Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.25039

Mixed-Precision GPU Acceleration for Confidence Regions of Spatial Excursion Sets

Xiran Zhang·Sameh Abdulah·Qinglei Cao·Joshua French·Rabab Alomairy·Marc G. Genton·David E. Keyes
View original source ↗Read source document ↗Export metadata ↓

Abstract

A spatial excursion set identifies where a field exceeds a threshold. With incomplete and noisy observations, inner and outer confidence regions quantify uncertainty in its spatial extent through lower and upper bounds at a specified confidence level. Constructing these regions requires repeated conditional simulations accounting for joint spatial uncertainty, making dense covariance factorization and simulation major computational bottlenecks. We present a GPU-accelerated framework that combines adaptive tile-based mixed-precision arithmetic with distributed task execution to accelerate universal kriging, prediction-uncertainty estimation, conditional Gaussian simulation, and confidence region calibration. In our parallel mixed-precision simulation workflow, triangular matrix-matrix multiplication (TRMM) inherits the adaptive precision layout of the Cholesky factor and uses FP16 simulation operands, while covariance scaling and compensating operand rescaling accommodate the numerical range of reduced precision arithmetic. With appropriate precision tolerances, empirical coverage is close to the double-precision reference and the nominal 90% confidence level across weak, medium, and strong spatial correlation. Ocean surface-salinity experiments show close agreement with the double-precision reference in spatial predictions and confidence regions. On four NVIDIA A100 GPUs (40 GB each), mixed-precision TRMM reaches 6.36x to 6.93x the theoretical FP64 peak throughput. On four NVIDIA GH200 Hopper GPUs, it reaches 10.20x to 11.03x. Each AMD MI250X node has eight GPU dies. Aggregate throughput rises by 9.1x to 11.5x from 1 to 16 nodes under weak scaling and by 1.6x to 1.9x from 4 to 16 nodes under strong scaling. Adaptive mixed-precision accelerates large-scale excursion set analysis while preserving the statistical accuracy required for spatial uncertainty quantification.

Explore related subjects

cs.DC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiran Zhang, Sameh Abdulah, Qinglei Cao, Joshua French, Rabab Alomairy, Marc G. Genton, David E. Keyes. 2026-10-18. Mixed-Precision GPU Acceleration for Confidence Regions of Spatial Excursion Sets. https://arxiv.org/abs/2610.25039

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

DIFFLET: A Fast Inference Engine for Diffusion Transformer Models on AWS Trainium

On AWS Trainium2, every component of a diffusion model runs as a program compiled ahead of time for fixed shapes, while pipelines differ across models, prompt lengths across requests, and caching decisions across denoising steps. We present Difflet, a serving engine in which five model pipelines share one compilation backend, joint attention is split across cores by attention head, and a host controller decides at each denoising step whether to run the denoiser or reuse its earlier output. A small probe program computes the caching signal on the device and returns one number per step, and a resident model process keeps every program loaded across requests. Across five image and video model families, fused attention accelerates denoiser steps by 1.2-4.3x over the same framework using PyTorch/XLA attention, and at 32.8k visual tokens it is the only attention that compiles. On four of the five models, resident serving is 1.5-3.4x faster than standalone runs that reload weights from a warm file cache for each generation.

cs.DC↗
arXiv2026

Constrained Federated Reinforcement Learning for Secure Offloading in Vehicular Edge Computing

Vehicular Edge Computing (VEC) reduces latency for computation-intensive vehicular applications by offloading tasks to roadside edge servers. Real vehicular workloads, however, consist of dependent subtasks with stringent reliability needs, while the cross-region collaborative learning used to adapt offloading policies must stay robust to heterogeneous data and secure over open wireless channels. We propose CRAFT, a Constrained Reinforcement-based Adaptive Federated Task Offloading framework that casts Directed Acyclic Graph (DAG)-structured offloading and resource allocation as a Constrained Markov Decision Process. CRAFT couples centralized-critic policy learning with Lagrangian relaxation to explicitly regulate the long-term task-completion constraint, and uses two-tier federated aggregation with proximal regularization to jointly mitigate non-Independent and Identically Distributed (non-IID) drift and stabilize constraint adaptation. Confidentiality and integrity for federated parameter exchange are provided by post-quantum homomorphic encryption and lattice-based signatures with modest amortized overhead. On a Virtual Machine-based testbed over a realistic Melbourne Central Business District network with real computation kernels, CRAFT cuts weighted cost by up to 42% over state-of-the-art baselines, maintains a completion rate of around 96%, and adds 6.4% security overhead.

cs.DC↗
arXiv2026

Evaluating Runtime Prediction and Conservative Walltime Policies in HPC Systems

Efficient use of large-scale supercomputing systems depends on accurate runtime estimation and walltime selection. To avoid premature termination, users often overestimate walltime, leading to poor resource utilization and reduced scheduling efficiency. This paper presents a predictor-agnostic framework for safe walltime recommendation in production HPC environments. Rather than proposing a new runtime prediction model, the primary contribution is the SAFE decision layer, which converts predicted runtimes into conservative bounded walltime recommendations suitable for deployment. Runtime prediction is therefore treated as a modular component within the proposed framework. We evaluate the approach using large-scale production job histories from the Fugaku and NREL Eagle supercomputers, comprising approximately 24M and 11M jobs, respectively. Runtime prediction performance is first evaluated using representative predictors, including linear regression, neural networks, and a neural ensemble. The proposed SAFE framework is then evaluated using the NN ensemble predictor. Experimental results indicate that similarity-aware prediction substantially improves runtime estimation for workloads similar to previous executions. When combined with the proposed conservative walltime recommendation policies, these improvements enable walltime gap reductions of approximately 20 - 26% while maintaining job termination rates below 1% across both systems.

cs.DC↗