Due 11:59pm Sunday Apr 6 (two week project)
Download the skeleton code for the project here.
Your task in this assignment is to implement a series of optimizations
for the CPS compiler. In the skeleton code you will find
src/miniscala/CPSOptimizer.scala. This file contains the
optimizer mechanism, followed by two specific instantiations:
CPSOptimizerHigh and CPSOptimizerLow. Your task is to
fill in the optimizer mechanism, which consists of shrinking and
non-shrinking optimizations (as described in the lectures) and the
specific implementation of CPSOptimizerHigh. The specific
implementation of CPSOptimizerLow is given.
More specifically, you are expected to implement:
For bonus grade, you may implement:
For fun and fame (see optimization challenge), you may implement:
Do not implement:
Once the optimizer is fully functional, it should optimize the following example correctly:
Input:
def printChar(c: Int) = putchar(c);
def functionCompose(f: Int => Int, g: Int => Int) = (x: Int) => f(g(x));
def plus(x: Int, y: Int) = x + y;
def succ(x: Int) = x + 1;
def twice(x: Int) = x + x;
printChar(functionCompose(succ,twice)(39));
printChar(functionCompose(succ,succ)(73));
printChar(functionCompose(twice,succ)(4));
0
Output:
vall t_2 = 79;
valp t_3 = byte-write(t_2);
vall t_5 = 75;
valp t_6 = byte-write(t_5);
vall t_8 = 10;
valp t_9 = byte-write(t_8);
vall t_11 = 0;
halt(t_11)
Closures? Blocks? The optimizer eliminated all abstractions and gave us the simplest inline code possible. :D Now, to start hacking on the optimizer:
cd proj6/compiler
sbt
> run ../library/miniscala.lib ../examples/pascal.scala
...
[info] Running miniscala.Main ../library/miniscala.lib ../examples/pascal.scala
enter size (0 to exit)> 12
(1 11 55 165 330 462 462 330 165 55 11 1 )
(1 10 45 120 210 252 210 120 45 10 1 )
(1 9 36 84 126 126 84 36 9 1 )
(1 8 28 56 70 56 28 8 1 )
(1 7 21 35 35 21 7 1 )
(1 6 15 20 15 6 1 )
(1 5 10 10 5 1 )
(1 4 6 4 1 )
(1 3 3 1 )
(1 2 1 )
(1 1 )
(1 )
enter size (0 to exit)> 0
Instruction Stats
=================
6205 LetP
2998 LetL
...
The “Instruction stats” indicate how many instructions were executed while computing the pascal triangle. This is in contrast to the total instructions in the output program, which is constant regardless of the input. NOTE: the number is when you use contification.
The implementation sketch that is given to you is not a trivial mapping of the theory into code, so here are some notes to guide you through it.
The optimizations are split in shrinking (in method shrink) and
non-shrinking (or general inlining, in method inline)
optimizations. Remember that shrinking optimizations are safe to apply
as often as desired, while inlining may lead to arbitrarily large code,
or even diverge the optimizer.
The general idea of the optimizer (see method apply) is the
following: After an initial step of iteratively applying shrink
until a fixed point, we iteratively apply a step of inline and a
step of shrink until one of the following happens:
maxSize, given as a
function of the initial size.fixedPoint
function itself, while the third is checked within inline.The inline function is actually a small series of inlining
steps. During each inlining step, we choose to inline
functions/continuations of increasing size. For continuations, this size
is linear to the number of steps, but for functions it is exponential
(according to the Fibonacci sequence). We stop inlining after a
specified number of steps, or if the tree grows to more than
maxSize.
The internal traversals of both the shrink and inline
functions accept an implicit argument of the State type, which,
as you may have guessed, tracks the local state of the optimization. You
are supposed to update it as you traverse the subtrees using the
designated methods. The descriptions in the source file will hopefully
make each field’s role to the optimization clear.
Testing will be slightly different this time. We have 3 sets of tests:
test/miniscala/test/CPSOptimizer_Blackbox.scala)test/miniscala/test/CPSOptimizer_Greybox.scala and the
additions to src/miniscala/CPSInterpreter.scala for more
details)src/miniscala/CPSInterpreter.scala) – you can compete with
your colleagues and with our reference implementation to get the
best results possible. And yes, you should brag about your results
on Piazza!This assignment gives you a lot of liberty into how and what you optimize, so go ahead and write the best optimizer in the entire class!
You should turn in the proj6 directory. Please run an ‘sbt clean’ and ‘./cleanall.sh’ before submitting.
To turn in your project create a ZIP file named
<purdueemailusername>-proj<N>.zip of the proj6 directory for
example axhebraj-proj6.zip and upload it to the corresponding
assignment on Brightspace.
For the reference compiler we have developed, the challenge results are given below. Different results form these statistics are encouraged, especially if they reduce the numbers in one category or the other.
NOTE: Turn on contification in order to improve your numbers.
Challenge: maze.scala with 12 for both inputs.
Instruction Stats
=================
1320209 LetP
1076643 LetL
1003773 LetC
446036 If
293889 AppF
113226 AppC
1 LetF
1 Halt
Value Primitives Stats
======================
510160 CPSBlockGet$
247412 CPSOr$
241868 CPSArithShiftL$
239466 CPSBlockTag$
30504 CPSBlockSet$
14660 CPSBlockAlloc
10367 CPSSub$
10105 CPSAdd$
8621 CPSArithShiftR$
3433 CPSAnd$
1611 CPSMul$
1056 CPSXOr$
662 CPSByteWrite$
264 CPSMod$
14 CPSBlockLength$
6 CPSByteRead$
Logic Primitives Stats
======================
380718 CPSEq$
63198 CPSNe$
2058 CPSLt$
52 CPSGt$
10 CPSLe$
Functions defined: 27
Continuations defined: 1003773