Overview of Optimization
Overview of Optimization
In general, optimization involves using transformations and replacements that generate more efficient code. This is done by the compiler and involves replacements that are independent of the particular target processor’s architecture as well as replacements that take advantage of the x86 or x64 architecture, instruction set and registers. For the discussion in this and the following chapters, optimization is divided into the following categories:
Local Optimization
This optimization is performed on a block-by-block basis within a program’s basic blocks. A basic block is a sequence of statements in which the flow of control enters at the beginning and leaves at the end without the possibility of branching, except at the end. The PGI compilers perform many types of local optimization including: algebraic identity removal, constant folding, common sub-expression elimination, redundant load and store elimination, scheduling, strength reduction, and peephole optimizations.
Global Optimization
This optimization is performed on a program unit over all its basic blocks. The optimizer performs controlflow and data-flow analysis for an entire program unit. All loops, including those formed by IFs and GOTOs, are detected and optimized. Global optimization includes: constant propagation, copy propagation, dead store elimination, global register allocation, invariant code motion, and induction variable elimination.
Loop Optimization: Unrolling, Vectorization, and Parallelization
The performance of certain classes of loops may be improved through vectorization or unrolling options.
Vectorization transforms loops to improve memory access performance and make use of packed SSE instructions which perform the same operation on multiple data items concurrently. Unrolling replicates the body of loops to reduce loop branching overhead and provide better opportunities for local optimization, vectorization and scheduling of instructions. Performance for loops on systems with multiple processors may also improve using the parallelization features of the PGI compilers.
Interprocedural Analysis (IPA) and Optimization
Interprocedural analysis (IPA) allows use of information across function call boundaries to perform optimizations that would otherwise be unavailable. For example, if the actual argument to a function is in fact a constant in the caller, it may be possible to propagate that constant into the callee and perform optimizations that are not valid if the dummy argument is treated as a variable. A wide range of optimizations are enabled or improved by using IPA, including but not limited to data alignment optimizations, argument removal, constant propagation, pointer disambiguation, pure function detection, F90/F95 array shape propagation, data placement, vestigial function removal, automatic function inlining, inlining of functions from pre-compiled libraries, and interprocedural optimization of functions from pre-compiled libraries.
Function Inlining
This optimization allows a call to a function to be replaced by a copy of the body of that function. This optimization will sometimes speed up execution by eliminating the function call and return overhead. Function inlining may also create opportunities for other types of optimization. Function inlining is not always beneficial.
When used improperly it may increase code size and generate less efficient code.
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