Embedded systems and Internet of Things (IoT) applications motivate in-place parallel algorithms, which avoid allocating additional shared memory past the input. Work by Gu, Obeya, and Shun [APOCS '21] defines a family of PIP (parallel in-place) models and parallel algorithms that eschew auxiliary memory at high processor counts while remaining in-situ when run sequentially. However, their models assume asynchronous processing and have no in-place guarantees for intermediate processor counts. We address this gap in the literature by proposing a Synchronous PIP family of models for in-place parallel and distributed computation. We demonstrate the effectiveness of our new model by giving efficient and synchronous parallel algorithms in this model that require no auxiliary shared memory and only constant private memory per processor. Importantly, we show how to leverage a new parallel-augmented sweep technique to ensure that Synchronous PIP algorithms remain efficient and strictly in-place at all processor counts.

翻译：嵌入式系统和物联网应用推动了原位并行算法的发展，这类算法避免在输入之外分配额外的共享内存。Gu、Obeya 和 Shun 在 APOCS '21 中的工作定义了一类 PIP（并行原位）模型和并行算法，这些算法在高处理器数下无需辅助内存，且在顺序执行时保持原位特性。然而，他们的模型假设异步处理，且对中等处理器数缺乏原位保证。我们通过提出用于原位并行与分布式计算的同步 PIP 模型系列，弥补了文献中的这一空白。我们通过在该模型中设计高效且同步的并行算法，展示了新模型的有效性，这些算法无需额外共享内存，且每个处理器仅需常量私有内存。重要的是，我们展示了如何利用一种新的并行增强扫描技术，确保同步 PIP 算法在所有处理器数下均保持高效且严格原位。