In this talk, we introduce Merlin HugeCTR. Merlin HugeCTR is an open source,
GPU-accelerated integration framework for click-through rate estimation. It
optimizes both training and inference, whilst enabling model training at scale
with model-parallel embeddings and data-parallel neural networks. In
particular, Merlin HugeCTR combines a high-performance GPU embedding cache with
an hierarchical storage architecture, to realize low-latency retrieval of
embeddings for online model inference tasks. In the MLPerf v1.0 DLRM model
training benchmark, Merlin HugeCTR achieves a speedup of up to 24.6x on a
single DGX A100 (8x A100) over PyTorch on 4x4-socket CPU nodes (4x4x28 cores).
Merlin HugeCTR can also take advantage of multi-node environments to accelerate
training even further. Since late 2021, Merlin HugeCTR additionally features a
hierarchical parameter server (HPS) and supports deployment via the NVIDIA
Triton server framework, to leverage the computational capabilities of GPUs for
high-speed recommendation model inference. Using this HPS, Merlin HugeCTR users
can achieve a 5~62x speedup (batch size dependent) for popular recommendation
models over CPU baseline implementations, and dramatically reduce their
end-to-end inference latency.

Merlin HugeCTR 是一种用于 CTR 估算的开源 GPU 加速集成框架，支持训练和推断优化，利用模型并行嵌入和数据并行神经网络进行规模化的模型训练，并使用高性能 GPU 嵌入式缓存和分层存储架构相结合，以实现低延迟的在线推荐任务。支持使用 NVIDIA Triton 服务器框架部署，可大幅减少端到端推荐模型推断的延迟。

Merlin HugeCTR: GPU 加速的推荐系统训练和推断

Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference

In this paper, we deal with the uncertainty of bidding for display
advertising. Similar to the financial market trading, real-time bidding (RTB)
based display advertising employs an auction mechanism to automate the
impression level media buying; and running a campaign is no different than an
investment of acquiring new customers in return for obtaining additional
converted sales. Thus, how to optimally bid on an ad impression to drive the
profit and return-on-investment becomes essential. However, the large
randomness of the user behaviors and the cost uncertainty caused by the auction
competition may result in a significant risk from the campaign performance
estimation. In this paper, we explicitly model the uncertainty of user
click-through rate estimation and auction competition to capture the risk. We
borrow an idea from finance and derive the value at risk for each ad display
opportunity. Our formulation results in two risk-aware bidding strategies that
penalize risky ad impressions and focus more on the ones with higher expected
return and lower risk. The empirical study on real-world data demonstrates the
effectiveness of our proposed risk-aware bidding strategies: yielding profit
gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on
a commercial RTB platform over the widely applied bidding strategies.

本研究透過評估使用者行為不確定性和拍賣競爭中的成本不確定性來為 RTB 顯示廣告中的不確定性建立數學模型，推導出了兩種風險意識下的出價策略，此模型可有效提高广告投放的利润。