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 …

We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose
autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the …

Deep-learning-based data-driven forecasting methods have achieved impressive results for
traffic forecasting. Specifically, spatiotemporal graph neural networks have emerged as a …

Providing a high-quality performance prediction has the potential to enhance various
aspects of a cluster, such as devising scheduling and provisioning policies, guiding …

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 …

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 …

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 …

High-performance computing (HPC) applications and workflows are increasingly making
use of custom data services to complement traditional parallel file systems with fast transient …

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 …

In the past decade, machine learning has experienced exponential growth, propelled by
abundant datasets, algorithmic advancements, and increased computational power …