5. This Talk ...
... reports on a new compiler architecture for dsc,
the Dotty Scala Compiler.
• It has a mostly functional architecture,
but uses a lot of low-level tricks for speed.
• Some of its concepts are inspired by
functional databases.
6. My Early Involvement in Compilers
80s Pascal, Modula-2
single pass, following the school of Niklaus Wirth.
95-96 Espresso, the 2nd Java compiler
E Compiler
Borland’s JBuilder
used an OO AST with one class per node
and all processing distributed between
methods on these nodes.
96-99 Pizza GJ javac (1.3+) -> scalac (1.x)
replaced OO AST with pattern matching.
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7. Current Scala Compiler
2004-12 nsc compiler for Scala (2.0-2.10)
Made (some) use of functional capabilities of Scala
Added:
– REPL
– presentation compiler for IDEs (Eclipse, Ensime)
– run-time meta programming with toolboxes
It’s the codebase for the official scalac compiler for 2.11,
2.12 and beyond.
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8. Next Generation Scala Compiler
2012 – now: Dotty
• Rethink compiler architecture
from the ground up.
• Introduce some language
changes with the aim of better
regularity.
• Status:
– Close to bootstrap
– But still rough around the edges
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12. Challenges
A compiler for a language like Scala faces quite a few
challenges.
Among the most important are:
» Complexity
» Speed
» Latency
» Reusability
13. Challenge: Complex Transformations
• Input language (Scala) is complicated.
• Output language (JVM) is also complicated.
• Semantic gap between the two is large.
Compare with compilers to simple low-level languages
such as System F or SSA.
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15. Challenge: Speed
• Current scalac achieves 500-700 loc/sec on idiomatic
Scala code.
• Can be much lower, depending on input.
• Everyone would like it to be faster.
• But this is very hard to achieve.
- FP does have costs.
- Optimizations are ineffective.
- No hotspots, costs are
smeared out widely.
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16. Challenge: Latency
• Some applications require fast turnaround for small
changes more than high throughput.
• Examples:
– REPL
– Worksheet
– IDE Presentation Compiler
Need to keep things loaded
(program + data)
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17. Challenge: Reusability
• A compiler has many clients:
– Command line
– Build tools
– IDEs
– REPL
– Meta-programming
Abstractions must not leak.
(FP helps)
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18. A Question
Every compiler has to answer questions like this:
Say I have a class
class C[T] {
def f(x: T): T = ...
}
At some point I change it to:
class C[T] {
def f(x: T)(y: T): T = ...
}
What is the type signature of C.f?
Clearly, it depends on the time when the question is asked!
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19. Time-Varying Answers
Initially: (x: T): T
After erasure: (x: Any): Any
After the edit: (x: T)(y: T): T
After uncurry: (x: T, y: T): T
After erasure: (x: Any, y: Any): Any
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20. Naive Functional Approach
World1 IR1,1 ... IRn,1 Output1
World2 IR1,2 ... IRn,2 Output2
.
.
.
Worldk IR1,k ... IRn,k Outputk
How big is the world?
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21. A More Practical Strategy
Taking Inspiration from FRP and Functional Databases:
• Treat every value as a time-varying function.
• So the question is not:
“What is the signature of C.f” ?
but:
“What is the signature of C.f at a given point in time” ?
Need to index every piece of information with the time
where it holds.
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22. Time in dsc
Period = (RunID, PhaseID)
• RunIDs is incremented for each compiler run
• PhaseID ranges from 1 (parser) to ~ 50 (backend)
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Run1 Run2 Run3
23. Time-Indexed Values
sig(C.f, (Run 1, parser)) = (x: T): T
sig(C.f, (Run 1, erasure)) = (x: Any): Any
sig(C.f, (Run 2, erasure)) = (x: T)(y: T): T
sig(C.f, (Run 2, uncurry)) = (x: T, y: T): T
sig(C.f, (Run 2, erasure) = (x: Any, y: Any): Any
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24. Task of the Compiler
• Compute all values needed for analysis and code
generation over all periods where they are relevant.
• Problem: The graph of this function is humongous!
• More work is needed to make it efficiently explorable.
• But for a start it looks like the right model.
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27. Tree Attributes
What about tree attributes?
In dsc, we simplified as much as we could.
Were left with just two attributes:
– Position (intrinsic)
– Type
The job of the type checker is to transform untyped to typed
trees.
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28. Typed Abstract Syntax Trees
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For instance, for x * 2:
The distinction whether a tree is typed or untyped is pretty
important, merits being reflected in the type of AST itself.
29. From Untyped to Typed Trees
Idea: parameterize the type Tree of AST’s with the attribute
info it carries.
Typed tree: tpd.Tree = Tree[Type]
Untyped tree: untpd.Tree = Tree[Nothing]
This leads to the following class:
class Tree[T] {
def tpe: T
def withType(t: Type): Tree[Type]
}
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30. Question of Variance
• Question: Which of the following two subtype
relationships should hold?
tpd.Tree <: untpd.Tree
untpd.Tree <: tpd.Tree ?
• What is the more useful relationship?
(the first)
• What relationship do the variance
rules imply?
(the second) 30
class Tree[? T] {
def tpe: T ...
}
31. Fixing class Tree
class Tree[-T] {
def tpe: T @uncheckedVariance
def withType(t: Type): Tree[Type]
}
Interesting exception to the variance rules related to the
bottom type Nothing.
What can go “wrong” here? Given an untpd.Tree, I expect Nothing,
but I might get a Type.
Shows that it’s good have an escape hatch in the form of
@uncheckedVariance.
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32. Types
• Types carry most of the essential information of trees
and symbols.
• Two kinds of types.
– Value types: Int, Int => Int, (Boolean, String)
– Types of definitions: (x: Int)Int, Lo..Hi, Class(...)
• Represented as subtypes of the same type “Type” for
convenience.
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33. References
case class Select(qual: Tree, name: Name) {
// what is its tpe?
}
case class Ident(name: Name) {
// what is its tpe?
}
• Normally, these tree nodes would carry a “symbol”,
which acts as a reference to some definition.
• But there are no symbol attributes in dsc, for good
reason.
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35. A Question of Meaning
Question: What is the meaning of
obj.fun
?
It depends on the period!
Does that mean that obj.fun has different types,
depending on period?
No, trees are immutable!
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36. References
36
• A reference is a type
• It contains (only)
– a name
– potentially a prefix
• References
are immutable, they
exist forever.
37. What about Overloads?
The name of a TermRef may be shared by several
overloaded members of a class.
How do we determine which member is meant?
(In a nutshell, that’s why overloading is so universally hated
by compiler writers)
Trick: Allow “signature” as part of term names.
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38. What Does A Reference Reference?
Surely, a symbol?
No!
References capture more than a symbol
And sometimes they do not refer to a unique
symbol at all.
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39. References capture more than a symbol.
Consider:
class C[T] {
def f(x: T): T
}
val prefix = new C[Int]
Then prefix.f:
resolves to C’s f
but at type (Int)Int, not (T)T
Both pieces of information are part of the meaning of
prefix.f. 39
40. References
Sometimes references point to no symbol at all.
We have already seen overloading.
Here’s another example using union types, which are newly
supported by dsc:
class A { def f: Int }
class B { def f: Int }
val prefix: A | B = if (...) new A else new B
prefix.f
What symbol is referenced by prefix.f ?
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41. Denotations
The meaning
of a reference is a denotation.
Non-overloaded denotations
carry symbols (maybe) and
types (always).
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42. What Then Is A Symbol?
A symbol represents a declaration in some source
file.
It “lives” as long as the source file is unchanged.
It has a denotation depending on the period.
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43. Denotation Transformers
• How do we compute new denotations from old ones?
• For references pre.f: Can recompute the member at
new phase.
• For symbols?
uncurry.transDenot(<(x: A)(y: B): C>) = <(x: A, y: B): C>
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44. Caching Denotations
Symbols are memoized functions: Period Denotation
Keep all denotations of a symbol at different phases as a
ring. 44
45. Putting it all Together
45
• ER diagram of core compiler architecture:
*
*
46. Lessons Learned
(Not done yet, still learning)
• Think databases for modeling.
• Think FP for transformations.
• Get efficiency through low-level techniques
(caching)
• But take care not to compromise the high-level
semantics.
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48. How to make it Fast
• Caching
– Symbols cache last denotation
– NamedTypes do the same
– Caches are stamped with validity interval (current period until the
next denotation transformer kicks in).
– Need to update only if outside of validity period
– Member lookup caches denotation
Not yet tried: Parallelization.
- Could be hard (similar to chess programs)
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49. Many forms of Caches
• Lazy vals
• Memoization
• LRU Caches
• Rely on
– Purely functional semantics
– Access to low-level imperative implementation code.
– Important to keep the levels of abstractions apart!
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50. Optimization: Phase Fusion
• For modularity reasons, phases should be small. Each
phase should od one self-contained transform.
• But that means we end up with many phases.
• Problem: Repeated tree rewriting is a performance killer.
• Solution: Automatically fuse phases into one tree
traversal.
– Relies on design pattern and some small amount of
introspection.
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