Browsing by Author "Lux, Thomas"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
    Chang, Tyler; Watson, Layne T.; Larson, Jeffrey; Neveu, Nicole; Thacker, William; Deshpande, Shubhangi; Lux, Thomas (ACM, 2022-09-10)
    VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of Deshpande et al. [2016], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.
  • Thumbnail Image
    MOANA: Modeling and Analyzing I/O Variability in Parallel System Experimental Design
    Cameron, Kirk W.; Anwar, Ali; Cheng, Yue; Xu, Li; Li, Bo; Ananth, Uday; Lux, Thomas; Hong, Yili; Watson, Layne T.; Butt, Ali R. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2018-04-19)
    Exponential increases in complexity and scale make variability a growing threat to sustaining HPC performance at exascale. Performance variability in HPC I/O is common, acute, and formidable. We take the first step towards comprehensively studying linear and nonlinear approaches to modeling HPC I/O system variability. We create a modeling and analysis approach (MOANA) that predicts HPC I/O variability for thousands of software and hardware configurations on highly parallel shared-memory systems. Our findings indicate nonlinear approaches to I/O variability prediction are an order of magnitude more accurate than linear regression techniques. We demonstrate the use of MOANA to accurately predict the confidence intervals of unmeasured I/O system configurations for a given number of repeat runs – enabling users to quantitatively balance experiment duration with statistical confidence.