Added parallel wordcount example.

examples/wordcount.j

@@ -0,0 +1,87 @@
+# wordcount.j
+#   
+# Implementation of parallelized "word-count" of a text, inspired by the 
+# Hadoop WordCount example. Uses @spawn and fetch() to parallelize 
+# the "map" task. Reduce is currently done single-threaded.
+#
+# To run in parallel on a string stored in variable `text`:
+#  julia -p <N> 
+#  julia> @everywhere load("<julia_dir>/examples/wordcount.j")
+#  julia> ...(define text)...
+#  julia> counts=parallel_wordcount(text)
+#
+# Or to run on a group of files, writing results to an output file:
+#  julia -p <N>
+#  julia> @everywhere load("<julia_dir/examples/wordcount.j")
+#  julia> wordcount_files("/tmp/output.txt", "/tmp/input1.txt","/tmp/input2.txt",...) 
+
+# "Map" function.
+# Takes a string. Returns a HashTable with the number of times each word 
+# appears in that string.
+function wordcount(text)
+    words=split(text,(' ','\n','\t','-','.',',',':',';'),false)
+    counts=HashTable()
+    for w = words
+        counts[w]=get(counts,w,0)+1
+    end
+    return counts
+end
+
+# "Reduce" function.
+# Takes a collection of HashTables in the format returned by wordcount()
+# Returns a HashTable in which words that appear in multiple inputs
+# have their totals added together.
+function wcreduce(wcs)
+    counts=HashTable()
+    for c = wcs
+        for (k,v)=c
+            counts[k] = get(counts,k,0)+v
+        end
+    end
+    return counts
+end
+
+# Splits input string into nprocs() equal-sized chunks (last one rounds up), 
+# and @spawns wordcount() for each chunk to run in parallel. Then fetch()s
+# results and performs wcreduce().
+function parallel_wordcount(text)
+    lines=split(text,'\n',false)
+    np=nprocs()
+    unitsize=ceil(length(lines)/np)
+    wcounts={}
+    rrefs={}
+    # spawn procs
+    for i=1:np
+        first=unitsize*(i-1)+1
+        last=unitsize*i
+        if last>length(lines)
+            last=length(lines)
+        end
+        subtext=join(lines[int(first):int(last)],"\n")
+        push(rrefs, @spawn wordcount( subtext ) )
+    end
+    # fetch results
+    while length(rrefs)>0
+        push(wcounts,fetch(pop(rrefs)))
+    end
+    # reduce
+    count=wcreduce(wcounts)
+    return count
+end
+
+# Takes the name of a result file, and a list of input file names.
+# Combines the contents of all files, then performs a parallel_wordcount
+# on the resulting string. Writes the results to result_file.
+function wordcount_files(result_file,input_file_names...)
+    text=""
+    for f = input_file_names
+        fh=open(f)
+        text=join( {text,readall(fh)}, "\n" )
+        close(fh)
+    end
+    wc=parallel_wordcount(text)
+    fo=open(result_file,"w")
+    for (k,v) = wc
+        with_output_stream(fo,println,k,"=",v)
+    end
+end