arXiv subjects

Kun Tan

Publications and source records attributed to Kun Tan.

3 recordsLinked to original sources

arXiv2025

SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mapping

Hyperspectral imaging (HSI) is a critical technique for fine-grained land-use and land-cover (LULC) mapping. However, the inherent heterogeneity of HSI data, particularly the variation in spectral channels across sensors, has long constrained the development of model generalization via transfer learning or joint training. Existing HSI foundation models show promise for different downstream tasks, but typically underutilize the critical guiding role of sensor meta-attributes and image semantic features, resulting in limited adaptability to cross-sensor joint learning. To address these issues, we propose SpecAware, which is a novel hyperspectral spectral-content aware foundation model for unifying multi-sensor learning for HSI mapping. To support this work, we constructed the Hyper-400K dataset, which is a new large-scale pre-training dataset with over 400\,k high-quality patches from diverse airborne AVIRIS sensors that cover two data processing levels (L1 and L2). The core of SpecAware is a hypernetwork-driven unified image embedding process for HSI data. Firstly, we designed a meta-content aware module to generate a unique conditional input for each HSI sample, tailored to each spectral band by fusing the sensor meta-attributes and its own image content. Secondly, we designed the HyperEmbedding module, where a sample-conditioned hypernetwork dynamically generates a pair of matrix factors for channel-wise encoding. This process implements two-step matrix factorization, consisting of adaptive spatial pattern extraction and latent semantic feature projection, yielding a unified hyperspectral token representation. Thus, SpecAware learns to capture and interpret spatial-spectral features across diverse scenes and sensors, enabling adaptive processing of variable spectral channels within a unified multi-sensor joint pre-training framework.

cs.CV↗

arXiv2023

NP-RDMA: Using Commodity RDMA without Pinning Memory

Remote Direct Memory Access (RDMA) has been haunted by the need of pinning down memory regions. Pinning limits the memory utilization because it impedes on-demand paging and swapping. It also increases the initialization latency of large memory applications from seconds to minutes. To remove memory pining, existing approaches often require special hardware which supports page fault, and still have inferior performance. We propose NP-RDMA, which removes memory pinning during memory registration and enables dynamic page fault handling with commodity RDMA NICs. NP-RDMA does not require NICs to support page fault. Instead, by monitoring local memory paging and swapping with MMU-notifier, combining with IOMMU/SMMU-based address mapping, NP-RDMA efficiently detects and handles page fault in the software with near-zero additional latency to non-page-fault RDMA verbs. We implement an LD_PRELOAD library (with a modified kernel module), which is fully compatible with existing RDMA applications. Experiments show that NP-RDMA adds only 0.1{\sim}2 {\mu}s latency under non-page-fault scenarios. Moreover, NP-RDMA adds only 3.5{\sim}5.7 {\mu}s and 60 {\mu}s under minor or major page faults, respectively, which is 500x faster than ODP which uses advanced NICs that support page fault. With non-pinned memory, Spark initialization is 20x faster and the physical memory usage reduces by 86% with only 5.4% slowdown. Enterprise storage can expand to 5x capacity with SSDs while the average latency is only 10% higher. To the best of our knowledge, NP-RDMA is the first efficient and application-transparent software approach to remove memory pinning using commodity RDMA NICs.

cs.NI↗

arXiv2020

DS-Sync: Addressing Network Bottlenecks with Divide-and-Shuffle Synchronization for Distributed DNN Training

Bulk synchronous parallel (BSP) is the de-facto paradigm for distributed DNN training in today's production clusters. However, due to the global synchronization nature, its performance can be significantly influenced by network bottlenecks caused by either static topology heterogeneity or dynamic bandwidth contentions. Existing solutions, either system-level optimizations strengthening BSP (e.g., Ring or Hierarchical All-reduce) or algorithmic optimizations replacing BSP (e.g., ASP or SSP, which relax the global barriers), do not completely solve the problem, as they may still suffer from communication inefficiency or risk convergence inaccuracy. In this paper, we present a novel divide-and-shuffle synchronization (DS-Sync) to realize communication efficiency without sacrificing convergence accuracy for distributed DNN training. At its heart, by taking into account the network bottlenecks, DS-Sync improves communication efficiency by dividing workers into non-overlap groups to synchronize independently in a bottleneck-free manner. Meanwhile, it maintains convergence accuracy by iteratively shuffling workers among different groups to ensure a global consensus. We theoretically prove that DS-Sync converges properly in non-convex and smooth conditions like DNN. We further implement DS-Sync and integrate it with PyTorch, and our testbed experiments show that DS-Sync can achieve up to $94\%$ improvements on the end-to-end training time with existing solutions while maintaining the same accuracy.

cs.LG↗