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diff --git a/chapter-sccp/README.md b/chapter-sccp/README.md deleted file mode 100644 index 3b7599da..00000000 --- a/chapter-sccp/README.md +++ /dev/null @@ -1,176 +0,0 @@ -# SCCP Within a Function - -English | [日本語](README.ja.md) - -[Previous: Methods and Types](../chapter23/README.md) | -[Next: SCCP Across Functions](../chapter-interprocedural-sccp/README.md) - -This chapter introduces [SCCP](https://en.wikipedia.org/wiki/Sparse_conditional_constant_propagation) -within each function. Next chapter, we'll go interprocedural with SCCP. - - -The [chapter split](../docs/chapter-split.md) uses names while the chapter -boundaries settle. This is an independent compiler snapshot, extracted from -Chapter 24. The numbered snapshot remains a comparison baseline. - -## Sparse Conditional Constant Propagation - -[SCCP](https://en.wikipedia.org/wiki/Sparse_conditional_constant_propagation) -is the top-down version of the same peephole/constant-folding work we have -been doing all along. The rules for the algorithm are well known from the -literature and commonly taught at the college level; -[example slides](https://www.cse.psu.edu/~gxt29/teaching/cse522s23/slides/08monotoneFramework.pdf). - -- We have a lattice already -- We have SSA form already (hence the algorithm is *sparse*) -- We have a mapping from Nodes to Types (`Node._type`) -- We have transfer functions (`Node.compute`) already. - -We initialize all `_type` fields to ⊤ (as opposed to ⊥), and place all Nodes on -a worklist. We pull a Node from the worklist, run the transfer function -`node.compute()`, and if the result changes from `node._type` we update -`node._type` and put its output neighbors on the worklist. A simplified -version with all asserts and debug helpers removed: - -```java -while( (n = code._iter._work.pop()) != null ) { - Type nval = n.compute(); - if( n._type != nval ) { - assert n._type.isa(nval); // Types start high and always fall - n._type = nval; // Update type in Node - code._iter.addAll(n._outputs);// Neighbors might change, need to inspect - } -} -``` - -This algorithm always terminates (and generally fairly quickly) and always -reaches a *fixed point*, as "best answer" subject to the limits of our lattice -and transfer functions. The resulting types are monotonically better than -those found by the peephole rules; we can then run the peephole rules on nodes -with improved types, to see if unrelated peepholes can fire - such as newly -discovered constants replacing computations with ConstantNodes. - -The following is SSA pseudocode: - -```text -int x0 = 1; // x0 = 1 -while( rand ) { - x1 = phi(x0,x2); // x1 = BOT = phi(1,BOT) - x2 = 2 - x1; // x2 = BOT = 2 - BOT -} -return x1 -``` - -Without SCCP, this small program cannot fold the computation of `x`. -A glance at the program tells us `x` must always be a `1` but -the bottom-up (pessimistic) approach decides that since x1 is BOT, x2 must be -BOT so x1 must be BOT. We need the optimistic approach to break the -stalemate, and just *assume* x1 is `1`, and then we can discover that x2 is -also a `1`. Folding the value does not prove the loop terminates or make -its condition and side effects removable. - - -The algorithm progresses from left to right and stops when no more changes -happen: - -|Var|init|x0=1|x1=phi(1,T)|x2=2-x1|x1=phi(1,1)| -|---|----|----|-----------|-------|-----------| -|x0 | T | 1 | 1 | 1 | 1 | -|x1 | T | T | 1 | 1 | 1 | -|x2 | T | T | T | 1 | 1 | - - - -### Conditional - -Since our control flow is in the same graph as our data flow (that's the major -point of Sea-of-Nodes!), we need not do anything special and our algorithm is -*conditional* already. Again an example is interesting: - -Again using SSA pseudocode: - -```text -int x0 = 1; // x0 = 1 -while( rand ) { - x1 = phi(x0,x4); // x1 = BOT = phi(1,BOT) - if( x1 == 1 ) // Since x1 is BOT, both true and false arms are taken - x2 = 2 - x1; // x2 = BOT = 2 - BOT - else x3 = 99; // x3 = 99 - x4 = phi(x2,x3); // x4 = BOT = phi(BOT,99) -} -return x1 -``` - -Again the algorithm progresses from left to right, although this time not all -paths are reachable. The `if` result is a pair of Control values, shown as -either `C` if the path is alive or `X` if not. This is used when we compute -`x4 = phi(x2,x3)`; the `PhiNode` ALSO checks the corresponding control input -from the `if` and only merges alive paths. - - -|Var|init |x0=1|x1=phi(1,T)| 1 == 1? | x2=2-x1 | x3=99 |x4=phi(C/1,X/99)|x1=phi(1,1)| -|---|-----|----|-----------|---------|---------|---------|----------------|-----------| -|x0 | T | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -|x1 | T | T | 1 | 1 | 1 | 1 | 1 | 1 | -|if | T | T | T | [C,X] | [C,X] | [C,X] | [C,X] | [C,X] | -|x2 | T | T | T | T | 1 | 1 | 1 | 1 | -|x3 | T | T | T | T | T | 99 | 99 | 99 | -|x4 | T | T | T | T | T | T | 1 | 1 | - -This kind of conditional constant propagation really helps if a condition can -be proven true or false, as this removes a test and branch from the optimized -program. A common way this happens is with null tests; here is the same -program above, but using null pointers instead of integers: - -```java -List head = notNullList(); -while( rand ) { - if( head ) head = new List(head,payload); - else { head = null; default_action(); } -} -``` - -Here since `head` starts not-null, it can never be null and the -`default_action` can never happen, and the loop can -simplify: - -```java -List head = notNullList(); -while( rand ) { - head = new List(head,payload); -} -``` - -## Convergence and calls - -Integer ranges can grow one value per loop trip. A loop Phi widens its range -after a bounded number of such steps. Constants stay precise; above-center -ranges must remain narrow so they can fall to constants monotonically. - -During SCCP, each function retains its unknown-caller Start input. Call edges -are disconnected, and call results keep the summaries already established by -pessimistic analysis. Those summaries can be sharper than the written -signature, so discarding them would lose valid facts. Calls still carry -whole-memory effects. The solve discovers no new call edges. - -After the solve, ordinary peepholes relink calls and apply inlining as before. -FinalizeIR removes uncalled bodies and detaches calls for scheduling. This -separates local optimistic analysis from the existing pessimistic call machinery. - -`SccpTest` checks a loop fixed point and a function called with 1 and 2: its -parameter remains general here, while the following chapter infers [1,2]. - -A recursive function whose return type needs optimistic propagation through -calls can still report "No defined return type" here. The next chapter adds -that inference and the widening needed to make it converge; its -`Chapter24Test.testRecursiveWidening` exercises both recursive data cycles. - -## Building and checking - -Run `make lib` once for Java test dependencies, then `make tests` in this -directory. From the repository root, use `make tests CHAPTERS=chapter-sccp`. -The inherited tests retain their historical class names. - -The [grammar](docs/24-grammar.md) includes chained comparisons inherited from -the extraction baseline. Their planned teaching home is with short-circuit -expressions. Allocator tuning is covered separately by the planned appendix. |