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They try to recognize special cases that have efficient implementations and rewrite the code to realize those savings. The transformations in an optimizer try to remove the overhead introduced in support of source-language abstractions, including data structures, control structures, and error checking. Optimizations improve performance by tailoring general translation schemes to the specific details of the code at hand. This chapter has shown a variety of techniques that operate over scopes that range from single basic blocks through entire programs. While most optimizations try to improve runtime speed, optimizations can also target other measures, such as code size or energy consumption.
The optimizer in a modern compiler contains a collection of techniques that try to improve the performance of the compiled code. Cooper, Linda Torczon, in Engineering a Compiler (Second Edition), 2012 8.8 Summary and Perspective At a minimum, there should be a user-specified bias toward either execution time or memory usage. As a result, fine control over the optimization process is essential. Manufacturers of development tools for embedded systems have very limited knowledge of the architecture of individual configurations-every system is unique. This example gives a compiler a distinct advantage over a human assembler code writer. Since the compiler can “rewrite” the code each time it is run, efficiency rather than future flexibility can be the sole priority. It may even be efficient to generate a table with dummy entries if the constants are not quite contiguous. Explicit tests, lookup tables, or indexed jump tables are all possible, depending upon the number and contiguousness of the case constants. Depending upon the values and sequence of the case constants, quite different code-generation techniques may be appropriate. In C, switch statements lend themselves to optimal code generation. The optimization can yield very dramatic improvements in runtime performance. Some compilers require specific functions to be nominated for this treatment, but automatic selection by the compiler is preferable. This is useful to maximize execution speed of the compiled code.
The inlining of functions is the procedure whereby the actual code of a (small) function is included instead of a call. However, CISC devices can benefit from such a treatment. This technique is a common requirement for getting the best out of RISC architectures. Instruction scheduling is a mechanism by which instructions are presented to the microprocessor in a sequence that ensures optimal usage of the CPU. These include instruction scheduling, function inlining, and switch statement tuning. More interesting are those that take specific advantage of the architectural characteristics of a specific microprocessor. Many optimizations may be applied in a generalized way, regardless of the target. Optimizations can be local to a function or global across a whole module.
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These optimization techniques can result in software that rivals handcrafted assembler code for size and efficiency. Since the Interpolate() function is not an inline function, the compiler takes a conservative approach and decides to not vectorize the loop.Ĭolin Walls, in Embedded Software (Second Edition), 2012 2.2.4 OptimizationsĪll modern compilers make use of optimization techniques to generate good-quality code. In this particular situation, the compiler’s auto-vectorization algorithm needs to rely on code inlining to resolve trip counts along with potential data, pointer, and loop dependencies. While the message highlighted in boldface in the report is somewhat obscure to non-compiler savvy developers, it does tell us that the compiler cannot resolve all the dependencies associated with the Interpolate() function. Remark #15344: loop was not vectorized: vector dependence prevents Interpolate(double, const point *) cannot be vectorized Remark #15382: vectorization support: call to function
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The compiler switch to get these reports on Linux is –qopt-report = 5 –qopt-report-phase = vec and on Windows /Qopt-report = 5 /Qopt-report-phase = vec. The following vector report shows that the critical loop in Figure 22.3 was not vectorized because the compiler could not determine if the call to the Interpolate() function was safe. Most modern compilers are able to apply auto-vectorization algorithms that can decide if a loop can be vectorized, and when appropriate, perform the vectorization. Unoptimized C source code for a linear spline.