11.3 Strictness Analysis

Strictness analysis is a game-changer for functional language compilers. It figures out which function arguments must be evaluated, allowing for smart optimizations that boost performance without changing how the program behaves.

This technique is crucial for optimizing lazy evaluation, a key feature of many functional languages. By identifying strict arguments, compilers can reduce overhead from delayed computations and improve memory usage.

Evaluation Strategies

Lazy and Strict Evaluation

Top images from around the web for Lazy and Strict Evaluation

• 

  Functional Programming View original

  Is this image relevant?

• 

  Lazy Dynamic Programming | jelv.is View original

  Is this image relevant?

• 

  Frontiers | Optimizing BCPNN Learning Rule for Memory Access View original

  Is this image relevant?

• 

  Functional Programming View original

  Is this image relevant?

• 

  Lazy Dynamic Programming | jelv.is View original

  Is this image relevant?

1 of 3

Top images from around the web for Lazy and Strict Evaluation

• 

  Functional Programming View original

  Is this image relevant?

• 

  Lazy Dynamic Programming | jelv.is View original

  Is this image relevant?

• 

  Frontiers | Optimizing BCPNN Learning Rule for Memory Access View original

  Is this image relevant?

• 

  Functional Programming View original

  Is this image relevant?

• 

  Lazy Dynamic Programming | jelv.is View original

  Is this image relevant?

1 of 3

• Lazy evaluation defers computation until results are needed, potentially improving efficiency
• Strict evaluation computes expressions immediately, ensuring all values are available upfront
• Lazy evaluation can handle infinite data structures (infinite lists)
• Strict evaluation guarantees immediate error detection and consistent memory usage
• Thunks represent unevaluated expressions in lazy evaluation, storing computations for later execution

Evaluation Order and Performance

• Evaluation order determines the sequence in which expressions are computed
• Left-to-right evaluation processes arguments from left to right before applying functions
• Right-to-left evaluation computes rightmost arguments first, potentially optimizing tail-recursive functions
• Evaluation order impacts performance by affecting cache usage and memory allocation patterns
• Choosing the appropriate evaluation strategy can significantly improve program efficiency and resource utilization

Program Analysis Techniques

Demand and Absence Analysis

• Demand analysis identifies which parts of a program are actually used, enabling targeted optimizations
• Absence analysis determines when certain computations or data structures are unnecessary, allowing for their removal
• Demand analysis helps in eliminating dead code and reducing memory usage
• Absence analysis contributes to more efficient memory management by identifying unused variables or expressions
• Both techniques work together to streamline program execution and improve overall performance

Strictness Analysis and Bottoming Functions

• Strictness analysis determines which function arguments must be evaluated for the function to terminate
• Identifies opportunities for converting lazy evaluation to strict evaluation without changing program behavior
• Bottoming functions never return a normal value, always resulting in an error or non-termination (

  error

  ,

  undefined

  )
• Strictness analysis helps optimize bottoming functions by avoiding unnecessary computations
• Improves performance by allowing earlier evaluation of strict arguments, reducing thunk creation and management overhead

Optimization Techniques

Unboxing and Strictness Optimization

• Unboxing converts boxed values (heap-allocated objects) to unboxed values (directly stored in registers or on the stack)
• Reduces memory allocation and improves cache locality by eliminating indirection
• Strictness optimization converts lazy evaluation to strict evaluation when safe to do so
• Eliminates thunk creation and management overhead for strict arguments
• Combines with unboxing to further improve performance by reducing heap allocations and memory usage

Lazy Evaluation and Thunk Management

• Lazy evaluation optimization focuses on minimizing the overhead of delayed computation
• Implements sharing to avoid redundant evaluation of the same expression multiple times
• Thunk elimination removes unnecessary delayed computations when their results are immediately needed
• Reduces memory usage by avoiding the creation of thunks for expressions that will be evaluated right away
• Balances the benefits of lazy evaluation with the performance gains of immediate computation in specific scenarios