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POParallel recursion in scala
 

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    <p>I am trying to parallelize recursive calls of sudoku solver from <a href="http://scala-programming-language.1934581.n4.nabble.com/25-lines-Sudoku-solver-in-Scala-td1987506.html" rel="nofollow">25 lines Sudoku solver in Scala</a>. I've changed their <code>Fold</code> into <code>reduce</code></p> <pre><code>def reduce(f: (Int, Int) =&gt; Int, accu: Int, l: Int, u: Int): Int = { accu + (l until u).toArray.reduce(f(accu, _) + f(accu, _)) } </code></pre> <p>which if run sequentially works fine, but when I change it into </p> <pre><code> accu + (l until u).toArray.par.reduce(f(accu, _) + f(accu, _)) </code></pre> <p>the recursion reaches the bottom much more often and generates false solutions. I thought, that it will execute the bottom level recursion and work it's way up, but doesn't seem to do so. <br> I've also tried futures</p> <pre><code>def parForFut2(f: (Int, Int) =&gt; Int, accu: Int, l: Int, u: Int): Int = { var sum: Int = accu val vals = l until u vals.foreach(t =&gt; scala.actors.Futures.future(sum + f(accu, t))) sum } </code></pre> <p>which appears to have the same problem as the <code>par.reduce</code>. I would appreciate any comment. The whole code is here: </p> <pre><code>object SudokuSolver extends App { // The board is represented by an array of string val source = scala.io.Source.fromFile("./puzzle") val lines = (source.getLines).toArray var m: Array[Array[Char]] = for ( str &lt;- lines; line: Array[Char] = str.toArray ) yield line source.close() // For printing m def print = { Console.println(""); refArrayOps(m) map (carr =&gt; Console.println(new String(carr))) } // The test for validity of n on position x,y def invalid(i: Int, x: Int, y: Int, n: Char): Boolean = i &lt; 9 &amp;&amp; (m(y)(i) == n || m(i)(x) == n || m(y / 3 * 3 + i / 3)(x / 3 * 3 + i % 3) == n || invalid(i + 1, x, y, n)) // Looping over a half-closed range of consecutive Integers [l..u) // is factored out Into a higher-order function def parReduce(f: (Int, Int) =&gt; Int, accu: Int, l: Int, u: Int): Int = { accu + (l until u).toArray.par.reduce(f(accu, _) + f(accu, _)) } // The search function examines each position on the board in turn, // trying the numbers 1..9 in each unfilled position // The function is itself a higher-order fold, accumulating the value // accu by applying the given function f to it whenever a solution m // is found def search(x: Int, y: Int, f: (Int) =&gt; Int, accu: Int): Int = Pair(x, y) match { case Pair(9, y) =&gt; search(0, y + 1, f, accu) // next row case Pair(0, 9) =&gt; f(accu) // found a solution - print it and continue case Pair(x, y) =&gt; if (m(y)(x) != '0') search(x + 1, y, f, accu) else parForFut1((accu: Int, n: Int) =&gt; if (invalid(0, x, y, (n + 48).asInstanceOf[Char])) accu else { m(y)(x) = (n + 48).asInstanceOf[Char]; val newaccu = search(x + 1, y, f, accu); m(y)(x) = '0'; newaccu }, accu, 1, 10) } // The main part of the program uses the search function to accumulate // the total number of solutions Console.println("\n" + search(0, 0, i =&gt; { print; i + 1 }, 0) + " solution(s)") } </code></pre>
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    1. COdon't use mutable data structures / vars; start by making your methods pure functions and then work on parallel execution
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    2. COThanks, it did help in the end. It makes you understand what variables (values) you are changing whenb you don't want to.
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