arXiv subjects

Tianfang Xie

Publications and source records attributed to Tianfang Xie.

5 recordsLinked to original sources

arXiv2026

Reclaiming Idle CPU Cycles on Kubernetes: Sparse-Domain Multiplexing for Concurrent MPI-CFD Simulations

When simulations parallelized with the Message Passing Interface (MPI) run on shared Kubernetes clusters, conventional CPU scheduling leaves most provisioned cycles idle at synchronization barriers. This paper presents a multiplexing framework that reclaims this idle capacity by co-locating multiple simulations on the same cluster. Profiling through the MPI profiling interface quantifies per-rank idle fractions; proportional CPU allocation then lets a second simulation run concurrently with minimal overhead, yielding 1.77x throughput. A sweep to eight concurrent simulations locates the capacity boundary: throughput rises to 4.09x at N=6, where pod count equals cluster vCPU count, then collapses to 0.50x at N=8 as busy-wait MPI progress threads displace useful computation. A single-parameter analytical model fitted on one mesh reproduces all twelve concurrent configurations, measured across a 4x range of mesh sizes, within 6.4%, including the boundary point within 0.8%. Four NAS Parallel Benchmarks kernels (1.92-1.99x at N=2), a half-size cluster (1.68x), and per-pod memory accounting confirm generality across workloads, cluster sizes, and resource dimensions. A dynamic controller automates profiling, In-Place Pod Vertical Scaling (KEP-1287), packing, and fairness monitoring, achieving 3.25x throughput for four simulations with zero pod restarts, the first CPU application of in-place resize to running MPI processes.

cs.DC↗

arXiv2026

Rank-Aware Resource Scheduling for Tightly-Coupled MPI Workloads on Kubernetes

Fully provisioned Message Passing Interface (MPI) parallelism achieves near-optimal wall-clock time for Computational Fluid Dynamics (CFD) solvers. This work addresses a complementary question for shared, cloud-managed clusters: can fine-grained CPU provisioning reduce resource reservation of low-load subdomains, improving cluster packing efficiency without unacceptably degrading performance? We propose rank-aware resource scheduling on Kubernetes, mapping each MPI rank to a pod whose CPU request is proportional to its subdomain cell count. We also demonstrate In-Place Pod Vertical Scaling (Kubernetes v1.35 GA) for mid-simulation CPU adjustment without pod restart. Three findings emerge. First, hard CPU limits via the Linux CFS bandwidth controller cause 78x slowdown through cascading stalls at MPI_Allreduce barriers; requests-only allocation eliminates throttling entirely. Second, on non-burstable c5.xlarge instances, concentric decomposition with equal CPU is 19% faster than the Scotch baseline, while adding proportional CPU yields a further 3% improvement. Third, at 16 MPI ranks on 101K-cell meshes, proportional allocation is 20% faster than equal allocation while reducing sparse-subdomain provisioned CPU by 82%, freeing 6.5 vCPU of scheduling headroom. Experiments are conducted on AWS EC2 c5.xlarge clusters (4-16 ranks) running k3s v1.35. All scripts and data are released as open source.

cs.DC↗

arXiv2025

Stoichiometry preservation and generalization of Bilger mixture fraction for non-premixed combustion with differential molecular diffusion

The Bilger mixture fraction is a widely used parameter in non-premixed combustion when considering differential molecular diffusion, a prevalent phenomenon in hydrogen or hydrogen-blended fuel combustion. The property of stoichiometry preservation of mixture fractions is investigated. Two different Bilger mixture fraction formulations are clarified. It is found that they belong to a class of one-parameter generalized mixture fraction definitions discovered in this work. Specific definitions from the class of mixture fractions are compared for hydrocarbon fuels. The comparison shows that the difference can be significant. An optimal mixture fraction definition is sought from the general definitions by minimizing its deviation from the desired properties. The obtained optimal mixture fractions show overall better preservation of stoichiometry than Bilger's definitions. The extension of the generalized mixture fraction to other fuels that contain nitrogen (like ammonia $\mathrm{NH_3}$) or sulfur (like hydrogen sulfide $\mathrm{H_2S}$) is also demonstrated.

physics.flu-dyn↗

arXiv2024

Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders

This study presents a novel approach for intelligent user interaction interface generation and optimization, grounded in the variational autoencoder (VAE) model. With the rapid advancement of intelligent technologies, traditional interface design methods struggle to meet the evolving demands for diversity and personalization, often lacking flexibility in real-time adjustments to enhance the user experience. Human-Computer Interaction (HCI) plays a critical role in addressing these challenges by focusing on creating interfaces that are functional, intuitive, and responsive to user needs. This research leverages the RICO dataset to train the VAE model, enabling the simulation and creation of user interfaces that align with user aesthetics and interaction habits. By integrating real-time user behavior data, the system dynamically refines and optimizes the interface, improving usability and underscoring the importance of HCI in achieving a seamless user experience. Experimental findings indicate that the VAE-based approach significantly enhances the quality and precision of interface generation compared to other methods, including autoencoders (AE), generative adversarial networks (GAN), conditional GANs (cGAN), deep belief networks (DBN), and VAE-GAN. This work contributes valuable insights into HCI, providing robust technical solutions for automated interface generation and enhanced user experience optimization.

cs.HC↗

arXiv2024

PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification

Due to their substantial sizes, large language models (LLMs) are typically deployed within a single-backbone multi-tenant framework. In this setup, a single instance of an LLM backbone must cater to multiple users or tasks through the application of various parameter-efficient fine-tuning (PEFT) models. Despite the availability of numerous effective PEFT techniques such as LoRA, there remains a need for a PEFT approach that achieves both high efficiency during inference and competitive performance on downstream tasks. In this research, we introduce a new and straightforward PEFT methodology named \underline{P}rompt D\underline{E}pen\underline{D}ent \underline{R}epresentation M\underline{O}dification (PEDRO). The proposed method involves integrating a lightweight vector generator into each Transformer layer, which generates vectors contingent upon the input prompts. These vectors then modify the hidden representations created by the LLM through a dot product operation, thereby influencing the semantic output and generated content of the model. Extensive experimentation across a variety of tasks indicates that: (a) PEDRO surpasses recent PEFT benchmarks when using a similar number of tunable parameters. (b) Under the single-backbone multi-tenant deployment model, PEDRO exhibits superior efficiency compared to LoRA, indicating significant industrial potential.

cs.CL↗