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Electron micrographs of clay minerals, Volume 31 Developments in Sedimentology. Encyclopedia of National Anthems. Evolution of the Brain: Creation of the Self. Exploring Tort Law. Grid Computing. Par milburn frank le jeudi, septembre 1 , - Lien permanent. English Grammar Book. Compilers - Principles, Techniques, and Tools 2e. Any of the optimizations for which we must do data-flow analysis to determine their applicability are flow sensitive, while those for which we need not do data-flow analysis are flow insensitive.

The may vs. Flow-insensitive problems can be solved by solving subproblems and then combining their solutions to provide a solution for the whole problem, independent of control flow. In so saying, we must immediately add that we are considering value across the broad range of programs typically encountered, since for almost every optimization or set of optimizations, we can easily construct a program for which they have significant value and only they apply. Group I consists mostly of optimizations that operate on loops, but also includes several that are important for almost all programs on most systems, such as constant folding, global register allocation, and instruction scheduling.

Group I consists of 1. In general, we recommend that partial-redundancy elimination see Section On the other hand, the combination of commonsubexpression elimination and loop-invariant code motion involves solving many fewer systems of data-flow equations, so it may be a more desirable approach if speed of compilation is an issue and if not many other optimizations are being performed. Group III consists of optimizations that apply to whole procedures and others that increase the applicability of other optimizations, namely, 1.

Order and Repetition of Optimizations additional control-flow optimizations straightening, if simplification, unswitching, and conditional moves.

Finally, group IV consists of optimizations that save code space but generally do not save time, namely, 1. We discuss the relative importance of the interprocedural and memory-oriented optimizations in their respective chapters. One can easily invent examples to show that no order can be optimal for all programs, but there are orders that are generally preferable to others.

Other choices for how to order optimizations can be found in the industrial compiler descriptions in Chapter The optimizations in box A are best performed on a high-level intermediate language such as h ir and both require the information provided by dependence analysis.

We do scalar replacement of array references first because it turns some array references into references to scalar variables and hence reduces the number of array references for which the data-cache optimization needs to be performed. Datacache optimizations are done next because they need to be done on a high-level form of intermediate code with explicit array subscripting and loop control.

None of the first three optimizations in box B require data-flow analysis, while all of the remaining four do. Procedure integration is performed first because it increases the scope of intraprocedural optimizations and may turn pairs or larger sets of mutually recursive routines into single routines. Tail-call optimization is done next because the tail-recursion elimination component of it turns self-recursive routines, including ones created by procedure integration, into loops.

Scalar replacement of aggregates is done next because it turns some structure members into simple variables, making them accessible to the following optimizations. Interprocedural constant propagation is done next because it may benefit from the preceding phase of intraprocedural constant propagation and FIG. Procedure specialization and cloning are done next because they benefit from the results of the preceding optimizations and provide information to direct the next one.

Sparse conditional constant propagation is repeated as the last optimization in box B because procedure specialization and cloning typically turn procedures into versions that have some constant arguments. Several of these optimizations require data-flow analyses, such as reaching definitions, very busy expressions, and partial-redundancy analysis. Global Section Note that this ordering makes it desirable to perform copy propagation on code in SSA form, since the optimizations before and after it require SSA-form code.

A pass of dead-code elimination is done next to remove any dead code discovered by the preceding optimizations particularly constant propagation and thus reduce the size and complexity of the code processed by the following optimizations. Next we do redundancy elimination, which may be either the pair consisting of local and global common-subexpression elimination and loop-invariant code motion box C2 or partial-redundancy elimination box C3.

Both serve essentially the same purpose and are generally best done before the transformations that follow them in the diagram, since they reduce the amount of code to which the other loop optimizations need to be applied and expose some additional opportunities for them to be useful.

Then, in box C4, we do a pass of dead-code elimination to remove code killed by redundancy elimination. Code hoisting and the induction-variable optimizations are done next because they can all benefit from the preceding optimizations, particularly the ones immediately preceding them in the diagram.

Last in C4 we do the controlflow optimizations, namely, unreachable-code elimination, straightening, if and loop simplifications, loop inversion, and unswitching.

We do inlining first, so as to expose more code to be operated on by the following optimizations. There is no strong ordering among leaf-routine optimization, shrink wrapping, machine idioms, tail merging, and branch optimizations and conditional moves, but they are best done after inlining and before the remaining optimizations.

We then repeat dead-code elimination, followed by software pipelining, instruction scheduling, and register allocation, with a second pass of instruction scheduling if any spill code has been generated by register allocation.

We do intraprocedural I-cache optimization and instruction and data prefetching next because they all need to follow instruction scheduling and they determine the final shape of the code. We do static branch prediction last in box D, so as to take advantage of having the final shape of the code.

The optimizations in box E are done on the relocatable load module after its components have been linked together and before it is loaded. All three require that we have the entire load module available. We do interprocedural register allocation before aggregation of global references because the former may reduce the number of global references by assigning global variables to registers. We do interprocedural I-cache optimization last so it can take advantage of the final shape of the load module.

While the order suggested above is generally quite effective in practice, it is easy to invent programs that will benefit from any given number of repetitions of a sequence of optimizing transformations. We leave doing so as an exercise for the reader.

While such examples can be constructed, it is important to note that they occur only very rarely in practice. It is usually sufficient to apply the transformations that make up an optimizer once, or at most twice, to get all or almost all the benefit one is likely to derive from them.

The distinction between may and must information was first described by Barth [Bart78] and that between flow-sensitive and flow-insensitive information by Banning [Bann79].

What conclusions can be drawn from this article regarding the relevance of profiling to actual use of programs? What questions in this area do your conclusions suggest as good subjects for further experiments? The first three are independent of data-flow analysis, i. It is a relatively simple transformation to perform, in most cases. In its simplest form, constantexpression evaluation involves determining that all the operands in an expression are constant-valued, performing the evaluation of the expression at compile time, and replacing the expression by its value.

For Boolean values, this optimization is always applicable. For integers, it is almost always applicable—the exceptions are cases that would produce run-time exceptions if they were executed, such as divisions by zero and overflows in languages whose semantics require overflow detection.

Reassociation refers to using specific algebraic properties—namely, associativity, commutativity, and distributivity—to divide an expression into parts that are constant, loop-invariant i.

We present most of our examples in source code rather than in m ir , simply because they are easier to understand as source code and because the translation to mir is generally trivial.

The most obvious algebraic simplifications involve combining a binary operator with an operand that is the algebraic identity element for the operator or with an operand that always yields a constant, independent of the value of the other operand. For bit-field values, rules similar to those for Booleans apply, and others apply for shifts as well. Suppose f has a bit-field value whose length is Algebraic simplifications may also apply to relational operators, depending on the architecture being compiled for.

For example, on a machine with condition Early Optimizations codes, testing i 1. It associates a symbolic value with each computation without interpreting the operation performed by the computation, but in such a way that any two computations with the same symbolic value always compute the same value.

However, value numbering is, in fact, incomparable with the three others. In Figure Thus, we have shown that there are cases where value numbering is more powerful than any of the three others and cases where each of them is more powerful than value numbering. As we shall see in Section The original formulation of value numbering operated on individual basic blocks. Then the value graph is identical to the one in Figure Alpern, Wegman, and Zadeck discuss a series of generalizations of this approach to global value numbering, including the following: 1.

As a second exam ple, consider the program in Figure Figure It is only a matter of convenience that we have used nodes that contain a single statement each rather than basic blocks. The algorithm can easily be adapted to use basic blocks— it only requires, for example, that we identify definition sites of variables by the block number and the position within the block. The time complexity of sparse conditional constant propagation is bounded by the number of edges in the flowgraph plus the number of SSA edges, since each Section This is quadratic in the number of nodes in the worst case, but it is almost always linear in practice.

The last three begin the study of optimizations that depend on data-flow information for their effectiveness and correctness. We summarize the topics and their significance in the optimization process as follows: 1.

Scalar replacement of aggregates is best performed very early in the compilation process because it turns structures that are not usually subject to optimization into scalars that are. Algebraic simplifications and reassociation, like constant folding, are best structured as a subroutine that can be invoked as needed.

Algebraic simplification of addressing expressions and the other optimizations that apply to them, such as loop-invariant code motion if they occur in loops, are among the most important optimizations for a large class of programs.

It differs from all the other optimizations that require data-flow analysis in that it performs a somewhat more FIG. The ones discussed in this chapter are highlighted in bold type. Both global value numbering and sparse conditional constant propagation are performed on flowgraphs in SSA form and derive considerable benefit from using this form — in essence, the form er is global because o f its use and the second is more powerful than traditional global constant propagation because of it.

These optim izations are highlighted in bold type. For an example of a compiler that performs scalar replacement of aggregates, see [Much91].

From the Foreword by Susan L. Graham: This book takes on the challenges of contemporary languages and architectures, and prepares the reader for the new compiling problems that will inevitably arise in the future. The implementatio book on advanced compiler design This comprehensive, up-to-date work examines advanced issues in the design and implementation of compilers for mo. The definitive book on advanced compiler design This comprehensive, up-to-date work examines advanced issues in the design and implementation of compilers for modern processors. Written for professionals and graduate students, the book implenentation readers in designing and implementing efficient structures for highly optimizing compilers for real-world languages. Covering advanced issues in fundamental areas of compiler design, black holes and time warps pdf free download book discusses a wide array of possible code optimizations, determining- all of me instrumental mp3 download free, ustad amjad ali khan mp3 free download Advanced Compiler Design and Implementation pdfAdvanced Compiler Design and ImplementationAdvanced compiler design and implementation