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Department of Computer Science and Technology

Optimising Compilers


Course pages 2022–23

Optimising Compilers

Principal lecturer: Timothy Jones
These slides due to (and copyright holder): Tom Stuart (lectures 1-16) and Alan Mycroft (lecture 13a); modified by Timothy Jones
Taken by: Part II

The slides for each lecture are available as individual downloads below; alternatively, download them all as one giant document: 1-up, tree-preserving 8-up or 8-up with the course notes.

There is an errata at the end of this page

  • Lecture 1: Introduction
    • Structure of an optimising compiler
    • Why optimise?
    • Optimisation = Analysis + Transformation
    • 3-address code
    • Flowgraphs
    • Basic blocks
    • Types of analysis
    • Locating basic blocks

  • Lecture 2: Unreachable-code & -procedure elimination
    • Control-flow analysis operates on the control structure of a program (flowgraphs and call graphs)
    • Unreachable-code elimination is an intra-procedural optimisation which reduces code size
    • Unreachable-procedure elimination is a similar, interprocedural optimisation making use of the program's call graph
    • Analyses for both optimisations must be imprecise in order to guarantee safety

  • Lecture 3: Live variable analysis
    • Data-flow analysis collects information about how data moves through a program
    • Variable liveness is a data-flow property
    • Live variable analysis (LVA) is a backwards data-flow analysis for determining variable liveness
    • LVA may be expressed as a pair of complementary data-flow equations, which can be combined
    • A simple iterative algorithm can be used to find the smallest solution to the LVA data-flow equations

  • Lecture 4: Available expression analysis
    • Expression availability is a data-flow property
    • Available expression analysis (AVAIL) is a forwards data-flow analysis for determining expression availability
    • AVAIL may be expressed as a pair of complementary data-flow equations, which may be combined
    • A simple iterative algorithm can be used to find the largest solution to the AVAIL data-flow equations
    • AVAIL and LVA are both instances (among others) of the same data-flow analysis framework

  • Lecture 5: Data-flow anomalies and clash graphs
    • Data-flow analysis is helpful in locating (and sometimes correcting) data-flow anomalies
    • LVA allows us to identify dead code and possible uses of uninitialised variables
    • Write-write anomalies can be identified with a similar analysis
    • Imprecision may lead to overzealous warnings
    • LVA allows us to construct a clash graph

  • Lecture 6: Register allocation
    • A register allocation phase is required to assign each virtual register to a physical one during compilation
    • Registers may be allocated by colouring the vertices of a clash graph
    • When the number of physical registers is limited, some virtual registers may be spilled to memory
    • Non-orthogonal instructions may be handled with additional MOVs and new edges on the clash graph
    • Procedure calling standards are also handled this way

  • Lecture 7: Redundancy elimination
    • Some optimisations exist to reduce or remove redundancy in programs
    • One such optimisation, common-subexpression elimination, is enabled by AVAIL
    • Copy propagation makes CSE practical
    • Other code motion optimisations can also help to reduce redundancy
    • The optimisations work together to improve code

  • Lecture 8: Static single-assignment; strength reduction
    • Live range splitting reduces register pressure
    • In SSA form, each variable is assigned to only once
    • SSA uses Φ-functions to handle control-flow merges
    • SSA aids register allocation and many optimisations
    • Optimal ordering of compiler phases is difficult
    • Algebraic identities enable code improvements
    • Strength reduction uses them to improve loops

  • Lecture 9: Abstract interpretation
    • Abstractions are manageably simple models of unmanageably complex reality
    • Abstract interpretation is a general technique for executing simplified versions of computations
    • For example, the sign of an arithmetic result can be sometimes determined without doing any arithmetic
    • Abstractions are approximate, but must be safe
    • Data-flow analysis is a form of abstract interpretation

  • Lecture 10: Strictness analysis
    • Functional languages can use CBV or CBN evaluation
    • CBV is more efficient but can only be used in place of CBN if termination behaviour is unaffected
    • Strictness shows dependencies of termination
    • Abstract interpretation may be used to perform strictness analysis of user-defined functions
    • The resulting strictness functions tell us when it is safe to use CBV in place of CBN

  • Lecture 11: Constraint-based analysis
    • Many analyses can be formulated using constraints
    • 0CFA is a constraint-based analysis
    • Inequality constraints are generated from the syntax of a program
    • A minimal solution to the constraints provides a safe approximation to dynamic control-flow behaviour
    • Polyvariant (as in 1CFA) and polymorphic approaches may improve precision

  • Lecture 12: Inference-based analysis
    • Inference-based analysis is another useful framework
    • Inference rules are used to produce judgements about programs and their properties
    • Type systems are the best-known example
    • Richer properties give more detailed information
    • An inference system used for analysis has an associated safety condition

  • Lecture 13: Effect systems
    • Effect systems are a form of inference-based analysis
    • Side-effects occur when expressions are evaluated
    • Function types must be annotated to account for latent effects
    • A type system may be modified to produce judgements about both types and effects
    • Subtyping may be required to handle annotated types
    • Different effect structures may give more information

  • Lecture 13a: Alias and points-to analysis
    • Reads and writes through pointers add ambiguity to analyses
    • Exploiting parallelism requires us to know whether memory-access instructions alias
    • Anderson's points-to analysis is a flow-insensitve algorithm
    • Other approaches improve analysis time or precision

  • Lecture 14: Instruction scheduling
    • Instruction pipelines allow a processor to work on executing several instructions at once
    • Pipeline hazards cause stalls and impede optimal throughput, even when feed-forwarding is used
    • Instructions may be reordered to avoid stalls
    • Dependencies between instructions limit reordering
    • Static scheduling heuristics may be used to achieve near-optimal scheduling with an O(n²) algorithm

  • Lecture 15: Register allocation vs. instruction scheduling; legality of reverse engineering
    • Register allocation makes scheduling harder by creating extra dependencies between instructions
    • Less aggressive register allocation may be desirable
    • Some processors allocate and schedule dynamically
    • Reverse engineering is used to extract source code and specifications from executable code
    • Existing copyright legislation may permit limited reverse engineering for interoperability purposes

  • Lecture 16: Decompilation
    • Decompilation is another application of program analysis and transformation
    • Compilation discards lots of information about programs, some of which can be recovered
    • Loops can be identified by using dominator trees
    • Other control structure can also be recovered
    • Types can be partially reconstructed with constraint-based analysis


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