Related papers: Snowmass 2013 Computing Frontier: Accelerator Scie…
Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving…
Computational scientists are facing a new era where the old ways of developing and reusing code have to be left behind and a few daring steps are to be made towards new horizons. The present work analyzes the needs that drive this change,…
Accelerator radio frequency (RF) technology has been and remains critical for modern high energy physics (HEP) experiments based on particle accelerators. Tremendous progress in advancing this technology has been achieved over the past…
Power awareness is fast becoming immensely important in computing, ranging from the traditional High Performance Computing applications, to the new generation of data centric workloads. In this work we describe our efforts towards a power…
This report documents the process that led to the NSF Workshop on "Sustainable Computing for Sustainability" held in April 2024 at NSF in Alexandria, VA, and reports on its findings. The workshop's primary goals were to (i) advance the…
The 2022 Snowmass Community Summer Study consisted of two years of engagement with the particle physics community to craft a US particle physics strategy for the next decade, culminating in the 2022 Snowmass Book. This Report of the Topical…
To improve the environmental implications of the growing demand of computing, future applications need to improve the carbon-efficiency of computing infrastructures. State-of-the-art approaches, however, do not consider the intermittent…
Here, we attempt to summarize the activities of Track 3 of the 17th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2016).
Plots summarizing the constraints on Dark Matter models can help visualize synergies between different searches for the same kind of experiment, as well as between different experiments. In this whitepaper, we present an update to the…
I detail an optimistic future vision for the use of SMEFT at high-energy colliders, and describe the use of the studies which could result for interpretation of future models. I also explore some of the potential pitfalls which could…
Effective data center cooling is crucial for reliable operation; however, cooling systems often exhibit inefficiencies that result in excessive energy consumption. This paper presents a three-stage, physics-guided machine learning framework…
The paper gives a short overview of the principles of particle accelerators, their historical development and the typical performance limitations. After an introduction to the basic concepts, the main emphasis is to sketch the layout of…
Computation has become an integral part of physics research. However, little is known about how students learn to productively use computation as a tool beyond the introductory level, especially as they transition into physics research. In…
This document summarizes the conclusions of the Neutrino Town Meeting held at CERN in October 2018 to review the neutrino field at large with the aim of defining a strategy for accelerator-based neutrino physics in Europe. The importance of…
Workload characterization is an integral part of performance analysis of high performance computing (HPC) systems. An understanding of workload properties sheds light on resource utilization and can be used to inform performance…
Neural Networks (NN) provide a solid and reliable way of executing different types of applications, ranging from speech recognition to medical diagnosis, speeding up onerous and long workloads. The challenges involved in their…
Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their…
The search for particle-like dark matter with meV-to-GeV masses has developed rapidly in the past few years. We summarize the science case for these searches, the recent progress, and the exciting upcoming opportunities. Funding for…
Progress in many domains increasingly benefits from our ability to view the systems through a computational lens, i.e., using computational abstractions of the domains; and our ability to acquire, share, integrate, and analyze disparate…
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming…