We describe LLVM (low level virtual machine), a compiler framework designed to support transparent, lifelong program analysis and transformation for arbitrary programs, by providing high-level information to compiler transformations at compile-time, link-time, run-time, and in idle time between runs. LLVM defines a common, low-level code representation in static single assignment (SSA) form, with several novel features: a simple, language-independent type-system that exposes the primitives commonly used to implement high-level language features; an instruction for typed address arithmetic; and a simple mechanism that can be used to implement the exception handling features of high-level languages (and setjmp/longjmp in C) uniformly and efficiently. The LLVM compiler framework and code representation together provide a combination of key capabilities that are important for practical, lifelong analysis and transformation of programs. To our knowledge, no existing compilation approach provides all these capabilities. We describe the design of the LLVM representation and compiler framework, and evaluate the design in three ways: (a) the size and effectiveness of the representation, including the type information it provides; (b) compiler performance for several interprocedural problems; and (c) illustrative examples of the benefits LLVM provides for several challenging compiler problems.

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LLVM: a compilation framework for lifelong program analysis & transformation

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.

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The program dependence graph and its use in optimization

J+=j

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.

/pdf/efficiently-computing-static-single-assignment-form-and-the-3anyy9zi2s.pdf

Efficiently computing static single assignment form and the control dependence graph

A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest Such a point of interest is referred to as a slicing criterion, and is typically specified by a location in the program in combination with a subset of the program’s variables The task of computing program slices is called program slicing The original definition of a program slice was presented by Weiser in 1979 Since then, various slightly different notions of program slices have been proposed, as well as a number of methods to compute them An important distinction is that between a static and a dynamic slice Static slices are computed without making assumptions regarding a program’s input, whereas the computation of dynamic slices relies on a specific test case This survey presents an overview of program slicing, including the various general approaches used to compute slices, as well as the specific techniques used to address a variety of language features such as procedures, unstructured control flow, composite data types and pointers, and concurrency Static and dynamic slicing methods for each of these features are compared and classified in terms of their accuracy and efficiency Moreover, the possibilities for combining solutions for different features are investigated Recent work on the use of compiler-optimization and symbolic execution techniques for obtaining more accurate slices is discussed The paper concludes with an overview of the applications of program slicing, which include debugging, program integration, dataflow testing, and software maintenance

/pdf/a-survey-of-program-slicing-techniques-1u546ok1jr.pdf

A Survey of Program Slicing Techniques.

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.

/pdf/an-efficient-method-of-computing-static-single-assignment-2idjcmefuh.pdf

An efficient method of computing static single assignment form

We present a method that combines a deep analysis of program dependences with a broad analysis of the interaction among procedures. The method is more efficient than existing methods: we reduce many tests, performed separately by existing methods, to a single test. The method is more precise than existing methods with respect to references to multi-dimensional arrays and dependence information hidden by procedure calls. The method is more general than existing methods: we accommodate potentially aliased variables and structures of differing shapes that share storage. We accomplish the above through a unified approach that integrates subscript analysis with aliasing and interprocedural information.

Interprocedural dependence analysis and parallelization

In this paper, we present an algorithm that constructs sparse evaluation graphs for forward or backward monotone data flow problems. The sparse graph combines information as early as possible, yet directly connects nodes that generate and use information. This allows problems from the large, general class of monotone data flow problems to err joy the advantages of solutions based on Static Single Assignment (SSA) form.

Automatic construction of sparse data flow evaluation graphs

PTRAN (Parallel TRANslator) is a system for automatically restructuring sequential FORTRAN programs for execution on parallel architectures. This paper describes PTRAN-A: the currently operational analysis phase of PTRAN. The analysis is both broad and deep, incorporating interprocedural information into dependence analysis. The system is organized around a persistent database of program and procedure information. PTRAN incorporates several new, fast algorithms in a pragmatic design.

Ron Cytron

Papers

Efficiently computing static single assignment form and the control dependence graph

An efficient method of computing static single assignment form

Interprocedural dependence analysis and parallelization

Automatic construction of sparse data flow evaluation graphs

An overview of the PTRAN analysis system for multiprocessing