We present the design and implementation of an automatic polyhedral source-to-source transformation framework that can optimize regular programs (sequences of possibly imperfectly nested loops) for parallelism and locality simultaneously. Through this work, we show the practicality of analytical model-driven automatic transformation in the polyhedral model -far beyond what is possible by current production compilers. Unlike previous works, our approach is an end-to-end fully automatic one driven by an integer linear optimization framework that takes an explicit view of finding good ways of tiling for parallelism and locality using affine transformations. The framework has been implemented into a tool to automatically generate OpenMP parallel code from C program sections. Experimental results from the tool show very high speedups for local and parallel execution on multi-cores over state-of-the-art compiler frameworks from the research community as well as the best native production compilers. The system also enables the easy use of powerful empirical/iterative optimization for general arbitrarily nested loop sequences.

In many scientific applications, significant time is spent tuning codes for a particular highperformance architecture. Tuning approaches range from the relatively nonintrusive (e.g., by using compiler options) to extensive code modifications that attempt to exploit specific architecture features. Intrusive techniques often result in code changes that are not easily reversible, which can negatively impact readability, maintainability, and performance on different architectures. We introduce an extensible annotation-based empirical tuning system called Orio, which is aimed at improving both performance and productivity by enabling software developers to insert annotations in the form of structured comments into their source code that trigger a number of low-level performance optimizations on a specified code fragment. To maximize the performance tuning opportunities, we have designed the annotation processing infrastructure to support both architecture-independent and architecture-specific code optimizations. Given the annotated code as input, Orio generates many tuned versions of the same operation and empirically evaluates the versions to select the best performing one for production use. We have also enabled the use of the PLuTo automatic parallelization tool in conjunction with Orio to generate efficient OpenMP-based parallel code. We describe our experimental results involving a number of computational kernels, including dense array and sparse matrix operations. MotivationThe size and complexity of scientific computations are increasing at least as fast as the improvements in processor technology. Programming such scientific applications is hard, and optimizing them for high performance is even harder. This situation results in a potentially large gap between the achieved performance of applications and the peak available performance, with many applications achieving 10% or less of the peak (see, e.g., [4]). A greater concern is the inability of existing languages, compilers, and systems to deliver the available performance for the application through fully automated code optimizations.Delivering performance without degrading productivity is crucial for the success of scientific computing. Scientific code developers generally attempt to improve performance by applying one or more of the following three approaches: manually optimizing code fragments; using tuned libraries for key numerical algorithms; and, less frequently, using compiler-based source transformation tools for loop-level optimizations. Manual tuning is time-consuming and impedes readability 1

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Tiling is a crucial loop transformation for generating high performance code on modern architectures. Efficient generation of multilevel tiled code is essential for maximizing data reuse in systems with deep memory hierarchies. Tiled loops with parametric tile sizes (not compile-time constants) facilitate runtime feedback and dynamic optimizations used in iterative compilation and automatic tuning. Previous parametric multi-level tiling approaches have been restricted to perfectly nested loops, where all assignment statements are contained inside the innermost loop of a loop nest. Previous solutions to tiling for imperfect loop nests have only handled fixed tile sizes. In this paper, we present an approach to parametric multi-level tiling of imperfectly nested loops. The tiling technique generates loops that iterate over full rectangular tiles, making them amenable to compiler optimizations such as register tiling. Experimental results using a number of computational benchmarks demonstrate the effectiveness of the developed tiling approach.

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