Temporal Interaction Graphs (TIGs) are widely employed to model intricate
real-world systems such as financial systems and social networks. To capture
the dynamism and interdependencies of nodes, existing TIG embedding models need
to process edges sequentially and chronologically. However, this requirement
prevents it from being processed in parallel and struggle to accommodate
burgeoning data volumes to GPU. Consequently, many large-scale temporal
interaction graphs are confined to CPU processing. Furthermore, a generalized
GPU scaling and acceleration approach remains unavailable. To facilitate
large-scale TIGs' implementation on GPUs for acceleration, we introduce a novel
training approach namely Streaming Edge Partitioning and Parallel Acceleration
for Temporal Interaction Graph Embedding (SPEED). The SPEED is comprised of a
Streaming Edge Partitioning Component (SEP) which addresses space overhead
issue by assigning fewer nodes to each GPU, and a Parallel Acceleration
Component (PAC) which enables simultaneous training of different sub-graphs,
addressing time overhead issue. Our method can achieve a good balance in
computing resources, computing time, and downstream task performance. Empirical
validation across 7 real-world datasets demonstrates the potential to expedite
training speeds by a factor of up to 19.29x. Simultaneously, resource
consumption of a single-GPU can be diminished by up to 69%, thus enabling the
multiple GPU-based training and acceleration encompassing millions of nodes and
billions of edges. Furthermore, our approach also maintains its competitiveness
in downstream tasks.

大规模时间交互图的 GPU 加速训练方法 (SPEED)。通过流式边分区和并行加速方法，SPEED 实现了更高的训练速度和更低的资源消耗，具有潜力在百万节点和十亿边的环境下进行训练和加速，并在后续任务中保持竞争力。