arXiv subjects

Ziyang Jia

Publications and source records attributed to Ziyang Jia.

3 recordsLinked to original sources

arXiv2026

Energy-Efficient Multimodal Inference Serving with Tri-serve

Multimodal model inference creates substantial energy demand with growing performance requirements. Within GPUs, power is autonomously managed by an on-board power management unit (PMU), which makes frequency boosting/throttling decisions. However, we find that these hardware-managed frequency decisions can cause significant power inefficiency. This work identifies three classes of power inefficiencies within modern multimodal inference serving: (1) inter-stage dependency stalls run at near-maximum frequency despite being idle; (2) anti-correlation between auto-boost frequency and arithmetic intensity (A.I.) results in compute-bound phases (e.g., prefill) running at lower frequency and vice versa; and (3) thermal throttling degrades SM frequency and throughput. We propose Tri-serve, a software-based DVFS controller that jointly accounts for inter-stage dependency stalls, the arithmetic-intensity effect on frequency and power, and the thermal-throttling effect of high A.I. phases, to deliver energy-efficient multimodal serving on commodity GPUs. We show that Tri-serve achieves a 22% energy-efficiency improvement with no latency or throughput impact.

cs.DC↗

arXiv2026

NIXT: A NCCL Inspector Exporter Tool for Observability of Collective Communication in Large Model Training

As machine learning workloads scale, it is increasingly important to gain more observability into the performance of collective communication to easily identify performance vari- ations and accelerate root cause identification. Towards this goal, the Nvidia Collective Communication Library (NCCL) introduced NCCL Inspector, a profiler plugin that provides lightweight and continuous reporting of NCCL communication performance statistics. However, the large volume of data collected by NCCL Inspector can be difficult to assess and to extract actionable insights from. This paper presents NIXT, a NCCL Inspector Exporter Tool that improves the observability of collective communication by providing readily accessible analysis and actionable insights from NCCL Inspector profiling. To highlight the benefits of our Exporter Tool, we present a case study of Nemotron-4 LLM pretraining on an Nvidia H100 GPU cluster with up to 2,048 GPUs, demonstrate observability into how communication phases change with ML parallelism and GPU scale, and perform attribution of performance variation and root cause analysis of stragglers.

cs.DC↗

arXiv2019

Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems

Recent researches have shown that deep forest ensemble achieves a considerable increase in classification accuracy compared with the general ensemble learning methods, especially when the training set is small. In this paper, we take advantage of deep forest ensemble and introduce the Dense Adaptive Cascade Forest (daForest). Our model has a better performance than the original Cascade Forest with three major features: first, we apply SAMME.R boosting algorithm to improve the performance of the model. It guarantees the improvement as the number of layers increases. Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration. Third, we add a hyper-parameters optimization layer before the first classification layer, making our model spend less time to set up and find the optimal hyper-parameters. Experimental results show that daForest performs significantly well, and in some cases, even outperforms neural networks and achieves state-of-the-art results.

cs.LG↗