标题： D&A：使用多核机器在个性化PageRank计算中的资源优化 显示英文标题

标题： D&A: Resource Optimisation in Personalised PageRank Computations Using Multi-Core Machines

摘要： 资源优化常用于工作负载管理，确保在利用可用资源的情况下高效且及时地完成任务。 它旨在最小化成本，促使开发了许多针对此目的的算法。 这些技术大多数专注于在提供的资源限制内有效地调度和执行工作负载。 在本文中，我们使用另一种方法来解决这个问题。 我们提出了一种新的框架D&A，以确定在时间约束下完成工作负载所需的内核数量。 我们首先预处理一小部分查询，以推导出所需槽位的数量，从而将剩余的工作负载分配到每个槽位中。 我们在处理由随机函数引起的时间波动问题时引入了一个缩放因子。 我们进一步建立了在这种情况下所需内核数的下限，作为比较的基准。 我们通过计算涉及密集计算的个性化PageRank值来检验该框架。 我们的实验结果表明，D&A优于基准，实现了所需内核数减少38.89%至73.68%，覆盖包含数百万个顶点和边的基准数据集。 显示英文摘要

摘要： Resource optimisation is commonly used in workload management, ensuring efficient and timely task completion utilising available resources. It serves to minimise costs, prompting the development of numerous algorithms tailored to this end. The majority of these techniques focus on scheduling and executing workloads effectively within the provided resource constraints. In this paper, we tackle this problem using another approach. We propose a novel framework D&A to determine the number of cores required in completing a workload under time constraint. We first preprocess a small portion of queries to derive the number of required slots, allowing for the allocation of the remaining workloads into each slot. We introduce a scaling factor in handling the time fluctuation issue caused by random functions. We further establish a lower bound of the number of cores required under this scenario, serving as a baseline for comparison purposes. We examine the framework by computing personalised PageRank values involving intensive computations. Our experimental results show that D&A surpasses the baseline, achieving reductions in the required number of cores ranging from 38.89% to 73.68% across benchmark datasets comprising millions of vertices and edges.