Quantum chromodynamics with advanced computing

UNEDF: Advanced Scientific Computing Transforms the Low-Energy Nuclear Many-Body Problem

The UNEDF SciDAC collaboration of nuclear theorists, applied mathematicians, and computer scientists is developing a comprehensive description of nuclei and their reactions that delivers maximum predictive power with quantified uncertainties. This paper illustrates significant milestones accomplished by UNEDF through integration of the theoretical approaches, advanced numerical algorithms, and leadership class computational resources.

UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem

The demands of cutting-edge science are driving the need for larger and faster computing resources. With the rapidly growing scale of computing systems and the prospect of technologically disruptive architectures to meet these needs, scientists face the challenge of effectively using complex computational resources to advance scientific discovery. Multidisciplinary collaborating networks of researchers with diverse scientific backgrounds are needed to address these complex challenges. The UNEDF SciDAC collaboration of nuclear theorists, applied mathematicians, and computer scientists is develo…

Computing support for advanced medical data analysis and imaging

We discuss computing issues for data analysis and image reconstruction of PET-TOF medical scanner or other medical scanning devices producing large volumes of data. Service architecture based on the grid and cloud concepts for distributed processing is proposed and critically discussed.

通过学习和GPU计算合成粒子云网格模拟以用于混合粒子加速器束线

Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines

本文提出并实现了一种完全GPU加速的数据驱动代理模拟工作流，利用高保真粒子云网格模拟数据和机器学习方法，为混合粒子加速器束线提供快速且无近似的端到端建模能力。