It has long been known that a single ordering of optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting constraints on time, code size, and power consumption. Given that many embedded application developers are willing to spend time tuning an application, we believe a viable approach is to allow the developer to steer the process of optimizing a function. In this paper, we describe support in VISTA, an interactive compilation system, for finding effective sequences of optimization phases. VISTA provides the user with dynamic and static performance information that can be used during an interactive compilation session to gauge the progress of improving the code. In addition, VISTA provides support for automatically using performance information to select the best optimization sequence among several attempted. One such feature is the use of a genetic algorithm to search for the most efficient sequence based on specified fitness criteria. We hav e included a number of experimental results that evaluate the effectiveness of using a genetic algorithm in VISTA to find effective optimization phase sequences.

Software designers face many challenges when developing applications for embedded systems. One major challenge is meeting the conflicting constraints of speed, code size and power consumption. Embedded application developers often resort to hand-coded assembly language to meet these constraints since traditional optimizing compiler technology is usually of little help in addressing this challenge. The results are software systems that are not portable, less robust and more costly to develop and maintain. Another limitation is that compilers traditionally apply the optimizations to a program in a fixed order. However, it has long been known that a single ordering of optimization phases will not produce the best code for every application. In fact, the smallest unit of compilation in most compilers is typically a function and the programmer has no control over the code improvement process other than setting flags to enable or disable certain optimization phases. This paper describes a new code improvement paradigm implemented in a system called VISTA that can help achieve the cost/performance trade-offs that embedded applications demand. The VISTA system opens the code improvement process and gives the application programmer, when necessary, the ability to finely control it. VISTA also provides support for finding effective sequences of optimization phases. This support includes the ability to interactively get

This paper presents the integration of Soot, a byte-code analysis and transformation framework, with an integrated development environment (IDE), Eclipse. Such an integrated toolkit is useful for both the compiler developer, to aid in understanding and debugging new analyses, and also for the end-user of the IDE, to aid in program understanding by exposing semantic information gathered by the advanced compiler analyses. The paper discusses these advantages and provides concrete examples of its usefulness. There are several major challenges to overcome in developing the integrated toolkit, and the paper discusses three major challenges and the solutions to those challenges. An overview of Soot and the integrated toolkit is given, followed by a more detailed discussion of the fundamental components. The paper concludes with several illustrative examples of using the integrated toolkit along with a discussion of future plans and research. 1

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There is an active research community concentrating on visualizations of algorithms taught in CS1 and CS2 courses. These visualizations can help students to create concrete visual images of the algorithms and their underlying concepts. Not only fundamental algorithms can be visualized, but also algorithms used in compilers. Visualizations that exist for use in compiler courses are mostly for the front end, though. In this article we propose the use of visualizations for understanding optimization passes. Optimization passes are complex algorithms that operate on large amounts of code and it is not obvious when, where and how often each optimization is applied to the code. We show in this article how visualizations for a procedural abstraction optimization pass can capture the effect of all instances of this optimization over an entire program to make it easier for students to comprehend procedural abstraction.

Abstract—Visualizing computational processes of optimization passes helps to reason about, and to gain insight into, the inner workings of the optimization passes. In this paper, we visualize the computational processes of two procedural abstraction passes. We modified two procedural abstraction post pass optimizers to visualize for each the difference in machine code before and after optimization by drawing abstracted fragments in the original code. We then explain how the generated visualizations aid in better understanding the optimization passes and eventually improve them. I.