In this article, we introduce a p arallelizing a pproximatio n - d isc o very f ra mework, PANDORA, for automatically discovering application- and architecture-specialized approximations of provided code. PANDORA complements existing compilers and runtime optimizers by generating approximations with a range of Pareto-optimal tradeoffs between performance and error, which enables adaptation to different inputs, different user preferences, and different runtime conditions (e.g., battery life). We demonstrate that PANDORA can create parallel approximations of inherently sequential code by discovering alternative implementations that eliminate loop-carried dependencies. For a variety of functions with loop-carried dependencies, PANDORA generates approximations that achieve speedups ranging from 2.3x to 81x, with acceptable error for many usage scenarios. We also demonstrate PANDORA’s architecture-specialized approximations via FPGA experiments, and highlight PANDORA’s discovery capabilities by removing loop-carried dependencies from a recurrence relation with no known closed-form solution.

中文翻译：

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潘多拉

在这篇文章中，我们介绍了一个p平行化一种近似n-d国际标准委员会○非常f拉mework，PANDORA，用于自动发现所提供代码的应用程序和架构专用近似值。PANDORA 通过在性能和错误之间进行一系列帕累托最优权衡来生成近似值，从而补充现有的编译器和运行时优化器，从而能够适应不同的输入、不同的用户偏好和不同的运行时条件（例如，电池寿命）。我们证明 PANDORA 可以通过发现消除循环携带依赖的替代实现来创建固有顺序代码的并行近似。对于具有循环携带依赖关系的各种函数，PANDORA 生成的近似值可实现从 2.3 倍到 81 倍的加速，并且对于许多使用场景具有可接受的误差。