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Control flow graph

About: Control flow graph is a research topic. Over the lifetime, 2391 publications have been published within this topic receiving 42765 citations.


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[...]

TL;DR: An intermediate program representation, called the program dependence graph (PDG), that makes explicit both the data and control dependences for each operation in a program, allowing transformations to be triggered by one another and applied only to affected dependences.
Abstract: In this paper we present an intermediate program representation, called the program dependence graph (PDG), that makes explicit both the data and control dependences for each operation in a program. Data dependences have been used to represent only the relevant data flow relationships of a program. Control dependences are introduced to analogously represent only the essential control flow relationships of a program. Control dependences are derived from the usual control flow graph. Many traditional optimizations operate more efficiently on the PDG. Since dependences in the PDG connect computationally related parts of the program, a single walk of these dependences is sufficient to perform many optimizations. The PDG allows transformations such as vectorization, that previously required special treatment of control dependence, to be performed in a manner that is uniform for both control and data dependences. Program transformations that require interaction of the two dependence types can also be easily handled with our representation. As an example, an incremental approach to modifying data dependences resulting from branch deletion or loop unrolling is introduced. The PDG supports incremental optimization, permitting transformations to be triggered by one another and applied only to affected dependences.

2,517 citations

Journal ArticleDOI

[...]

TL;DR: In this article, the authors present new algorithms that efficiently compute static single assignment forms and control dependence graphs for arbitrary control flow graphs using the concept of {\em dominance frontiers} and give analytical and experimental evidence that these data structures are usually linear in the size of the original program.
Abstract: In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

2,105 citations

Journal ArticleDOI

[...]

Mark N. Wegman1, F. Kenneth Zadeck1
TL;DR: Four algorithms, all conservitive in the sense that all constants may not be found, but each constant found is constant over all possible executions of the program, are presented.
Abstract: Constant propagation is a well-known global flow analysis problem. The goal of constant propagation is to discover values that are constant on all possible executions of a program and to propagate these constant values as far foward through the program as possible. Expressions whose operands are all constants can be evaluated at compile time and the results propagated further. Using the algorithms presented in this paper can produce smaller and faster compiled programs. The same algorithms can be used for other kinds of analyses (e.g., type of determination). We present four algorithms in this paper, all conservitive in the sense that all constants may not be found, but each constant found is constant over all possible executions of the program. These algorithms are among the simplest, fastest, and most powerful global constant propagation algorithms known. We also present a new algorithm that performs a form of interprocedural data flow analysis in which aliasing information is gathered in conjunction with constant progagation. Several variants of this algorithm are considered.

525 citations

Proceedings ArticleDOI

[...]

Ron Cytron1, Jeanne Ferrante1, Barry K. Rosen1, Mark N. Wegman1, F. K. Zadeck2 
03 Jan 1989
TL;DR: This paper presents strong evidence that static single assignment form and the control dependence graph can be of practical use in optimization, and presents a new algorithm that efficiently computes these data structures for arbitrary control flow graph.
Abstract: In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point where advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present a new algorithm that efficiently computes these data structures for arbitrary control flow graph We also give analytical and experimental evidence that they are usually {\em linear} in the size of the original program. This paper thus presents strong evidence that these structures can be of {\em practical} use in optimization.

461 citations

Journal ArticleDOI

[...]

TL;DR: Algorithms for inserting monitoring code to profile and trace programs and show that edge profiling with edge counters works well in practice because it is simple and efficient and finds optimal counter placements in most cases.
Abstract: This paper describes algorithms for inserting monitoring code to profile and trace programs. These algorithms greatly reduce the cost of measuring programs with respect to the commonly used technique of placing code in each basic block. Program profiling counts the number of times each basic block in a program executes. Instruction tracing records the sequence of basic blocks traversed in a program execution. The algorithms optimize the placement of counting/tracing code with respect to the expected or measured frequency of each block or edge in a program's control-flow graph. We have implemented the algorithms in a profiling/tracing tool, and they substantially reduce the overhead of profiling and tracing.We also define and study the hierarchy of profiling problems. These problems have two dimensions: what is profiled (i.e., vertices (basic blocks) or edges in a control-flow graph) and where the instrumentation code is placed (in blocks or along edges). We compare the optimal solutions to the profiling problems and describe a new profiling problem: basic-block profiling with edge counters. This problem is important because an optimal solution to any other profiling problem (for a given control-flow graph) is never better than an optimal solution to this problem. Unfortunately, finding an optimal placement of edge counters for vertex profiling appears to be a hard problem in general. However, our work shows that edge profiling with edge counters works well in practice because it is simple and efficient and finds optimal counter placements in most cases. Furthermore, it yields more information than a vertex profile. Tracing also benefits from placing instrumentation code along edges rather than on vertices.

409 citations

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202227
202156
202098
201999
2018113