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  1. Free, publicly-accessible full text available May 1, 2025
  2. Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications. However, their node memory favors smaller batch sizes to capture more dependencies in graph events and needs to be maintained synchronously across all trainers. As a result, existing frameworks suffer from accuracy loss when scaling to multiple GPUs. Even worse, the tremendous overhead of synchronizing the node memory makes it impractical to deploy the solution in GPU clusters. In this work, we propose DistTGL — an efficient and scalable solution to train memory-based TGNNs on distributed GPU clusters. DistTGL has three improvements over existing solutions: an enhanced TGNN model, a novel training algorithm, and an optimized system. In experiments, DistTGL achieves near-linear convergence speedup, outperforming the state-of-the-art single-machine method by 14.5% in accuracy and 10.17× in training throughput. 
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    Free, publicly-accessible full text available November 11, 2024
  3. Multi-Agent Reinforcement Learning (MARL) is a key technology in artificial intelligence applications such as robotics, surveillance, energy systems, etc. Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is a state-of-the-art MARL algorithm that has been widely adopted and considered a popular baseline for novel MARL algorithms. However, existing implementations of MADDPG on CPU and CPU-GPU platforms do not exploit fine-grained parallelism between cooperative agents and handle inter-agent communication sequentially, leading to sub-optimal throughput performance in MADDPG training. In this work, we develop the first high-throughput MADDPG accelerator on a CPU-FPGA heterogeneous platform. Specifically, we develop dedicated hardware modules that enable parallel training of each agent's internal Deep Neural Networks (DNNs) and support low-latency inter-agent communication using an on-chip agent interconnection network. Our experimental results show that the speed performance of agent neural network training improves by a factor of 3.6×−24.3× and 1.5×−29.5× compared with state-of-the-art CPU and CPU-GPU implementations. Our design achieves up to a 1.99× and 1.93× improvement in overall system throughput compared with CPU and CPU-GPU implementations, respectively. 
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    Free, publicly-accessible full text available September 25, 2024
  4. Free, publicly-accessible full text available September 1, 2024