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8. Static Single Assignment Form Marcus Denker

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© Marcus Denker SSA Roadmap Static Single Assignment Form (SSA) Converting to SSA Form Examples Transforming out of SSA 2

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© Marcus Denker SSA Static Single Assignment Form Goal: simplify procedure-global optimizations Definition: 3 Program is in SSA form if every variable is only assigned once

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© Marcus Denker SSA Why Static? Why Static? —We only look at the static program —One assignment per variable in the program At runtime variables are assigned multiple times! 4

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© Marcus Denker SSA Example: Sequence Easy to do for sequential programs: 5 a := b + c b := c + 1 d := b + c a := a + 1 e := a + b a 1 := b 1 + c 1 b 2 := c 1 + 1 d 1 := b 2 + c 1 a 2 := a 1 + 1 e 1 := a 2 + b 2 Original SSA

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© Marcus Denker SSA Example: Condition Conditions: what to do on control-flow merge? 6 if B then a := b else a := c end … a … if B then a 1 := b else a 2 := c End … a? … Original SSA

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© Marcus Denker SSA Solution: -Function Conditions: what to do on control-flow merge? 7 if B then a := b else a := c end … a … if B then a 1 := b else a 2 := c End a 3 := (a 1,a 2 ) … a 3 … Original SSA

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© Marcus Denker SSA The -Function -functions are always at the beginning of a basic block Select between values depending on control-flow a 1 := (a 1 …a k ): the block has k preceding blocks PHI-functions are all evaluated simultaneously. 8

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© Marcus Denker SSA SSA and CFG SSA is normally done for control-flow graphs (CFG) Basic blocks are in 3-address form 9

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Repeat: Control flow graph A CFG models transfer of control in a program —nodes are basic blocks (straight-line blocks of code) —edges represent control flow (loops, if/else, goto …) © Marcus Denker SSA 10 if x = y then S1 else S2 end S3

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SSA: a Simple Example © Marcus Denker SSA 11 if B then a1 := 1 else a2 := 2 End a3 := PHI(a1,a2) … a3 …

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Repeat: IR © Oscar Nierstrasz Intermediate Representation 12 front end produces IR optimizer transforms IR to more efficient program back end transform IR to target code

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SSA as IR © Marcus Denker SSA 13

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© Marcus Denker SSA Transforming to SSA Problem: Performance / Memory —Minimize number of inserted -functions —Do not spend to much time Many relatively complex algorithms —We do not go too much into details —See literature! 14

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© Marcus Denker SSA Minimal SSA Two steps: —Place -functions —Rename Variables Where to place -functions? We want minimal amount of needed —Save memory —Algorithms will work faster 15

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© Marcus Denker SSA Path Convergence Criterion There should be a for a at node Z if: 1.There is a block X containing a definition of a. 2.There is a block Y (Y != X) containing a definition of a. 3.There is a nonempty path P xz of edges from X to Z. 4.There is a nonempty path P yz of edges from Y to Z. 5.Path P xz and P yz do not have any nodes in common other than Z 6.The node Z does not appear within both P xz and P yz prior to the end (although it may appear in one or the other) 16

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© Marcus Denker SSA Iterated Path-Convergence Inserted is itself a definition! 17 While there are nodes X,Y,Z satisfying conditions 1-5 and Z does not contain a phi-function for a do insert PHI at node Z. A bit slow, other algorithms used in practice

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© Marcus Denker SSA Example (Simple) 18 1. block X containing a definition of a 2. block Y (Y != X) containing a definition of a. 3. path P xz of edges from X to Z. 4. path P yz of edges from Y to Z. 5. Path P xz and P yz do not have any nodes in common other than Z 6. The node Z does not appear within both P xz and P yz prior to the end

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© Marcus Denker SSA Dominance Property of SSA Dominance: node D dominates node N if every path from the start node to N goes through D. (“strictly dominates”: D!=N) 19 Dominance Property of SSA: 1.If x is used in a Phi-function in block N, then the definition of x dominates every predecessor of N. 2.If x is used in a non-Phi statement in N, then the definition of x dominates N “Definition dominates use”

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© Marcus Denker SSA Dominance and SSA Creation Dominance can be used to efficiently build SSA -Functions are placed in all basic blocks of the Dominance Frontier. Dominance Frontier: the set of all nodes N such that D dominates an immediate predecessor of N but does not strictly dominate N. 20

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Dominance Frontier 21 Marcus Denker SSA DF(D) = the set of all nodes N such that D dominates an immediate predecessor of N but does not strictly dominate N. Intuition: Nodes at the border of a region of dominance Dominance and SSA Creation

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Dominance Frontier 22 © Marcus Denker DF(D) = the set of all nodes N such that D dominates an immediate predecessor of N but does not strictly dominate N. ? Dominance and SSA Creation SSA

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Dominance Frontier 23 © Marcus Denker DF(D) = the set of all nodes N such that D dominates an immediate predecessor of N but does not strictly dominate N. Intuition: Nodes at the border of a region of dominance Dominance and SSA Creation SSA

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24 © Marcus Denker Dominance and SSA Creation SSA

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25 © Marcus Denker 5 Dominates all nodes in the gray area Dominance and SSA Creation SSA

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26 © Marcus Denker DF(5)= {4, 5, 12, 13} Targets of edges from the dominates by 5 to the region not strictly dominated by 5. Dominance and SSA Creation SSA

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Simple Example 27 © Marcus Denker DF(B1)= DF(B2)= DF(B3)= DF(B4)= SSA

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Simple Example 28 © Marcus Denker DF(B1)={?} DF(B2)= DF(B3)= DF(B4)= SSA

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Simple Example 29 © Marcus Denker DF(B1)={} DF(B2)= DF(B3)= DF(B4)= SSA

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Simple Example 30 © Marcus Denker DF(B1)={} DF(B2)={?} DF(B3)= DF(B4)= SSA

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Simple Example 31 © Marcus Denker DF(B1)={} DF(B2)={B4} DF(B3)= DF(B4)= SSA

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Simple Example 32 © Marcus Denker DF(B1)={} DF(B2)={B4} DF(B3)={B4} DF(B4)= SSA

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Simple Example 33 © Marcus Denker DF(B1)={} DF(B2)={B4} DF(B3)={B4} DF(B4)={} SSA

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Simple Example 34 © Marcus Denker DF(B1)={} DF(B2)={B4} DF(B3)={B4} DF(B4)={} PHI-Function needed in B4 (for a) SSA

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© Marcus Denker SSA Properties of SSA Simplifies many optimizations —Every variable has only one definition —Every use knows its definition, every definition knows its uses —Unrelated variables get different names Examples: —Constant propagation —Value numbering —Invariant code motion and removal —Strength reduction —Partial redundancy elimination 35 Next Week!

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© Marcus Denker SSA SSA in the Real World Invented end of the 80s, a lot of research in the 90s Used in many modern compilers —ETH Oberon 2 —LLVM —GNU GCC 4 —IBM Jikes Java VM —Java Hotspot VM —Mono —Many more… 36

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© Marcus Denker SSA Transforming out-of SSA Processor cannot execute -Function How do we remove it? 37

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© Marcus Denker SSA Simple Copy Placement 38

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© Marcus Denker SSA Problems Problems: —Copies need to be removed —Wrong in some cases after reordering of code 39 OriginalSSA with opt removed

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© Marcus Denker SSA -Congruence Insert Copies Rename Variables 40 Idea: transform program so that all variables in are the same: a1 = (a1, a1) a1 = a1 --->

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© Marcus Denker SSA -Congruence: Definitions 41 -connected(x): a3 = (a1, a2) a5 = (a3, a4) --> a1, a4 are connected -congruence-class: Transitive closure of -connected(x).

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© Marcus Denker SSA -Congruence Property 42 -congruence property: All variables of the same congruence class can be replaced by one representative variable without changing the semantics. SSA without optimizations has -congruence property Variables of the congruence class never live at the same time (by construction)

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Repeat: Liveness © Oscar Nierstrasz Code Generation 43 A variable v is live on edge e if there is a path from e to a use of v not passing through a definition of v a and b are never live at the same time, so two registers suffice to hold a, b and c

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Interference © Marcus Denker SSA 44 A variable v is live on edge e if there is a path from e to a use of v not passing through a definition of v a, c live at the same time: interference

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© Marcus Denker SSA -Removal: Big picture 45 CSSA: SSA with -congruence-property. - directly after SSA generation - no interference TSSA: SSA without -congruence-property. - after optimizations - interference 1.Transform TSSA into CSSA (fix interference) 2.Rename -variables 3.Delete

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© Marcus Denker SSA Example: Problematic case 46 X2 and X3 interfereSolution: Break up

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© Marcus Denker SSA SSA and Register Allocation Idea: remove as late as possible Variables in -function never live at the same time! —Can be stored in the same register Do register allocation on SSA! 47

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© Marcus Denker SSA SSA: Literature 48 Books: - SSA Chapter in Appel Modern Compiler Impl. In Java - Chapter 8.11 Muchnik: Advanced Compiler Construction SSA Creation: Cytron et. al: Efficiently computing Static Single Assignment Form and the Control Dependency Graph (TOPLAS, Oct 1991) PHI-Removal: Sreedhar et at. Translating out of Static Single Assigment Form (LNCS 1694)

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© Marcus Denker SSA Summary 49 SSA, what it is and how to create it —Where to place -functions? Transformation out of SSA —Placing copies —Remove Next Week: Optimizations

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© Marcus Denker SSA 50 What you should know! When a program has SSA form. What is a -function. When do we place -functions How to remove -functions

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© Marcus Denker SSA 51 Can you answer these questions? Why can we not directly generate executable code from SSA? Why do we use 3-adress code and CFG for SSA?

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© Oscar Nierstrasz SSA 52 License > http://creativecommons.org/licenses/by-sa/2.5/ Attribution-ShareAlike 2.5 You are free: to copy, distribute, display, and perform the work to make derivative works to make commercial use of the work Under the following conditions: Attribution. You must attribute the work in the manner specified by the author or licensor. Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to this one. For any reuse or distribution, you must make clear to others the license terms of this work. Any of these conditions can be waived if you get permission from the copyright holder. Your fair use and other rights are in no way affected by the above.

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N. XXX Prof. O. Nierstrasz Thanks to Jens Palsberg and Tony Hosking for their kind permission to reuse and adapt the CS132 and CS502 lecture notes.

N. XXX Prof. O. Nierstrasz Thanks to Jens Palsberg and Tony Hosking for their kind permission to reuse and adapt the CS132 and CS502 lecture notes.

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