  9% 7% 100% 6%
1.2PyPy 3 #2 0.991.0176,4801443  46% 7% 64% 6%
1.2PyPy 3 #2 1.031.0576,2841443  17% 11% 98% 13%
2.0Pyston #2 1.741.7430,1561485  0% 0% 99% 0%
2.0Pyston #2 1.741.7428,0161485  100% 0% 1% 0%
2.0Pyston #2 1.741.7429,9721485  3% 98% 0% 0%
2.3Pyston 2.052.0530,0041579  100% 0% 0% 0%
2.3Pyston 2.052.0530,0601579  0% 100% 0% 0%
2.3Pyston 2.062.0630,0121579  0% 0% 2% 98%
2.4PyPy 2 #3 2.122.1483,2721354  6% 99% 3% 1%
2.5PyPy 2 #3 2.162.1882,9521354  9% 5% 3% 100%
2.6PyPy 2 #3 2.212.2482,9321354  15% 13% 15% 95%
2.6PyPy 3 #3 2.212.2675,2321334  28% 4% 77% 2%
2.6PyPy 3 #3 2.192.3175,1241334  100% 5% 3% 3%
2.7PyPy 3 #3 2.322.3575,1121334  9% 6% 100% 3%
3.0Python 2 #2 2.612.627,1321485  1% 0% 1% 100%
3.0Nuitka #2 2.632.6310,2161443  100% 7% 1% 1%
3.0Python 2 #2 2.642.657,1561485  3% 100% 1% 2%
3.0Python 2 #2 2.642.657,0921485  77% 2% 25% 1%
3.1Nuitka #2 2.692.6910,3041443  43% 8% 59% 2%
3.1Nuitka #2 2.752.769,9401443  8% 12% 17% 89%
3.5Nuitka 3.103.1110,0241540  2% 6% 100% 5%
3.6Nuitka 3.123.1310,0841540  4% 6% 1% 100%
3.6Python development version #2 3.193.209,0241443  1% 100% 4% 1%
3.6Python development version #2 3.203.209,0241443  11% 95% 3% 1%
3.7Python development version #2 3.213.228,9881443  1% 1% 0% 100%
3.7Nuitka 3.133.279,9241540  4% 99% 2% 2%
3.8Python 3 #2 3.313.328,8201443  6% 4% 4% 99%
3.8Python 3 #2 3.323.328,8161443  7% 6% 3% 100%
3.9Python 3 #2 3.403.468,8521443  44% 5% 64% 4%
4.3Cython #3 3.763.7711,2041334  13% 100% 9% 13%
4.3Python 2 3.793.807,0521579  14% 1% 87% 4%
4.3Cython #3 3.793.8011,1121334  12% 10% 100% 8%
4.3Python 2 3.803.807,0761579  8% 2% 93% 2%
4.4Python 2 3.853.866,9161579  3% 1% 100% 1%
4.5Cython #3 3.933.9411,0401334  22% 18% 17% 100%
4.7Nuitka #3 4.134.1412,1001334  1% 9% 100% 2%
4.8Python development version 4.204.218,7721540  1% 0% 1% 100%
4.8Nuitka #3 4.214.2211,9121334  5% 10% 100% 9%
4.8Pyston #3 4.234.2330,4481354  0% 0% 0% 100%
4.8Pyston #3 4.234.2330,3201354  1% 99% 0% 0%
4.8Pyston #3 4.234.2330,2761354  0% 0% 100% 0%
4.9Python development version 4.264.268,9441540  2% 1% 100% 0%
4.9Python development version 4.294.308,9241540  2% 0% 100% 1%
4.9Nuitka #3 4.124.3112,1001334  1% 100% 3% 1%
4.9Python 2 #3 4.304.317,6961354  0% 1% 100% 1%
4.9Python 3 4.334.338,9121540  7% 2% 2% 100%
5.0Python 2 #3 4.364.367,5401354  1% 1% 1% 100%
5.0Python 2 #3 4.354.377,5321354  2% 100% 2% 1%
5.0Python 3 4.394.408,8441540  7% 4% 4% 100%
5.1Python 3 4.374.478,9001540  54% 4% 6% 55%
5.3Python development version #3 4.634.6311,0561334  3% 2% 100% 1%
5.3Python development version #3 4.664.6611,0681334  3% 1% 100% 1%
5.3Python development version #3 4.674.6811,0121334  3% 1% 100% 1%
5.6Python 3 #3 4.804.9010,9601334  48% 11% 16% 52%
5.7Python 3 #3 4.984.9911,1121334  14% 13% 8% 100%
5.8Python 3 #3 4.885.1311,1401334  100% 7% 7% 5%
8.5Jython #2 12.517.41307,5761485  25% 40% 49% 55%
8.6Jython #2 12.667.55311,7961485  42% 41% 36% 48%
8.8Jython #3 12.957.69303,3281354  35% 62% 34% 37%
8.8Jython #2 13.337.70319,3601485  51% 40% 36% 46%
8.9Jython #3 13.537.82308,0281354  33% 48% 64% 28%
9.4Jython #3 13.558.20314,7721354  47% 29% 60% 29%
10Jython 15.039.07312,0961579  61% 33% 39% 33%
10Jython 15.389.08309,5681579  34% 46% 39% 51%
10Jython 15.239.16315,1881579  28% 47% 49% 43%
54Graal #2 110.7547.07756,3441443  32% 68% 80% 71%
56Graal #2 115.0148.86802,4601443  32% 75% 76% 70%
59Graal #2 119.6451.55855,4041443  42% 74% 79% 68%
82Graal 161.0171.481,541,5561540  35% 63% 74% 70%
missing benchmark programs
IronPython No program
Shedskin No program
Numba No program
MicroPython No program
Grumpy No program

 meteor-contest benchmark : Search for solutions to shape packing puzzle

This is a contest - different algorithms may be used.

You are expected to diff the output from your program N = 2098 against this output file before you contribute your program.

The Meteor Puzzle board is made up of 10 rows of 5 hexagonal Cells. There are 10 puzzle pieces to be placed on the board, we'll number them 0 to 9. Each puzzle piece is made up of 5 hexagonal Cells. As different algorithms may be used to generate the puzzle solutions, we require that the solutions be printed in a standard order and format. Here's one approach - working along each row left to right, and down the board from top to bottom, take the number of the piece placed in each cell on the board, and create a string from all 50 numbers, for example the smallest puzzle solution would be represented by

00001222012661126155865558633348893448934747977799

Print the smallest and largest Meteor Puzzle 50 character solution string in this format to mimic the hexagonal puzzle board:

0 0 0 0 1 
 2 2 2 0 1 
2 6 6 1 1 
 2 6 1 5 5 
8 6 5 5 5 
 8 6 3 3 3 
4 8 8 9 3 
 4 4 8 9 3 
4 7 4 7 9 
 7 7 7 9 9 

The command line parameter N should limit how many solutions will be found before the program halts, so that you can work with just a few solutions to debug and optimize your program.

Diff program output N = 2098 against the output file to check the format is correct.

The Meteor Puzzle and 3 Java puzzle solvers are described in "Optimize your Java application's performance" (pdf).

Revised BSD license

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