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An optimizing compiler is a compiler designed to generate code that is optimized in aspects such as minimizing program execution time, memory use, storage size, and power consumption. Optimization is generally implemented as a sequence of optimizing transformations, algorithms that transform code to produce semantically equivalent code optimized for some aspect. It is typically CPU and memory intensive. In practice, factors such as available memory and a programmer's willingness to wait for compilation limit the optimizations that a compiler might provide. Research indicates that some optimization problems are NP-complete, or even undecidable.
Standard ML (SML) is a general-purpose, high-level, modular, functional programming language with compile-time type checking and type inference. It is popular for writing compilers, for programming language research, and for developing theorem provers.
In computer architecture, predication is a feature that provides an alternative to conditional transfer of control, as implemented by conditional branch machine instructions. Predication works by having conditional (predicated) non-branch instructions associated with a predicate, a Boolean value used by the instruction to control whether the instruction is allowed to modify the architectural state or not. If the predicate specified in the instruction is true, the instruction modifies the architectural state; otherwise, the architectural state is unchanged. For example, a predicated move instruction will only modify the destination if the predicate is true. Thus, instead of using a conditional branch to select an instruction or a sequence of instructions to execute based on the predicate that controls whether the branch occurs, the instructions to be executed are associated with that predicate, so that they will be executed, or not executed, based on whether that predicate is true or false.
In computing, inline expansion, or inlining, is a manual or compiler optimization that replaces a function call site with the body of the called function. Inline expansion is similar to macro expansion, but occurs during compilation, without changing the source code, while macro expansion occurs prior to compilation, and results in different text that is then processed by the compiler.
D, also known as dlang, is a multi-paradigm system programming language created by Walter Bright at Digital Mars and released in 2001. Andrei Alexandrescu joined the design and development effort in 2007. Though it originated as a re-engineering of C++, D is now a very different language. As it has developed, it has drawn inspiration from other high-level programming languages. Notably, it has been influenced by Java, Python, Ruby, C#, and Eiffel.
OpenMP is an application programming interface (API) that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran, on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.
The syntax of the C programming language is the set of rules governing writing of software in C. It is designed to allow for programs that are extremely terse, have a close relationship with the resulting object code, and yet provide relatively high-level data abstraction. C was the first widely successful high-level language for portable operating-system development.
In computer programming, undefined behavior (UB) is the result of executing a program whose behavior is prescribed to be unpredictable, in the language specification of the programming language in which the source code is written. This is different from unspecified behavior, for which the language specification does not prescribe a result, and implementation-defined behavior that defers to the documentation of another component of the platform.
Loop splitting is a compiler optimization technique. It attempts to simplify a loop or eliminate dependencies by breaking it into multiple loops which have the same bodies but iterate over different contiguous portions of the index range.
In computer science and particularly in compiler design, loop nest optimization (LNO) is an optimization technique that applies a set of loop transformations for the purpose of locality optimization or parallelization or another loop overhead reduction of the loop nests. One classical usage is to reduce memory access latency or the cache bandwidth necessary due to cache reuse for some common linear algebra algorithms.
In computer programming, loop-invariant code consists of statements or expressions that can be moved outside the body of a loop without affecting the semantics of the program. Loop-invariant code motion is a compiler optimization that performs this movement automatically.
Cilk, Cilk++, Cilk Plus and OpenCilk are general-purpose programming languages designed for multithreaded parallel computing. They are based on the C and C++ programming languages, which they extend with constructs to express parallel loops and the fork–join idiom.
Loop unrolling, also known as loop unwinding, is a loop transformation technique that attempts to optimize a program's execution speed at the expense of its binary size, which is an approach known as space–time tradeoff. The transformation can be undertaken manually by the programmer or by an optimizing compiler. On modern processors, loop unrolling is often counterproductive, as the increased code size can cause more cache misses; cf. Duff's device.
In computer programming, a nested function is a named function that is defined within another, enclosing, block and is lexically scoped within the enclosing block – meaning it is only callable by name within the body of the enclosing block and can use identifiers declared in outer blocks, including outer functions. The enclosing block is typically, but not always, another function.
In computer programming languages, a switch statement is a type of selection control mechanism used to allow the value of a variable or expression to change the control flow of program execution via search and map.
In compiler theory, loop optimization is the process of increasing execution speed and reducing the overheads associated with loops. It plays an important role in improving cache performance and making effective use of parallel processing capabilities. Most execution time of a scientific program is spent on loops; as such, many compiler optimization techniques have been developed to make them faster.
Automatic vectorization, in parallel computing, is a special case of automatic parallelization, where a computer program is converted from a scalar implementation, which processes a single pair of operands at a time, to a vector implementation, which processes one operation on multiple pairs of operands at once. For example, modern conventional computers, including specialized supercomputers, typically have vector operations that simultaneously perform operations such as the following four additions :
In computer science, loop fission is a compiler optimization in which a loop is broken into multiple loops over the same index range with each taking only a part of the original loop's body. The goal is to break down a large loop body into smaller ones to achieve better utilization of locality of reference. This optimization is most efficient in multi-core processors that can split a task into multiple tasks for each processor.
The polyhedral model is a mathematical framework for programs that perform large numbers of operations -- too large to be explicitly enumerated -- thereby requiring a compact representation. Nested loop programs are the typical, but not the only example, and the most common use of the model is for loop nest optimization in program optimization. The polyhedral method treats each loop iteration within nested loops as lattice points inside mathematical objects called polyhedra, performs affine transformations or more general non-affine transformations such as tiling on the polytopes, and then converts the transformed polytopes into equivalent, but optimized, loop nests through polyhedra scanning.
In computer science, an enabling transformation is a compiler optimization that increases the effectiveness of other compiler optimizations. Such an optimization may or may not improve program performance by itself, but it also alters the structure of the program in such a way that other optimizations may produce superior results. Typical enabling transformations include:
References
- ↑ Cooper, Keith; Torczon, Linda (2004). Engineering a Compiler. Elsevier. ISBN 9781558606982.
- ↑ "GCC 3.4 Release Series — Changes, New Features, and Fixes - GNU Project".