XPS: FULL: CCA: Scalable Approximate Computing for Data Parallel Applications

数据并行应用是一类重要的问题，其中能源消耗是未来性能扩展的障碍。这些应用程序提供了一种范例，其中数千个线程在并行硬件上并发执行。其中许多应用程序共有的一个关键特征是，输出的绝对正确性不是正常操作所必需的。这为微处理器在性能和能耗与输出正确性之间进行权衡开辟了一个新的设计维度。图像和视频处理应用程序是众所周知的近似候选程序，因为用户可以容忍在视频回放期间偶尔丢失帧或分辨率的小损失。机器学习和对海量数据集的数据分析提供了在极短的时间内处理输入数据子集的机会，同时仍然产生具有代表性的结果。该项目将设计和开发支持应用程序分析、编译器和运行时软件技术，以促进可伸缩的近似计算。我们的目标是通过牺牲用户控制的小输出精度来提高效率，从而将数据并行应用程序在商用硬件上的执行效率提高一个数量级。通过创造使能技术，我们预计近似计算将变得无处不在，使数据密集型计算能够整合到生活的更多方面。推广活动将通过努力接触当地高中向新一代高中生介绍数据并行计算和能效。该项目使用垂直集成方法，结合了深入的应用程序分析、近似内核的自动生成和无缝管理近似执行的运行时系统。首先，对计算密集型移动和数据中心应用程序的深入分析将自动识别适合近似的代码区域。其次，使用成语识别和替换方法来识别常见的计算模式，并合成具有不同精度的近似版本。第三，将探索基于抽样的方法以及执行近似检查的预测策略，以确保输出质量降级不超过用户指定的阈值。最后，运行时编译器和管理层协调近似内核和错误检查的使用，同时确保遵守用户错误阈值。

Data parallel applications are an important class of problems where energy consumption is a barrier to future performance scaling. These applications provide a paradigm where thousands of threads are concurrently executed on parallel hardware. A key characteristic shared by many of these applications is that absolute correctness of the output is not essential for proper operation. This opens up a new design dimension for microprocessors to trade off performance and energy consumption with output correctness. Image and video processing applications are well known candidates for approximation as users can tolerate occasional dropped frames or small losses in resolution during video playback. Machine learning and data analysis on massive data sets provide opportunities to process subsets of input data in a fraction of the time, while still yielding representative results. This project will design and develop the enabling application analysis, compiler, and run-time software technologies to facilitate scalable approximate computing. Our goal is to increase the execution efficiency of data-parallel applications by an order of magnitude on commodity hardware by trading off small, user-controlled levels of output accuracy for increased efficiency. By creating enabling technologies, we expect approximate computing to become pervasive, enabling data-intensive computing to be integrated into more aspects of life. Outreach activities will introduce data-parallel computing and energy efficiency to a new generation of high school students through an effort to reach out to local high schools.This project uses a vertically integrated approach that combines deep application analysis, automatic generation of approximate kernels, and a run-time system that seamlessly manages approximate execution. First, deep analysis of compute-intensive mobile and datacenter applications will automatically identify code regions that are amenable to approximation. Second, an idiom recognition and replacement approach is used to identify common computation patterns and synthesize approximate versions with varying degrees of accuracy. Third, sampling-based approaches as well as predictive strategies that perform approximate checking will be explored to ensure that output quality degradation does not exceed a user-specified threshold. Finally, a run-time compiler and management layer orchestrates the usage of approximate kernels and error checking while ensuring user error thresholds are honored.