Graph partitioning is of great importance to optimizing the performance and cost of geo-distributed graph analytics applications. However, it is non-trivial to obtain efficient and effective partitioning due to the challenges brought by thelarge graph scales,dynamic graph changesand thenetwork heterogeneityin geo-distributed data centers (DCs). Existing studies usually adopt heuristic-based methods to achieve fast and balanced partitioning for large graphs, which are not powerful enough to address the complexity in our problem. Further, graph structures of many applications can change at various frequencies. Dynamic partitioning methods usually focus on achieving low latency to quickly adapt to changes, which unfortunately sacrifices partitioning effectiveness. Also, such methods are not aware of the dynamicity of graphs and can over sacrifice effectiveness for unnecessarily low latency. To address the limitations of existing studies, we proposeDistRLCut, a novel graph partitioner which leverages Multi-Agent Reinforcement Learning (MARL) to solve the complexity of the partitioning problem. To achieve fast partitioning for large graphs,DistRLCutadapts MARL to a distributed implementation which significantly accelerates the learning process. Further,DistRLCutincorporates two techniques to trade-off between partitioning effectiveness and efficiency, including local training and agent sampling. By adaptively tuning the number of local training iterations and the agent sampling rate,DistRLCutis able to achieve good partitioning results within an overhead constraint required by graph dynamicity. Experiments using real cloud DCs and real-world graphs show that, compared to state-of-the-art static partitioning methods,DistRLCutimproves the performance of geo-distributed graph analytics by 11%-95%.DistRLCutcan partition over 28 million edges per second, showcasing its scalability for large graphs. With varying graph changing frequencies,DistRLCutcan improve the performance by up to 71% compared to state-of-the-art dynamic partitioning.

Deep Learning Recommendation Models (DLRMs) have gained popularity in recommendation systems due to their effectiveness in handling large-scale recommendation tasks. The embedding layers of DLRMs have become the performance bottleneck due to their intensive needs on memory capacity and memory bandwidth. In this paper, we propose UpDLRM, which utilizes real-world processing-in-memory (PIM) hardware, UPMEM DPU, to boost the memory bandwidth and reduce recommendation latency. The parallel nature of the DPU memory can provide high aggregated bandwidth for the large number of irregular memory accesses in embedding lookups, thus offering great potential to reduce the inference latency. To fully utilize the DPU memory bandwidth, we further studied the embedding table partitioning problem to achieve good workload-balance and efficient data caching. Evaluations using real-world datasets show that, UpDLRM achieves much lower inference time for DLRM compared to both CPU-only and CPU-GPU hybrid counterparts.

Graph partitioning is an important problem to the performance and cost optimization of graph analytics in geo-distributed environments. Modern hybrid-cut model is expected to obtain better performance and cost optimizations than traditional partitioning models, but can further complicate geo-distributed graph partitioning which is already a challenging problem due to large graph sizes and network heterogeneities of geo-distributed DCs. Existing studies usually adopt heuristic-based methods to achieve fast partitioning for large graphs, which unfortunately sacrifices optimization effectiveness. Further, graph structures of many applications can change at various frequencies. Dynamic partitioning methods usually focus on achieving low latency to quickly adapt to changes, which may again sacrifice partitioning effectiveness. Also, such methods are not aware of the dynamicity of graphs and can over sacrifice effectiveness for unnecessarily low latency. In this paper, we propose RLCut, which uses Reinforcement Learning (RL) to help taming the complexity of the problem. Specifically, RLCut uses multi-agent learning which is more efficient than single agent RL and incorporates a sampling based optimization to adaptively control the training process to satisfy required trade-off between partitioning effectiveness and efficiency according to graph dynamicity. Experiments using real cloud DCs and real-world graphs show that, compared to state-of-the-art static partitioning methods, RLCut improves the performance of geo-distributed graph analytics by 10%-100% with comparable overhead. When users tolerate longer partitioning overhead, we can further improve the performance by up to 43%. With varying graph changing frequencies, RLCut can improve the performance by up to 60% compared to state-of-the-art dynamic partitioning.