Multicore processors serve as target platforms in a broad variety of applications ranging from high-performance computing to embedded mobile computing and automotive applications. But, the required parallel programming opens up a huge design space of parallelization strategies each with potential bottlenecks. Therefore, an early estimation of an application’s performance is a desirable development tool. However, out-of-order execution, superscalar instruction pipelines, as well as communication costs and (shared-) cache effects essentially influence the performance of parallel programs. While offering low modeling effort and good simulation speed, current approximate analytic models provide moderate prediction results so far. Virtual prototyping requires a time-consuming simulation, but produces better accuracy. Furthermore, even existing statistical methods often require detailed knowledge of the hardware for characterization. In this work, we present a concept called Multicore Performance Evaluation Tool (MPET) and its evaluation for a statistical approach for performance prediction based on abstract runtime parameters, which describe an application’s scalability behavior and can be extracted from profiles without user input. These scalability parameters not only include information on the interference of software demands and hardware capabilities, but indicate bottlenecks as well. Depending on the database setup, we achieve a competitive accuracy of 20% mean prediction error (11% median), which we also demonstrate in a case study.

从高性能计算到嵌入式移动计算和汽车应用，多核处理器可作为广泛应用中的目标平台。但是，所需的并行编程为并行化策略打开了巨大的设计空间，每种策略都有潜在的瓶颈。因此，早期评估应用程序的性能是一种理想的开发工具。但是，乱序执行，超标量指令流水线以及通信成本和（共享的）高速缓存效应实质上会影响并行程序的性能。尽管提供的建模工作量少且仿真速度快，但目前的近似分析模型迄今仍可提供中等的预测结果。虚拟原型制作需要耗时的仿真，但会产生更高的准确性。此外，即使是现有的统计方法，通常也需要详细的硬件知识来进行表征。在这项工作中，我们提出了一个概念多核性能评估工具（MPET）及其基于抽象运行时参数的性能预测统计方法的评估，该参数描述了应用程序的可伸缩性行为，无需用户输入即可从配置文件中提取。这些可伸缩性参数不仅包括有关软件需求和硬件功能的干扰的信息，而且还指出了瓶颈。根据数据库的设置，我们获得了20％的平均预测误差（中值11％）的竞争准确性，我们还在案例研究中证明了这一点。