Optimize control-flow analysis by use of persistent maps

The case that it's worth to optimize is functions
with a lot of variables.

After debugging it's recovered that control-flow works nearly
O(n * m) where n is pseudocode size and m is a number of variables:
the algorithm performs O(n) copies of hashmap of size O(m)

Persistent maps should help because we don't need to perform a
copy of them, so the expected performance after the change is applied
is O(n log m)

We've tried pcollections and javaslang, and the latter one has demonstrated
better results

See results before and after optimizations
before:
https://github.com/dzharkov/kotlin-compiler-benchmarks/blob/3da7ba45a704969653d70b50995f730e968540d8/reports/benchmarks-many-vars-2017-03-14.txt

after with pcollections:
https://github.com/dzharkov/kotlin-compiler-benchmarks/blob/3da7ba45a704969653d70b50995f730e968540d8/reports/benchmarks-many-vars-persistent-optimizations-2017-03-17.txt

after with javaslang:
https://github.com/dzharkov/kotlin-compiler-benchmarks/blob/d22a871b175b291fb337b51ef6465ba70bbfd96c/reports/benchmarks-many-vars-javaslang-2017-04-07.txt

update_dependencies.xml

@@ -236,6 +236,9 @@
         <!-- Rhino -->
         <get-maven-library prefix="org/mozilla" lib="rhino" version="1.7.6"/>
 
+        <!-- Javaslang -->
+        <get-maven-library prefix="io/javaslang" lib="javaslang" version="2.0.6"/>
+
         <!-- Uast -->
         <property name="uast.server" value="http://dl.bintray.com/kotlin/uast"/>
         <get-maven-library prefix="org/jetbrains/uast" lib="uast-common" version="${uast.version}" target.jar.name.base="uast-common" server="${uast.server}"/>