arXiv subjects

Haoxu Li

Publications and source records attributed to Haoxu Li.

3 recordsLinked to original sources

arXiv2026

PhotonZip: Lifecycle-Aware Compression for HDF5-Based Synchrotron Workflows

Synchrotron detectors generate data at rates that challenge storage and I/O systems. Compression can reduce data movement, but codec-only measurements do not capture the end-to-end behavior of HDF5 workflows. We present PhotonZip, a lifecycle-aware compression framework that tests candidate modes through a common HDF5 path and selects among eligible measured configurations according to either compression ratio or round-trip throughput. Its modular filters preserve standard HDF5 access when the required plugin is available. Across four beamline-derived datasets, PhotonZip reaches lossless compression ratios up to 1.629$\times$ and HDF5 write throughput up to 3.40GB/s write and 7.75GB/s read. The throughput-oriented configuration achieves 1.529$\times$ lossless data reduction while improving HDF5 write and read throughput by 21.2\% and 15.0\%, respectively. Lossy preview modes provide up to 16$\times$ data reduction and reduce preview latency by up to 8.35~s in the evaluated file-per-rank HDF5 configuration.

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arXiv2026

CCL-D: A High-Precision Diagnostic System for Slow and Hang Anomalies in Large-Scale Model Training

As training scales grow, collective communication libraries (CCL) increasingly face anomalies arising from complex interactions among hardware, software, and environmental factors. These anomalies typically manifest as slow/hang communication, the most frequent and time-consuming category to diagnose. However, traditional diagnostic methods remain inaccurate and inefficient, frequently requiring hours or even days for root cause analysis. To address this, we propose CCL-D, a high-precision diagnostic system designed to detect and locate slow/hang anomalies in large-scale distributed training. CCL-D integrates a rank-level real-time probe with an intelligent decision analyzer. The probe measures cross-layer anomaly metrics using a lightweight distributed tracing framework to monitor communication traffic. The analyzer performs automated anomaly detection and root-cause location, precisely identifying the faulty GPU rank. Deployed on a 4,000-GPU cluster over one year, CCL-D achieved near-complete coverage of known slow/hang anomalies and pinpointed affected ranks within 6 minutes-substantially outperforming existing solutions.

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arXiv2026

A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States

AI-driven methods have demonstrated considerable success in tackling the central challenge of accurately solving the Schrödinger equation for complex many-body systems. Among neural network quantum state (NNQS) approaches, the NNQS-SCI (Selected Configuration Interaction) method stands out as a state-of-the-art technique, recognized for its high accuracy and scalability. However, its application to larger systems is severely constrained by a hybrid CPU-GPU architecture. Specifically, centralized CPU-based global de-duplication creates a severe scalability barrier due to communication bottlenecks, while host-resident coupled-configuration generation induces prohibitive computational overheads. We introduce QiankunNet-cuSCI, a fully GPU-accelerated SCI framework designed to overcome these bottlenecks. It first integrates a distributed, load-balanced global de-duplication algorithm to minimize redundancy and communication overhead at scale. To address compute limitations, it employs specialized, fine-grained CUDA kernels for exact coupled configuration generation. Finally, to break the single-GPU memory barrier exposed by this full acceleration, it incorporates a GPU memory-centric runtime featuring GPU-side pooling, streaming mini-batches, and overlapped offloading. This design enables much larger configuration spaces and shifts the bottleneck from host-side limitations back to on-device inference. Our evaluation demonstrates that our work fundamentally expands the scale of solvable problems. On an NVIDIA A100 cluster with 64 GPUs, our work achieves up to 2.32X end-to-end speedup over the highly-optimized NNQS-SCI baseline while preserving the same chemical accuracy. Furthermore, it demonstrates excellent distributed performance, maintaining over 90% parallel efficiency in strong scaling tests.

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