  Because of the superior feature representation ability of deep learning,
various deep Click-Through Rate (CTR) models are deployed in the commercial
systems by industrial companies. To achieve better performance, it is necessary
to train the deep CTR models on huge volume of training data efficiently, which
makes speeding up the training process an essential problem. Different from the
models with dense training data, the training data for CTR models is usually
high-dimensional and sparse. To transform the high-dimensional sparse input
into low-dimensional dense real-value vectors, almost all deep CTR models adopt
the embedding layer, which easily reaches hundreds of GB or even TB. Since a
single GPU cannot afford to accommodate all the embedding parameters, when
performing distributed training, it is not reasonable to conduct the
data-parallelism only. Therefore, existing distributed training platforms for
recommendation adopt model-parallelism. Specifically, they use CPU (Host)
memory of servers to maintain and update the embedding parameters and utilize
GPU worker to conduct forward and backward computations. Unfortunately, these
platforms suffer from two bottlenecks: (1) the latency of pull \& push
operations between Host and GPU; (2) parameters update and synchronization in
the CPU servers. To address such bottlenecks, in this paper, we propose the
ScaleFreeCTR: a MixCache-based distributed training system for CTR models.
Specifically, in SFCTR, we also store huge embedding table in CPU but utilize
GPU instead of CPU to conduct embedding synchronization efficiently. To reduce
the latency of data transfer between both GPU-Host and GPU-GPU, the MixCache
mechanism and Virtual Sparse Id operation are proposed. Comprehensive
experiments and ablation studies are conducted to demonstrate the effectiveness
and efficiency of SFCTR.
