Chapter 24: Chaining Relationals and Sparse Conditional Constant Propagation

Co-authored-by: Tamás Hadházy <85063808+Hels15@users.noreply.github.com>
Co-authored-by: Hsiang-Ying Fu <66062782+fuhsnn@users.noreply.github.com>
Co-authored-by: fuhsnn <66062782+fuhsnn@users.noreply.github.com>
Co-authored-by: Symious <14933944+symious@users.noreply.github.com>
Co-authored-by: Jun Kawasaki <jun784@gmail.com>
Co-authored-by: Hadházy Tamás <helsgithub15@gmail.com>
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>

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+# Chapter 24: Chaining Relationals and Sparse Conditional Constant Propagation
+
+English | [日本語](README.ja.md)
+
+[Previous: Chapter 23](../chapter23/README.md) |
+[Next: Chapter 25](../chapter25/README.md)
+
+In this Chapter we allow *chaining* conditionals, e.g. `(60 <= score < 90)`
+where `score` in the middle does not have to be repeated.
+
+The main compiler change is Interprocedural Sparse Conditional Constant
+Propagation (SCCP): an optimistic type solve which also discovers call-graph
+edges. Memory follows Chapter 11's lazy partitioning: BulkMemPhi represents
+whole memory, MemPhi merges precise aliases, and allocations consume and produce
+partial memory for their instance fields. During SCCP, memory Phis ignore
+unreachable inputs and allocation results stay high until control reaches them.
+Chapter 25 retains the larger incomplete-types SSA redesign, private constructor
+memory, and separate compilation; see the [backport review queue](../chapter-backports.md).
+
+
+You can also read [this chapter](https://github.com/SeaOfNodes/Simple/tree/linear-chapter24) in a linear Git revision history on the [linear](https://github.com/SeaOfNodes/Simple/tree/linear) branch and [compare](https://github.com/SeaOfNodes/Simple/compare/linear-chapter23...linear-chapter24) it to the previous chapter.
+
+
+Here is the [complete language grammar](24-grammar.md) for this chapter.
+
+## Chaining Relational Tests
+
+Chaining relational tests offer a cleaner, more readable way to write chained
+comparisons without repeating the same variable.
+
+```java
+if (min <= x && x < max)
+```
+
+becomes
+
+```java
+if (min <= x < max)
+```
+
+This syntax saves space and makes the condition more intuitive, similar to how
+range checks are written in mathematics.  E.g.
+
+```java
+int score = 75;
+if (60 <= score < 90) {
+    sys.io.p("Pass");
+}
+```
+This checks if score is between 60 (inclusive) and 90 (exclusive), without repeating `score`.
+
+Expressions that mix opposite directions of comparison, like using both
+`<=` and `>=`, are not allowed by Simple's direction rule.
+
+```java
+if (a <= b >= c)
+```
+
+
+### Operator direction rules
+Stacked comparisons are *only* valid if all the comparison operators
+"point" the same way:
+- `<` with `<`
+- `<` with `<=`
+- `>` with `>`
+- `>` with `>=`
+
+Equality operators (`==` and `!=`) have their normal, lower precedence; they
+are not links in a relational chain.
+
+It is not allowed to mix directions in a single chain:
+```java
+if (a <= b >= c) // Invalid
+```
+
+Comparisons can be forced into numeric context using booleans.
+This is valid but unusual:
+```java
+return (0 < arg) > 1;
+```
+Depending on the result of (0 < arg) this will turn into either
+```java
+return 0 > 1;
+```
+OR
+```java
+return 1 > 1;
+```
+The equality operator is valid in this form, but since it has lower precedence than comparisons,
+```java
+a < b == c
+```
+is parsed as:
+```java
+(a < b) == c
+```
+This might not be the intended chained comparison.
+A more explicit alternative is:
+```java
+a < b && b == c
+```
+Comparisons can be chained together in any length, and different comparison operators may be
+mixed freely as long as they point in the same direction.
+Each intermediate operand is evaluated once. Later operands are evaluated only
+if earlier comparisons succeed, so calls and updates in a chain short-circuit.
+
+E.g:
+```
+return 0 < arg < arg+1 < 4;
+```
+
+### Tricky edge cases
+```java
+return 0 != arg != 1;
+```
+
+This is parsed by standard precedence rules as:
+```java
+return ((0 != arg) != 1);
+```
+not as chained inequality.
+
+```java
+return a == b == c;
+```
+Similarly, this is parsed as:
+```java
+return ((a == b) == c);
+```
+
+
+
+## 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; `phi` denotes a merge, not Simple syntax.
+
+```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);
+}
+```
+
+### Interprocedural
+
+In **Simple**, we let our SCCP run interprocedurally, again by (nearly) doing nothing:
+`CallNodes` call `FunNodes`, `ReturnNodes` return to `CallEndNodes` and they
+pass their type information along in exactly the normal way... except for one issue:
+there are no edges between `Calls` and `Funs` (nor `Returns` and `CallEnds`).
+Such edges make up a [Call Graph](https://en.wikipedia.org/wiki/Call_graph).
+
+We extend the normal SCCP algorithm to observe when a `Call`s function pointer
+input changes - when it picks up new function targets.  There are only a finite
+number so we represent them exactly with *function indices* as described in an
+earlier chapter.  (Separate compilation adds a special index for the infinite
+unknown functions that are called outside this compilation unit).
+
+Once we observe a new *fidx* at a `Call`, we *link* the `Call` with the `Fun`,
+and the `Return` with the `CallEnd`.  Such linkage acts exactly as-if we added
+a CFG edge from the `Call` to the `Fun`, and the `Call`s arguments to the
+`Fun`s `ParmNodes`.  `FunNodes` extend `RegionNodes` (and `ParmNodes` extend
+`PhiNodes`), so this linkage uses the same semantics.  `Fun`s become reachable
+when called; `Parm`s merge (*meet*) all calling arguments, and so on.
+Similarly, `CallEnd`s merge results from all called functions, computing the
+*meet* over all the `Return`s.
+
+This *link* step adds a few checks to our algorithm's inner loop, and also
+builds a precise Call Graph which allows our SCCP to run interprocedurally.
+Other than discovering a few new edges as the algorithm proceeds, the core
+algorithm is unchanged.  We are guaranteed to terminate with a fixed point
+solution, and may discover e.g. certain call parameters are constants (or
+e.g. not-null) on all calling paths.
+
+
+## RegAlloc improvements: try cold loop splits first
+
+A loop Phi and its backedge can belong to one live range even though their
+values must coexist at some instruction.  This self-conflict requires splitting
+the range.  Chapter 23 split the Phi's definition and incoming values right away.
+This chapter first leaves the hot definition/backedge alone and tries the
+outside-loop copies.  If coloring succeeds, it avoids moves on every iteration.
+Other conflicts, including two-address operations, keep their existing handling.
+
+The allocator remembers which Phis have had this chance.  If the same Phi
+self-conflicts again, the next attempt uses the ordinary aggressive splitting.
+The record uses Phi identity because live ranges are rebuilt each round.  It
+also covers a backedge-only conflict where the first attempt inserts no copy:
+that attempt must still consume the one permitted delay.  A regression checks
+both the cheaper first attempt and the mandatory fallback.
+
+Rows are fixed test cohorts; columns are compiler chapters. Each cell gives
+**Ops / RA / X**: executed ARM+RISC-V instructions, the subset emitted for
+allocator copies/rematerializations, and x86's loop-weighted split-move estimate.
+**Cases D / X** counts measured dynamic program/target cases and x86 compilations;
+membership and inputs stay fixed across columns. Native-library work is excluded.
+Chapter 20 has no encoder, hence no dynamic counts. Lower is better within each
+metric; see the [measurement details](../regalloc-spills.md) for coverage.
+
+<!-- spill-matrix:start -->
+| Test cohort | Cases D / X | Ch 20: Ops / RA / X | Ch 21: Ops / RA / X | Ch 22: Ops / RA / X | Ch 23: Ops / RA / X | Ch 24: Ops / RA / X |
+|---|---:|---:|---:|---:|---:|---:|
+| Ch 20 | 22 / 13 | — / — / 161 | 55,173 / 5,407 / 174 | 58,751 / 9,099 / 175 | 58,736 / 9,084 / 184 | 58,720 / 9,068 / 183 |
+| Ch 21 | 20 / 18 |  | 6,207 / 261 / 461 | 6,324 / 379 / 475 | 6,326 / 381 / 475 | 6,325 / 380 / 470 |
+| Ch 22 | 14 / 8 |  |  | 198 / 60 / 7 | 194 / 56 / 7 | 194 / 56 / 7 |
+| Ch 23 | 14 / 8 |  |  |  | 630 / 160 / 36 | 630 / 160 / 36 |
+| Ch 24 | 36 / 20 |  |  |  |  | 4,923 / 1,341 / 790 |
+<!-- spill-matrix:end -->
+