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Recent advances in memory architectures have provoked renewed interest in near-data-processing (NDP) as way to alleviate the "memory wall" problem. An NDP architecture places logic circuits, such as simple processors, in close proximity to memory. Effective use of NDP architectures requires rethinking data structures and their algorithms. Here, we provide an empirical evaluation of several NDP-aware algorithms for general-purpose concurrent data structures such as linked-lists, skiplists, and FIFO queues. The empirical analysis reveals that the potential benefits of NDP-based concurrent data structures are less than what had been expected in earlier studies. In turn, we introduce lightweight NDP hardware modifications, inspired by initial observations on data access patterns and underlying DRAM activity. Even the minimal changes to hardware significantly improve the performance and energy consumption of NDP-based concurrent data structures, and in many cases, the resulting data structures outperform state-of-the-art concurrent data structures. 

Flexibility is generally agreed to be a desirable feature of a Simple Temporal Network (STN). However, exactly what flexibility attempts to measure has varied, making it difficult to objectively evaluate flexibility metrics. Further, past metrics tend to lose information or exhibit other undesirable properties when aggregating the flexibility measures of individual events across an entire STN. Our work is driven by the realization that the solution space of an STN is a convex polyhedron whose geometric properties convey useful information about the STN. These geometric inspirations lead to measures of an STN solution space and also motivate a set of desiderata for general flexibility metrics. We also put forth two new geometrically-inspired flexibility metrics that have some theoretical advantages over existing metrics. Finally, we comprehensively evaluate both new and existing flexibility metrics against our proposed desiderata.