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/* NSC -- new scala compiler
* Copyright 2005 LAMP/EPFL
* @author Martin Odersky
*/
// $Id$
package scala.tools.nsc.backend.icode;
import scala.tools.nsc.ast._;
import scala.collection.mutable.Stack;
trait Linearizers: ICodes {
import opcodes._;
trait Linearizer {
def linearize(c: IMethod): List[BasicBlock];
}
/**
* A simple linearizer which predicts all branches to
* take the 'success' branch and tries to schedule those
* blocks immediately after the test. This is in sync with
* how 'while' statements are translated (if the test is
* 'true', the loop continues).
*/
class NormalLinearizer extends Linearizer with WorklistAlgorithm {
type Elem = BasicBlock;
type WList = Stack[Elem];
val worklist: WList = new Stack();
var blocks: List[BasicBlock] = Nil;
def linearize(m: IMethod): List[BasicBlock] = {
val b = m.code.startBlock;
blocks = Nil;
run {
worklist ++= (m.exh map (.startBlock));
worklist ++= (m.finalizers map (.startBlock));
worklist.push(b);
}
blocks.reverse;
}
/** Linearize another subtree and append it to the existing blocks. */
def linearize(startBlock: BasicBlock): List[BasicBlock] = {
//blocks = startBlock :: Nil;
run( { worklist.push(startBlock); } );
blocks.reverse;
}
def processElement(b: BasicBlock) =
if (b.size > 0) {
add(b);
b.lastInstruction match {
case JUMP(where) =>
add(where);
case CJUMP(success, failure, _, _) =>
add(success);
add(failure);
case CZJUMP(success, failure, _, _) =>
add(success);
add(failure);
case SWITCH(_, labels) =>
add(labels);
case RETURN(_) => ();
case THROW() => ();
case LEAVE_FINALIZER(_) => ();
}
}
def dequeue: Elem = worklist.pop;
/**
* Prepend b to the list, if not already scheduled.
* TODO: use better test than linear search
*/
def add(b: BasicBlock) =
if (blocks.contains(b))
()
else {
blocks = b :: blocks;
worklist push b;
}
def add(bs: List[BasicBlock]): Unit = bs foreach add;
}
}
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