Multi-core systems are now found in many electronic devices. But does current software design fully leverage their capabilities? The complexity of the hardware and software stacks in these platforms requires software optimization with end-to-end knowledge of the system. To optimize software performance, we must have accurate information about system behavior and time losses. Standard monitoring engines impose tradeoffs on profiling tools, making it impossible to reconcile all the expected requirements: accurate hardware views, fine-grain measurements, speed, and so on. Subsequently, new approaches have to be examined. In this article, we propose a non-intrusive, accurate tool chain, which can reveal and quantify slowdowns in low-level software mechanisms. Based on emulation, this tool chain extracts behavioral information (time, contention) through hardware side channels, without distorting the software execution flow. This tool consists of two parts. (1) An online acquisition part that dumps hardware platform signals. (2) An offline processing part that consolidates meaningful behavioral information from the dumped data. Using our tool chain, we studied and propose optimizations to MultiProcessor System on Chip (MPSoC) support in the Linux kernel, saving about 60% of the time required for the release phase of the GNU OpenMP synchronization barrier when running on a 64-core MPSoC.

中文翻译：

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用于优化 MPSoC 端到端系统的非侵入式工具链

现在在许多电子设备中都可以找到多核系统。但是当前的软件设计是否充分利用了它们的能力？这些平台中硬件和软件堆栈的复杂性需要对系统的端到端知识进行软件优化。为了优化软件性能，我们必须有关于系统行为和时间损失的准确信息。标准监控引擎对分析工具进行了权衡，因此无法协调所有预期要求：准确的硬件视图、细粒度测量、速度等。随后，必须检查新的方法。在本文中，我们提出了一种非侵入式、准确的工具链，可以揭示和量化低级软件机制的减速。该工具链基于仿真，提取行为信息（时间、争用）通过硬件侧通道，而不会扭曲软件执行流程。该工具由两部分组成。(1) 转储硬件平台信号的在线采集部分。(2) 离线处理部分，从转储数据中整合有意义的行为信息。使用我们的工具链，我们研究并提出了对 Linux 内核中多处理器片上系统 (MPSoC) 支持的优化建议，在 64 核 MPSoC 上运行时节省了 GNU OpenMP 同步屏障的发布阶段所需的大约 60% 的时间.