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N-Queen Problem: CPython 2.6.5 vs PyPy 1.5.0

May 29, 2011

It is amazing how small and compress a Python code can be for solving 8-Queen problem. Using Python’s itertools to compute permutations and yield keyword for generators, all N-Queen problem comes to mere 8 lines of code:

def hit(p):
    for i in range(len(p) - 1):
        for j in range(i + 1, len(p)):
            if j - i == abs(p[i] - p[j]): return True
    return False

def n_queen(n):
    for p in itertools.permutations(range(n)):
        if not hit(p): yield p

One may add just a bit more code to print the board like this:

base_char = ord('A')

def print_board(s):
    end = len(s)
    board = ['   '.join([str(i) for i in range(1, end + 1)]) + '\n']
    for i, r in enumerate(s):
        board += "%s: %s * %s\n" % (chr(base_char+i), (' - ' * r), (' - ' * (end - r - 1)))
    return ''.join(board)

def solve(n):
    count = 0
    for s in n_queen(n):
        count += 1
        print "==== solution %d =====" % count
        print s
        print print_board(s)
    return count

I used this code snippet to compare Python 2.6.5 with PyPy 1.5.0 JIT. It is interesting that for this CPU intensive problem, Python does not use all 100% CPU in User space and only about 30% of CPU usage is spent in User space and the rest 70% for System and Nice while PyPy dedicated most of CPU cycles to User CPU and hence finished up much faster.

Here are my results in an old single core 1.7G Pentium M processor. I expect PyPy JIT to do even better in a modern SSE-2 multi-core processor.

# board count cpython2.6.5 pypy1.5
5 10 0.012 0.007
6 4 0.048 0.064
7 40 0.327 0.224
8 92 1.556 0.562
9 352 15.207 3.685
10 724 156.736 37.676
11 2680 1723.404 427.216

Bellow is the chart comparing the performance upon above benchmark:

Next I tried to speed up the code by making a minor change in hit() method. To prevent len(p) call I passed the length as argument and replaced abs() call with a check for both positive and negative results. These two simple changes as bellow made the code almost 3 times faster:

def hit(p, n):
    for i in range(n - 1):
        for j in range(i + 1, n):
            d = p[i] - p[j]
            if j - i == d or i - j == d: return True
    return False

def n_queen(n):
    for p in itertools.permutations(range(n)):
        if not hit(p, n): yield p

I was expecting the PyPy fail to speed up the execution times as it did for basic code but amazingly the second code’s speed up is even better in PyPy.

Update 31 May 2011 :
Just one update which seemed necessary, back to some comments to this post and also in YCombinator news forum, it is obvious that what I proposed above was not an optimal solution for N-Queen. In fact N-Queen is a classic backtracking problem and can be solved much efficiently using BT but what I tried to show was 1st the power of Python generators and itertools to solve this problem is a few lines (while preserving simplicity) and 2nd using this CPU intensive brute-force search to compare CPython and PyPy implementations.

So I decided to mention a backtracking implementation which solves the problem much much faster and easily covers boards of size up to 14.

def last_one_hits(answers, size):
    i = size - 1
    x, j = answers[i], 0
    for y in answers[:i]:
        d = x - y
        if x == y or i - j == d or j - i == d: return True
        j += 1
    return False

def n_queen_bt(n):
    found = [0] * n # initiate an answer of size n
    size = 1 # in start, we have 1 queen in cloumn/row (0,0)

    while size > 0:
        hits = last_one_hits(found, size) # new queen hits prevs

        if size == n and not hits: #full and does not hit
            yield found

        if size < n and not hits: #not full or hit, add a new
            found[size] = 0
            size += 1
            while size > 0: #move last or maybe the ones before
                found[size-1] += 1
                if found[size-1] == n: #end of this clmn,go back
                    size -= 1
                    break #did the movement job, let's continue

From → linux, python

  1. I wonder what would happen if you use xrange() instead of range()… afaik, internally, it doesn’t build up a list but goes out yielding the numbers.

    • abbaspour permalink

      yes, I agree with @Dru89. I intentionally used range() rather than xrange() here.

  2. @Esteban, that wouldn’t make a difference here in terms of speed, I’m sure. Probably make an ounce of a difference in memory, since the size of the board is relatively small.

  3. Very interesting! One note: could plot the results with semilogy (the y-axis in log scale), then the results would be visible for n <= 9 and not just the last too points.

  4. Minor point — the title should be CPython vs. PyPy — “Python” doesn’t name a specific implementation of Python. ;)

  5. Krishna permalink

    Is this efficient? For n = 11, you are going through 39916800 permutations for a mere 2680 number of solutions.

    With a few more lines of code you can have a program that solves this problem in much less than 100 secs( for n = 11 ) and without using pypy.

    But I guess you know that and this is about reducing the number of lines of code rather than execution time.

  6. Luciano Silva permalink

    Why not

    def hit(p):
    n = len(p)
    for i in range(n – 1):


  7. JohnH permalink

    Must be an older computer? Just running on my year-old iMac in the regular python interpreter (2.6.1) computing the solutions for n=11 takes only 135 seconds…

  8. JohnH permalink

    ok, I see that it is older, missed that part of the article. PyPy on my 3Ghx core duo runs n=11 in 25.92 sec….

  9. abbaspour permalink

    Discussion related to this post in YCombinator:

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