arXiv subjects

Ziren Wang

Publications and source records attributed to Ziren Wang.

6 recordsLinked to original sources

arXiv2026

DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling

Intra-device parallelism addresses resource under-utilization in ML inference and training by overlapping the execution of operators with different resource usage. However, its wide adoption is hindered by a fundamental conflict with the static, sequential programming model of existing frameworks. Integrating these strategies requires invasive, model-specific code overhauls, representing an intractable engineering cost. This is further amplified by the high sensitivity of strategies to execution contexts (e.g., workload, model architecture, hardware), forcing developers to implement and maintain multiple specialized solutions. To address this, we propose DynaFlow, a framework that enables the transparent and flexible integration of intra-device parallelism by decoupling the logical model definition from the physical execution schedule. DynaFlow introduces a flexible frontend with annotations for graph partitioning and a programmable interface for defining custom intra-device parallelism strategies. Its efficient backend manages complex control/data-flow asynchronously, uses custom memory management to eliminate copy overheads, and preserves compatibility with optimizations like CUDA Graphs and TorchInductor. We demonstrate that DynaFlow can integrate representative parallelism strategies into 6 state-of-the-art ML systems with minimal code changes, achieving up to a 1.29x throughput improvement. DynaFlow is publicly available at https://github.com/uw-syfi/DynaFlow.

cs.DC↗

arXiv2025

NanoFlow: Towards Optimal Large Language Model Serving Throughput

Large Language Models (LLMs) have resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users. Consequently, throughput has emerged as a key metric that determines serving systems' performance. Due to large model sizes and memory-intensive self-attention, LLM serving has been commonly assumed to be memory-bound. Through a detailed analysis, we show that despite having memory-intensive components, end-to-end LLM serving is compute bound for most common workloads and LLMs. Alas, most existing serving engines fall short from optimal compute utilization, because the heterogeneous operations that comprise LLM serving--compute, memory, networking--are executed sequentially within a device. We propose NanoFlow, a novel serving framework that exploits intra-device parallelism, which overlaps the usage of heterogeneous resources within a single device. NanoFlow splits inputs into smaller nano-batches and duplicates operations to operate on each portion independently, enabling overlapping. NanoFlow automatically identifies the number, size, ordering, and GPU resource allocation of nano-batches to minimize the execution time, while considering the interference of concurrent operations. We evaluate NanoFlow's end-to-end serving throughput on several popular models such as LLaMA-2-70B, Mixtral 8x7B, LLaMA-3-8B, etc. With practical workloads, NanoFlow provides 1.91x throughput boost compared to state-of-the-art serving systems achieving 50% to 72% of optimal throughput across popular models.

cs.DC↗

arXiv2023

Room Occupancy Prediction: Exploring the Power of Machine Learning and Temporal Insights

Energy conservation in buildings is a paramount concern to combat greenhouse gas emissions and combat climate change. The efficient management of room occupancy, involving actions like lighting control and climate adjustment, is a pivotal strategy to curtail energy consumption. In contexts where surveillance technology isn't viable, non-intrusive sensors are employed to estimate room occupancy. In this study, we present a predictive framework for room occupancy that leverages a diverse set of machine learning models, with Random Forest consistently achieving the highest predictive accuracy. Notably, this dataset encompasses both temporal and spatial dimensions, revealing a wealth of information. Intriguingly, our framework demonstrates robust performance even in the absence of explicit temporal modeling. These findings underscore the remarkable predictive power of traditional machine learning models. The success can be attributed to the presence of feature redundancy, the simplicity of linear spatial and temporal patterns, and the advantages of high-frequency data sampling. While these results are compelling, it's essential to remain open to the possibility that explicitly modeling the temporal dimension could unlock deeper insights or further enhance predictive capabilities in specific scenarios. In summary, our research not only validates the effectiveness of our prediction framework for continuous and classification tasks but also underscores the potential for improvements through the inclusion of temporal aspects. The study highlights the promise of machine learning in shaping energy-efficient practices and room occupancy management.

cs.LG↗

arXiv2023

Beyond Tides and Time: Machine Learning Triumph in Water Quality

Water resources are essential for sustaining human livelihoods and environmental well being. Accurate water quality prediction plays a pivotal role in effective resource management and pollution mitigation. In this study, we assess the effectiveness of five distinct predictive models linear regression, Random Forest, XGBoost, LightGBM, and MLP neural network, in forecasting pH values within the geographical context of Georgia, USA. Notably, LightGBM emerges as the top performing model, achieving the highest average precision. Our analysis underscores the supremacy of tree-based models in addressing regression challenges, while revealing the sensitivity of MLP neural networks to feature scaling. Intriguingly, our findings shed light on a counterintuitive discovery: machine learning models, which do not explicitly account for time dependencies and spatial considerations, outperform spatial temporal models. This unexpected superiority of machine learning models challenges conventional assumptions and highlights their potential for practical applications in water quality prediction. Our research aims to establish a robust predictive pipeline accessible to both data science experts and those without domain specific knowledge. In essence, we present a novel perspective on achieving high prediction accuracy and interpretability in data science methodologies. Through this study, we redefine the boundaries of water quality forecasting, emphasizing the significance of data driven approaches over traditional spatial temporal models. Our findings offer valuable insights into the evolving landscape of water resource management and environmental protection.

stat.ML↗

arXiv2018

Breakdown of diffusivity-entropy scaling in colloidal glass forming liquids

Glass is a liquid that has lost its ability to flow. Why this particular substance undergoes its dramatic slowing down in kinetics while remaining barely distinguishable in structure from the fluid state upon cooling constitutes the central question of glass transition physics. Here, we experimentally tested the pathway of kinetic slowing down in glass$\textrm{-}$forming liquids that consisted of ellipsoidal or binary spherical colloids. In contrast to rotational motion, the exponential scaling between diffusion coefficient and excess entropy in translational motion was revealed to break down at startlingly low area fractions ($ϕ_\textrm{T}$) due to glassy effects. At $ϕ_\textrm{T}$, anormalous translation-rotation coupling was enhanced and the topography of the free energy landscape became rugged. Basing on the positive correlation between $ϕ_\textrm{T}$ and fragility, the measurement of $ϕ_\textrm{T}$ offers a novel method for predicting liquids' relaxation while circumventing the prohibitive increase in equilibrium times required in high density regions. Our results highlight the role that thermodynamical entropy plays in glass transitions.

cond-mat.soft↗

arXiv2017

Dynamic particle packing in freezing colloidal suspensions

In the field of freezing colloidal suspensions, it is important to understand the particle-scale behavior of particle packing. Here, we reveal the dynamics of particle packing by identifying the behavior of each single particle in situ. The typical pattern consists of locally ordered clusters and amorphous defects. The microscopic mechanism of pattern formation is ascribed to the non-equilibrium particle-packing process on the particle scale, described with the Péclet number. The macroscopic migration of a particle layer is also revealed by an analytical model involving parameters of freezing speed and initial volume fraction of particles.

cond-mat.mtrl-sci↗