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 highlights of recent scientific achievements:
An AI-enabled Framework for Earthquake Early Warning by Forecasting Ground Motions
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
Does In-Context Operator Learning Generalize to Domain-Shifted Settings?
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
Robustifying State-space Models for Long Sequences via Approximate Diagonalization
Uncertainty Quantification-enabled inversion of nuclear responses
Guaranteeing Error Bounds in Neural Network Based Autoencoders
Machine Learning for the Time Evolution of Aerosol Microphysics
Continuous Physical Field Reconstruction from Sparse Observations
The Art of Sparsity: Mastering High-Dimensional Tensor Storage
TensorSearch: High dimensional and Parallel Tensor Search Engine
Optimizing the Processing and Storage of Radio Astronomy Data
AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis
Energy Efficient Image Segmentation through Reinforcement Learning
Accelerating Large-Scale Atomistic td-DFTB Simulations Using GPU Offloading
sKokkos: Enabling Kokkos with Transparent Device Selection on Heterogeneous Systems using OpenACC
CCAMP: Integrated Translation and Optimization for OpenACC and OpenMP
IRIS Reimagined: Advancements in Intelligent Runtime System for Task-Based Programmin
CHARM-SYCL: New Unified Programming Environment for Multiple Kinds of Accelerators
MAPredict: Static Analysis Driven Memory Access Prediction Framework for Modern CPUs and GPUs
Performance portability solutions for GPUs and CPUs to track reconstruction kernels
Lagrangian Particle Tracing for Next Generation Architectures
Surrogate Modeling for Spatio-Temporal Data from Earth System Models
Accelerating Sparse Triangular Solves in Fusion Science Codes through One-Sided Communication
Integrating Human Perception with Computational Power for Guided Exploratory Data Analysis
Accelerating Event Reconstruction for Liquid Argon TPC Neutrino Detectors
Enabling Global Adjoint Tomography at scale through next-generation I/O
Multivariate, Temporal Visual Analytics for Climate Model Analysis
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
Improving Network Throughput with Global Communication Reordering
Improving Collective Reduction Performance On Manycore Architectures
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
Error-controlled Reduction and Retrieval for Scientific Data
FTK: A Spacetime Meshing Framework for Robust and Scalable Feature Tracking
Analysis of GPU Data Access Patterns on Complex Geometries for D3Q19 Lattice Boltzmann Algorithm
Machine learning-based application grouping and knowledge extraction for HPC I/O
Cosmic Inference: Constraining Parameters With Observations and Highly Limited Number of Simulations
IExchange: Asynchronous Communication and Termination Detection for Iterative Algorithms
RISE: Reducing I/O Contention in Staging-based Extreme-Scale In-situ Workflows
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems
MAPredict: Static Analysis Driven Memory Access Prediction Framework for Modern CPUs and GPUs
Analyzing the Impact of Lossy Data Reduction on Volume Rendering of Cosmology Data
VDL-Surrogate: A View-Dependent Visualization Surrogate for Ensemble Simulations
Leveraging One-Sided Communication for GPU-accelerated Sparse Triangular Solvers
FES-ASCR Partnership for Research on Superfacility Workflows
Chemical reaction networks and opportunities for machine learning
Learning differentiable solvers for systems with hard constraints
Steady-state collisional radiative closures using random forest regressors
NN-EFIT: Physics-constrained Plasma equilibrium reconstruction for magnetically confined Fusion
Understanding Data Access Patterns for Distributed Science Collaboration
Intelligent Draining to Reduce I/O Contention in Staging-based Workflows
Region-adaptive, Error-controlled Scientific Data Compression for Scientific Data
Error-bounded compression with preservation of derived quantities
Mitigate Data Management Challenges for the Exa.TrkX Workflow
Using Multi-Resolution Data to Accelerate Neural Network Training Time
Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization
IDLat: An Importance-Driven Latent Generation Method for Scientific Data
LANL used RAPIDS2 Production Visualization in Producing Science Video
Improved Parallel Rendering Performance with GPU-Based Image Compression
Analyzing the Impact of Lossy Data Reduction on Volume Rendering of Cosmology Data
Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems
Performance Portability of Sparse Computational Methods on GPU-Accelerated Architectures
ML-based Performance Portability for Density Functional Theory in HPC Environments
Optimized Preprocessing For Scientific Deep Learning Applications
Leveraging One-Sided Communication for Sparse Triangular Solvers on traditional CPUs
FES-ASCR Partnership for Research on Superfacility Workflows
EFIT-AI: Performance Portable GPU-accelerated Reconstruction
CCAMP: Integrated Translation and Optimization for OpenACC and OpenMP
IRIS: A Portable Task Runtime System for Extremely Heterogeneous Computing
Porting HEP Event Reconstruction to GPU-based Heterogeneous Systems
MAPredict: Static Analysis Driven Memory Access Prediction Framework for Modern CPUs and GPUs
Analysis of GPU Data Access Patterns on Complex Geometries for D3Q19 Lattice Boltzmann Algorithm