RAPIDS Highlights

The RAPIDS SciDAC Institute for Computer Science and Data has objective of assisting Office of Science (SC) application teams in overcoming computer science and data challenges in the use of DOE supercomputing resources to achieve science breakthroughs.

The following slides contain brief overviews of recent scientific achievements:

• Machine Learned Magnetohdrodynamic Reduced-Order Models
• Lagrangian Particle Tracing for Next Generation Architectures
• Verification of Structured Code Refactoring
• Parallel Data Aggregation for Neutrino Event Analysis
• Plasma-Material Coupling with ADIOS
• Surrogate Modeling for Spatio-Temporal Data from Earth System Models
• Accelerating Density Functional Theory Computations
• Accelerating Sparse Triangular Solves in Fusion Science Codes through One-Sided Communication
• Accelerating HEP Data Analysis on HPC Platforms
• Accelerating I/O for VPIC Simulations
• Galaxy-Scale Strong Gravitational Lens Detection
• Integrating Human Perception with Computational Power for Guided Exploratory Data Analysis
• Data Management and Visualization for Plasma Physics
• Automated Parallel Event Generation and Analysis
• Accelerating Event Reconstruction for Liquid Argon TPC Neutrino Detectors
• Enabling Global Adjoint Tomography at scale through next-generation I/O
• Graphical Model Structure Learning at Unprecedented Scale
• Global Particle-in-Cell Simulation of Fusion Plasmas
• Multivariate, Temporal Visual Analytics for Climate Model Analysis
• Accelerating Earthquake Detection
• Accelerating Weather Research Forecasting Simulations with Deep Neural Network Surrogates
• Accelerating Computational Kernels of Tokamak Simulations (with FASTMath)
• Performance Optimization for Multiscale Gyrokinetic Turbulence
• Accelerating HEP Event Generation and Analysis on HPC Systems
• Accelerating Fusion Fission Simulations
• In situ Viz Unlocks Unsteady Dynamics at Extreme Scale
• Roofline-Based Modeling in Intel Advisor
• Improving Network Throughput with Global Communication Reordering
• Improving Collective Reduction Performance On Manycore Architectures
• Deep Stack Program Optimization
• Autonomic Data Movement for Data Staging-based In-Situ Workflows
• Robust IO Performance Modeling in Leadership-Class Systems by Automated Change Detection
• In Situ Compression Artifact Removal in Scientific Data Using Deep Transfer Learning