In the early days of computer science, the community settled on a simple standard model of computing and a basic canon of general purpose algorithms and data structures suited to that model. With isochronous computing, heterogeneous multiprocessors, flash memory, energy-aware computing, cache and other anisotropic memory, distributed computing, streaming environments, functional languages, graphics coprocessors, etc., the basic canon of algorithms and data structures is not enough. Software developers know of real-world constraints and new models of computation and use them to design effective algorithms and data structures. These constraints motivate the development of elegant algorithms with broad utility. As examples, we present four algorithms that were motivated by specific hardware nuances, but are generally useful: reservoir sampling, majority of a stream, B-heap, and compacting an array in $\Theta (\log n)$Θ(logn) time.

中文翻译：

---

新型计算模型的算法和数据结构

在计算机科学的早期，社区决定使用简单的计算标准模型以及适用于该模型的通用算法和数据结构的基本准则。对于同步计算，异构多处理器，闪存，能源感知计算，高速缓存和其他各向异性内存，分布式计算，流环境，功能语言，图形协处理器等，算法和数据结构的基本标准是不够的。软件开发人员了解现实世界中的约束条件和新的计算模型，并使用它们来设计有效的算法和数据结构。这些约束促使开发具有广泛用途的优雅算法。作为示例，我们介绍了四种算法，这些算法受特定的硬件细微差别的驱使，但通常很有用：油藏采样