The large-scale Multi-Agent Path Finding (MAPF) problem presents a significant challenge in combinatorial optimization. Currently, one of the advanced, near-optimal algorithms is Large Neighborhood Search (LNS), which can handle instances with thousands of agents. Although a basic portfolio parallel search based on multiple independent LNS solvers enhances speed and robustness, it encounters scalability issues with increasing CPU cores. To address this limitation, we propose the Cooperative Parallel LNS (CPLNS) algorithm, aimed at boosting parallel efficiency. The main challenge in cooperative parallel search lies in designing suitable portfolio and cooperative strategies that balance search diversification and intensification. To address this, we first analyze the characteristics of LNS. We then introduce a flexible group-based cooperative parallel strategy, where the current best solution is shared within each group to aid intensification, while maintaining diversification through independent group computations. Furthermore, we augment search diversification by integrating a simulated annealing-based LNS and bounded suboptimal single-agent pathfinding. We also introduce a rule-based methodology for portfolio construction to simplify parameter settings and improve search efficiency. Finally, we enhance communication and memory efficiency through a shared data filtering technique and optimized data structures. In benchmarks on 33 maps with 825 instances, CPLNS achieved a median speedup of 21.95 on a 32-core machine, solving 96.97% of cases within five minutes and reducing the average suboptimality score from 1.728 to 1.456. Additionally, tests with up to 10,000 agents verify CPLNS's scalability for large-scale MAPF problems.

Universal constructions are attractive as they can turn a sequential implementation of any data structure into a concurrent implementation. However, existing universal constructions have limitations, such as imposing high copying overhead, or poor scalability on NUMA systems mainly due to their lack of NUMA-aware design principles. To overcome these limitations, this paper introduces CR, a universal construction that provides highly scalable updates on NUMA systems while offering fast read-side performance. CR achieves NUMA-awareness by utilizing delegation within a NUMA node and a global shared log to maintain the consistency of replicas of data structures across nodes. Using CR does not require expertise in concurrent data structure design. Our evaluation shows that CR has up to 11.2 times better performance compared to a state-of-the-art universal construction CX on our tested sequential data structures. To demonstrate the effectiveness and applicability of CR, we have applied CR to an in-memory database system. The database shows up to 18.1 times better performance compared to the original version.

Summary Parallel program performance often critically depends on barrier performance. In modern NUMA multi‐core machines, barrier synchronization performance is significantly affected by cache‐coherence communication between cores, especially when the scale of NUMA systems is large, complex interconnected networks, memory hierarchies, and cache‐coherence protocols make optimization of barrier algorithm hard. We propose a general barrier optimization framework on NUMA multi‐core machines. The framework splits the barrier into three stages: the barrier arrival within a NUMA node, the barrier arrival across the NUMA nodes, and the wakeup, providing an opportunity to optimize the communication pattern and the cache‐line placement in each stage. To reduce remote communication traffic, we introduce a coordinator per NUMA node. In addition, we implement two barrier algorithms based on the framework. Finally, we show the superiority of the barrier algorithms within our framework over other barrier algorithms and show how to translate a barrier algorithm into a performance model to help make an optimal tradeoff design. Experiments were conducted on three NUMA multi‐core platforms and the results show that the barrier algorithm optimized within our framework is sufficient to deliver as good or better performance than state‐of‐art approaches on NUMA multi‐core machines.

Summary Modern NUMA multicore architectures exhibit complicated memory behavior, such as cache coherence invalidation and nonuniform memory access where the access from a core to its local memory is significantly faster than crossnode access to memory on a different NUMA node. The complicated memory behavior has a large impact on the efficiency of locking synchronization, which affects the performance of parallel applications. Prior works offer several efficient designs to improve locking performance such as delegation schemes. However, the existing delegation schemes either occupy computing cores or provide nonscalable performance, or offer less portability. In this work, we present a NUMA‐aware delegation lock that occupies no cores while offering scalable performance under high contention for NUMA multicore machines. The new lock is a variant of an efficient FFWD lock, and inherits its performance features, such as buffering responses within a NUMA node to minimize cache coherence traffic. Unlike FFWD, the new lock employs hierarchical NUMA‐aware memory allocation and NUMA‐aware dynamic server thread technique, to reduce crossnode communication between client and server threads. Our evaluation shows that the new lock outperforms FFWD under high contention, achieving the significant performance gains when compared with other state‐of‐the‐art locks.