A tutorial on derivative-free policy learning methods for interpretable controller representations
JA Paulson, F Sorourifar… - 2023 American Control …, 2023 - ieeexplore.ieee.org
This paper provides a tutorial overview of recent advances in learning control policy
representations for complex systems. We focus on control policies that are determined by …
representations for complex systems. We focus on control policies that are determined by …
Baco: A fast and portable Bayesian compiler optimization framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose
autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the …
autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the …
HEPnOS: A specialized data service for high energy physics analysis
In this paper, we present HEPnOS, a distributed data service for managing data produced by
high-energy physics (HEP) experiments. Using HEPnOS, HEP applications can use HPC …
high-energy physics (HEP) experiments. Using HEPnOS, HEP applications can use HPC …
Uncertainty Quantification for Traffic Forecasting Using Deep-Ensemble-Based Spatiotemporal Graph Neural Networks
T Mallick, J Macfarlane… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Deep-learning-based data-driven forecasting methods have achieved impressive results for
traffic forecasting. Specifically, spatiotemporal graph neural networks have emerged as a …
traffic forecasting. Specifically, spatiotemporal graph neural networks have emerged as a …
High-Quality I/O Bandwidth Prediction with Minimal Data via Transfer Learning Workflow
Providing a high-quality performance prediction has the potential to enhance various
aspects of a cluster, such as devising scheduling and provisioning policies, guiding …
aspects of a cluster, such as devising scheduling and provisioning policies, guiding …
[PDF][PDF] Performance Roulette: How Cloud Weather Affects ML-Based System Optimization
As system complexity, workload diversity, and cloud computing adoption continue to grow,
both operators and developers are turning to machine learning (ML) based approaches for …
both operators and developers are turning to machine learning (ML) based approaches for …
Auto-tuning for HPC storage stack: an optimization perspective
Storage stack layers in high-performance computing (HPC) systems offer many tunable
parameters controlling I/O behaviors and underlying file system settings. The setting of these …
parameters controlling I/O behaviors and underlying file system settings. The setting of these …
Extending the Mochi Methodology to Enable Dynamic HPC Data Services
High-performance computing (HPC) applications and workflows are increasingly making
use of custom data services to complement traditional parallel file systems with fast transient …
use of custom data services to complement traditional parallel file systems with fast transient …
Performance Tuning for GPU-Embedded Systems: Machine-Learning-Based and Analytical Model-Driven Tuning Methodologies
AP Diéguez, MA López - 2023 IEEE 35th International …, 2023 - ieeexplore.ieee.org
GPU-embedded systems have gained popularity across various domains due to their
efficient power consumption. However, in order to meet the demands of real-time or time …
efficient power consumption. However, in order to meet the demands of real-time or time …
Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality
Tuning searches in High-Performance Computing (HPC) are challenged not only by the
need to finely tune parameters in application routines but also by considering their potential …
need to finely tune parameters in application routines but also by considering their potential …