Optimizing compiler

Optimizing Compiler

An optimizing compiler is a type of compiler that improves code efficiency by minimizing aspects like program execution time, memory use, storage size, and power consumption. Generally, optimization involves a series of transformations that modify code to be more efficient while keeping its functionality the same. This process, however, can be CPU and memory-intensive. Due to limitations like memory availability and the time required for compiling, only a subset of optimizations can typically be applied. Some optimization problems are known to be complex, or even impossible to solve perfectly.

Types of Optimizations

Optimization techniques can be divided into two broad categories: machine-independent optimizations and machine-dependent optimizations.

Machine-independent Optimizations

These optimizations are performed regardless of the target architecture. They focus on making the code faster, smaller, and more efficient, often improving general performance.

1. Constant Folding and Propagation Replaces expressions that evaluate to constants at compile time (e.g., 2 * 3 becomes 6).
2. Common Subexpression Elimination Identifies and removes repeated computations of the same expression.
3. Dead-code Elimination Removes instructions that do not affect the program's observable behavior.
4. Loop Invariant Code Motion Moves computations that produce the same result each time the loop is executed outside the loop to reduce unnecessary calculations.
5. Strength Reduction Substitutes expensive operations (like multiplication) with equivalent, cheaper operations (e.g., replacing i * 2 with i + i).
6. Partial Evaluation Specializes a program by evaluating expressions with known values at compile time, which reduces runtime work.
7. Inline Expansion Replaces function calls with the body of the function, particularly for small functions that are frequently called, reducing the overhead of function calls.
8. Register Allocation Assigns variables to machine registers to minimize memory access and increase performance.
9. Loop Unrolling Reduces the overhead of looping by duplicating the loop body multiple times to decrease loop control instructions.
10. Tail-call Optimization Optimizes tail-recursive functions by reusing the current function’s stack frame, converting the recursion to iteration.

Machine-dependent Optimizations

Machine-dependent optimizations take into account the characteristics of the target machine architecture, such as processor features, memory hierarchy, and instruction set.

1. Instruction Scheduling Optimizes the order of instructions to make use of pipeline and reduce bottlenecks, especially in processors with pipelines.
2. Register Allocation (Spilling and Coalescing) Allocates variables to registers or spills them to memory when there are not enough registers. Optimizing register usage can improve performance.
3. Instruction Selection Chooses the most efficient instruction for a given operation based on the target architecture.
4. Branch Prediction Optimization Optimizes branching by rearranging or predicting the branch direction to minimize pipeline stalls.
5. Memory Access Optimization Optimizes memory access patterns to improve cache locality, reducing the time spent waiting for memory accesses.

Interprocedural Optimizations

Interprocedural optimization works on the entire program, including across procedure and file boundaries. It performs analyses that involve multiple functions or procedures and tries to make global improvements in code efficiency. These optimizations often require significant additional computation, and hence are not always enabled by default in compilers.

1. Procedure Inlining Replaces calls to functions with the function body itself, reducing the overhead of function calls and improving performance, particularly for small, frequently called functions.
2. Interprocedural Constant Propagation Propagates constants across function boundaries, allowing values that are constant in one function to be known in others.
3. Interprocedural Dead-code Elimination Eliminates code that is not reachable or has no effect across multiple functions or modules.
4. Procedure Reordering Changes the order of function calls to optimize for cache and CPU efficiency.
5. Interprocedural Alias Analysis Analyzes pointer aliases across function calls to allow optimizations that assume pointers do not alias, increasing the range of possible optimizations.
6. Global Value Numbering Performs a global analysis to identify and eliminate redundant computations by recognizing the same values across different procedures.

Practical Considerations

Compilers often provide a range of optimization levels to balance the trade-off between compilation time and performance improvements. These levels range from minimal to aggressive optimizations:

1. Optimization Level Options Many compilers allow users to specify the level of optimization, from no optimization (-O0) to full optimization (-O3), depending on the trade-offs between compilation time and execution speed.
2. Post-pass Optimizers After an initial compilation, post-pass optimizers can be used to further optimize the generated machine code or assembly code. These optimizations can include instruction reordering, dead code elimination, and other low-level adjustments.
3. Fail-safe Techniques Optimization algorithms are complex and can introduce bugs. A fail-safe technique is used in compilers to catch and handle errors in the optimization logic, ensuring that the rest of the compilation proceeds successfully even if an optimization step fails.

History of Compiler Optimization

Compiler optimization began in the 1960s with early compilers focusing primarily on correctness and simplicity. The development of optimizing compilers became more prominent in the 1970s and 1980s with the introduction of advanced techniques and tools.

1. IBM FORTRAN H Compiler (1960s) One of the first optimizing compilers, primarily concerned with improving the efficiency of FORTRAN programs.
2. BLISS Compiler (1970s) The BLISS compiler was one of the first to pioneer many advanced optimization techniques that would later become common in modern compilers.
3. RISC Architecture and Compiler Optimization By the late 1980s, optimizing compilers were well-developed, with the rise of RISC (Reduced Instruction Set Computing) architectures and advanced processor features designed to be targeted by optimizing compilers.
4. Modern Compiler Advances In the 2000s and beyond, commercial compilers such as GCC, Clang, and Microsoft's Visual C++ have continued to evolve with sophisticated optimization techniques, including interprocedural analysis, vectorization, and parallelization optimizations.

List of static code analyses

• Alias analysis
• Pointer analysis
• Shape analysis
• Escape analysis
• Array-access analysis
• Dependence analysis
• Control-flow analysis
• Data-flow analysis
  • Use-define chain analysis
  • Live-variable analysis
  • Available expression analysis

See also

• Algorithmic efficiency
• Compile-time function execution
• Full-employment theorem
• Just-in-time compilation (JIT)
• Kildall's method
• Profile-guided optimization
• Program optimization

External links

• Optimization manuals by Agner Fog – documentation about x86 processor architecture and low-level code optimization