Large language model (LLM) inference serving systems are essential to various LLM-based applications. As demand for LLM services continues to grow, scaling these systems to handle high request rates while meeting latency Service-Level Objectives (SLOs), referred to as effective throughput, becomes critical. However, existing systems often struggle to improve effective throughput, primarily due to a significant decline in Time To First Token (TTFT) SLO attainment. We identify two major causes of this bottleneck: (1) memory-intensive KV cache that limits batch size expansion under GPU memory constraints, and (2) rigid batch composition enforced by the default First-Come-First-Serve scheduling policy. In this paper, we introduce Apt-Serve, a scalable framework designed to enhance effective throughput in LLM inference serving. Apt-Serve features a new hybrid cache scheme that combines KV cache with a memory-efficient hidden cache for reusable input hidden state vectors, allowing large batch sizes and improving request concurrency. Based on the hybrid cache, Apt-Serve employs an adaptive runtime scheduling mechanism that dynamically optimizes batch composition. We formally define the adaptive scheduling optimization problem and propose an efficient algorithm with theoretical guarantees. Extensive evaluations on three real-world datasets and LLMs ranging from 13B to 66B parameters demonstrate that Apt-Serve achieves up to 8.8x improvement in effective throughput compared to the state-of-the-art inference serving systems.

Dynamic graphs are essential in real-world scenarios like social media and e-commerce for tasks such as predicting links and classifying nodes. Temporal Graph Neural Networks (T-GNNs) stand out as a prime solution for managing dynamic graphs, employing temporal message passing to compute node embeddings at specific timestamps. Nonetheless, the high CPU-GPU data loading overhead has become the bottleneck for efficient training of T-GNNs over large-scale dynamic graphs. In this work, we present SIMPLE, a versatile system designed to address the major efficiency bottleneck in training existing T-GNNs on a large scale. It incorporates a dynamic data placement mechanism, which maintains a small buffer space in available GPU memory and dynamically manages its content during T-GNN training. SIMPLE is also empowered by systematic optimizations towards data processing flow. We compare SIMPLE to the state-of-the-art generic T-GNN training system TGL on four large-scale dynamic graphs with different underlying T-GNN models. Extensive experimental results show that SIMPLE effectively cuts down 80.5% ~ 96.8% data loading cost, and accelerates T-GNN training by 1.8× ~ 3.8× (2.6× on average) compared to TGL.

Dynamic graphs play a crucial role in various real-world applications, such as link prediction and node classification on social media and e-commerce platforms. Temporal Graph Neural Networks (T-GNNs) have emerged as a leading approach for handling dynamic graphs, using temporal message passing to compute temporal node embeddings. However, training existing T-GNNs on large-scale dynamic graphs is prohibitively expensive due to the ill-suited batching scheme and significant data access overhead. In this paper, we introduce ETC, a generic framework designed specifically for efficient T-GNN training at scale. ETC incorporates a novel data batching scheme that enables large training batches improving model computation efficiency, while preserving model effectiveness by restricting information loss in each training batch. To reduce data access overhead, ETC employs a three-step data access policy that leverages the data access pattern in T-GNN training, significantly reducing redundant data access volume. Additionally, ETC utilizes an inter-batch pipeline mechanism, decoupling data access from model computation and further reducing data access costs. Extensive experimental results demonstrate the effectiveness of ETC, showcasing its ability to achieve significant training speedups compared to state-of-the-art training frameworks for T-GNNs on real-world dynamic graphs with millions of interactions. ETC provides a training speedup ranging from 1.6X to 62.4X, highlighting its potential for efficient training on large-scale dynamic graphs.

Graph Neural Networks (GNNs) have gained significant popularity for learning representations of graph-structured data. Mainstream GNNs employ the message passing scheme that iteratively propagates information between connected nodes through edges. However, this scheme incurs high training costs, hindering the applicability of GNNs on large graphs. Recently, the database community has extensively researched effective solutions to facilitate efficient GNN training on massive graphs. In this tutorial, we provide a comprehensive overview of the GNN training process based on the graph data lifecycle, covering graph preprocessing, batch generation, data transfer, and model training stages. We discuss recent data management efforts aiming at accelerating individual stages or improving the overall training efficiency. Recognizing the distinct training issues associated with static and dynamic graphs, we first focus on efficient GNN training on static graphs, followed by an exploration of training GNNs on dynamic graphs. Finally, we suggest some potential research directions in this area. We believe this tutorial is valuable for researchers and practitioners to understand the bottleneck of GNN training and the advanced data management techniques to accelerate the training of different GNNs on massive graphs in diverse hardware settings.

Two improved particle swarm optimization algorithms are given to overcome the defects in the commonly used particle swarm optimization. These are particle swarm optimization with nonlinear inertia weight and simulated annealing particle swarm optimization. The global search ability and local search accuracy can be optimized by introducing nonlinear inertia weight coefficients. It is well known that the particle swarm optimization has a problem that the algorithm is easily trapped into the local optimum. This paper shows that such a problem can be solved partially by combining the particle swarm optimization with simulated annealing algorithm. Autonomous moving robot path planning is given based on improved particle swarm optimization. The simulation results show the validity of the proposed improved algorithm in moving robot path planning.