arXiv subjects

Xianwen Guo

Publications and source records attributed to Xianwen Guo.

4 recordsLinked to original sources

arXiv2025

FLAME: A Serving System Optimized for Large-Scale Generative Recommendation with Efficiency

Generative recommendation (GR) models possess greater scaling power compared to traditional deep learning recommendation models (DLRMs), yet they also impose a tremendous increase in computational burden. Measured in FLOPs, a typical GR model's workload sits in $10^9 \sim 10^{11}$ range, roughly four orders of magnitude higher than traditional DLRMs. Delivering accurate results in a few tens of milliseconds while processing billions of such requests per day puts extreme demands on the performance of the online serving system. Therefore, for industry practitioners, the alluring gains of GR models are tempered by the formidable challenge of online deployment at scale in production services. In this work, we introduce a comprehensive solution of online serving system tailored For Large-scale GenerAtive RecoMmendation with Efficiency (FLAME). Specifically, we leveraging CPU-GPU heterogeneous hardware to decouple feature pre-processing and model computation. We encapsulated several memory optimization features as the Proximal Data Accelerator (PDA) module to make full use of limited bandwidth and storage resources, which achieves a 1.9x throughput gain and a 1.7x latency reduction. We implement the Fused Kernel Engine (FKE) module based on the functionality and interface of NVIDIA TensorRT to boost model computation, delivering a speedup ratio of 4.6x-6.1x, throughput gain ratio of 4.7x-6.3x one step further. In addition, we design the Dynamic Stream Orchestrator (DSO) module to coordinate concurrent requests, enhancing the system throughput performance with 1.3x improvement in throughput and 2.3x speed-up under non-uniform distribution of upstream candidates. Comprehensive evaluations demonstrate that our FLAME effectively supports large-scale online deployment of GR models and achieves remarkable improvements in system performance.

cs.DC↗

arXiv2025

Climber: Toward Efficient Scaling Laws for Large Recommendation Models

Transformer-based generative models have achieved remarkable success across domains with various scaling law manifestations. However, our extensive experiments reveal persistent challenges when applying Transformer to recommendation systems: (1) Transformer scaling is not ideal with increased computational resources, due to structural incompatibilities with recommendation-specific features such as multi-source data heterogeneity; (2) critical online inference latency constraints (tens of milliseconds) that intensify with longer user behavior sequences and growing computational demands. We propose Climber, an efficient recommendation framework comprising two synergistic components: the model architecture for efficient scaling and the co-designed acceleration techniques. Our proposed model adopts two core innovations: (1) multi-scale sequence extraction that achieves a time complexity reduction by a constant factor, enabling more efficient scaling with sequence length; (2) dynamic temperature modulation adapting attention distributions to the multi-scenario and multi-behavior patterns. Complemented by acceleration techniques, Climber achieves a 5.15$\times$ throughput gain without performance degradation by adopting a "single user, multiple item" batched processing and memory-efficient Key-Value caching. Comprehensive offline experiments on multiple datasets validate that Climber exhibits a more ideal scaling curve. To our knowledge, this is the first publicly documented framework where controlled model scaling drives continuous online metric growth (12.19\% overall lift) without prohibitive resource costs. Climber has been successfully deployed on Netease Cloud Music, one of China's largest music streaming platforms, serving tens of millions of users daily.

cs.IR↗

arXiv2025

Advancing the Foundation Model for Music Understanding

The field of Music Information Retrieval (MIR) is fragmented, with specialized models excelling at isolated tasks. In this work, we challenge this paradigm by introducing a unified foundation model named MuFun for holistic music understanding. Our model features a novel architecture that jointly processes instrumental and lyrical content, and is trained on a large-scale dataset covering diverse tasks such as genre classification, music tagging, and question answering. To facilitate robust evaluation, we also propose a new benchmark for multi-faceted music understanding called MuCUE (Music Comprehensive Understanding Evaluation). Experiments show our model significantly outperforms existing audio large language models across the MuCUE tasks, demonstrating its state-of-the-art effectiveness and generalization ability.

cs.SD↗

arXiv2020

Obtaining the mean fields with known Reynolds stresses at steady state

With the rising of modern data science, data--driven turbulence modeling with the aid of machine learning algorithms is becoming a new promising field. Many approaches are able to achieve better Reynolds stress prediction, with much lower modeling error ($ε_M$), than traditional RANS models but they still suffer from numerical error and stability issues when the mean velocity fields are estimated using RANS equations with the predicted Reynolds stresses, illustrating that the error of solving the RANS equations ($ε_P$) is also very important. In the present work, the error $ε_P$ is studied separately by using the Reynolds stresses obtained from direct numerical simulation and we derive the sources of $ε_P$. For the implementations with known Reynolds stresses solely, we suggest to run an adjoint RANS simulation to make first guess on $ν_t^*$ and $S_{ij}^0$. With around 10 iterations, the error could be reduced by about one-order of magnitude in flow over periodic hills. The present work not only provides one robust approach to minimize $ε_P$, which may be very useful for the data-driven turbulence models, but also shows the importance of the nonlinear part of the Reynolds stresses in flow problems with flow separations.

physics.flu-dyn↗