The 𝜂-threshold decomposition in uncertain graphs, which calculates the 𝜂-thresholds for each vertex, is a fundamental problem in graph analysis. However, the current CPU-based peeling algorithm suffers from prohibitive computational costs, making it infeasible for time-sensitive applications such as fraud detection and dynamic public opinion monitoring. To address this, we introduce Gatd, the first GPU-accelerated framework for 𝜂-threshold decomposition, co-designed with GPU architecture to enable efficient parallelization. Given that the problem is a computationally intensive per-vertex task dominated by probability computation, thereby constraining efficiency, Gatd incorporates three enhancement modules: (i) Redundancy reduction through lower-bound pruning, leveraging safety thresholds from prior iterations, and batch updating of vertices sharing the same 𝜂-threshold; (ii) Adaptive parallelization utilizing dynamically sized thread collaboration groups and hybrid scheduling to match computational resources with dynamic workloads; and (iii) Three-stage load balancing based on neighbor grouping, work stealing, and hierarchical merging to mitigate supernode-induced imbalance. Extensive experiments on diverse uncertain graphs demonstrate that the optimized Gatd achieves speedups of up to four orders of magnitude over state-of-the-art CPU-based methods and existing GPU-based graph processing frameworks, facilitating efficient decomposition even for large-scale networks.