Compiler optimization pdf
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Compiler optimization pdf
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Can’t change meaning of program to behavior not allowed by source. Ref: Principle of Compiler Design,, Rabi Se thi, •Optimization are code transformations: •They can be applied at any stage of the compiler •They must be safe – they shouldn’t change the meaning of the program Compile clean, modular, high-level source code to expert assembly-code performance. The first are simple re-writing transformations that remove dependence arcs Dependence graphs can be used as a vehicle for formulating and implementing compiler optimizations. •Can be done at intermediate language representation and at assembly level •Local optimizations at assembly level called peephole optimizations •Examine some small set of instructions and replace with different set •Often very machine specific In this paper, we describe the relationship between machine learning and compiler optimization and introduce the main concepts of features, models, training, and deployment. Different goals We introduce CompilerGym, a Python library that for mulates compiler optimization problems as easy-to-use Gym [7] environments with a simple API. We provide Compiler infrastructures such as LLVM provide reusable and general-purpose compiler optimizations, but these optimizations differ from superoptimization. We are the first to systematically mitigate the prob-lem of compiler instability through a learned model that predicts source-to-source transformations likely to make compiler optimizations more efective Ingredients in a Compiler Optimization Formulate optimization problem –Identify opportunities of optimization •applicable across many programs •affect key parts of the program (loops/recursions) •amenable to “efficient enough” algorithm Representation –Must abstract essential details relevant to optimization Analysis We introduce CompilerGym, a Python library that for mulates compiler optimization problems as easy-to-use Gym [7] environments with a simple API. We provide environments for three compiler optimization problems: LLVM phase ordering, GCC flag selection, and CUDA loop nest generation When to Perform Local Optimization? Where compiler Moving up a level of abstraction, compiler development teams are faced with many demands on their time—fixing bugs, supporting new language features, supporting new In this paper we discuss the various techniques of obtaining better compiler optimization by using different cache optimization techniques, dynamic optimization techniques as In this paper, we describe the relationship between machine learning and compiler optimization and introduce the main concepts of features, models, training, and compiler optimization chandler carruthmeetingcppunderstanding performance When to Perform Local Optimization? This paper defines such graphs and discusses two kinds of transformations. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas We construct LLMs solely for the purpose of compiler optimization and show that they achieve a single-compile % improvement in code size reduction over the compiler versus a search-based approach which achieves % with e9 compilations and versus state of the state-of-the-art ML approaches that cause regressions and require thousands of c Abstract. •Can be done at intermediate language representation and at assembly level •Local optimizations at assembly level called Optimization of basic Blocks, Loops in flow graph, Introduction to Global data flow analysis. time optimization: reduce execution time. Different goals: space optimization: reduce memory use. What Do Compilers Do?Translate one language into another –e.g., convert C++ into xobject code –difficult for “natural” languages, but feasible for computer languages Compile clean, modular, high-level source code to expert assembly-code performance. Can’t change meaning of program to behavior not allowed by source. power optimization: reduce power usage Learning-based approach to mitigate compiler instabil-ity.
