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Found 10 papers, 8 papers with code
Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning for Enhancing Forecasting Accuracy
This is an important result that implies the possibility to apply a pre-trained ML model to real-time hurricane forecasting results with the goal of bias correcting and improving the produced simulation accuracy.
Deploying a Task-based Runtime System on Raspberry Pi Clusters
Here we describe our efforts to configure and benchmark the use of a Raspberry Pi cluster with the HPX/Phylanx platform (normally intended for use with HPC applications) and document the lessons we learned.
Distributed, Parallel, and Cluster Computing
Towards a Scalable and Distributed Infrastructure for Deep Learning Applications
Although recent scaling up approaches to training deep neural networks have proven to be effective, the computational intensity of large and complex models, as well as the availability of large-scale datasets, require deep learning frameworks to utilize scaling out techniques.
Supporting OpenMP 5.0 Tasks in hpxMP -- A study of an OpenMP implementation within Task Based Runtime Systems
This paper is a follow-up paper on the fundamental implementation of hpxMP, an implementation of the OpenMP standard which utilizes the C++ standard library for Parallelism and Concurrency (HPX) to schedule and manage tasks.
Distributed, Parallel, and Cluster Computing Programming Languages
From Piz Daint to the Stars: Simulation of Stellar Mergers using High-Level Abstractions
We study the simulation of stellar mergers, which requires complex simulations with high computational demands.
Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science
An Introduction to hpxMP: A Modern OpenMP Implementation Leveraging HPX, An Asynchronous Many-Task System
Not only must users port their own codes, but often users rely on highly optimized libraries such as BLAS and LAPACK which use OpenMP for parallization.
Distributed, Parallel, and Cluster Computing
Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)
One big aspect for distributed applications is to guarantee high utilization of all available resources, including local or remote acceleration cards on a cluster while fully using all the available CPU resources and the integration of the GPU work into the overall programming model.
Distributed, Parallel, and Cluster Computing Programming Languages
Asynchronous Execution of Python Code on Task Based Runtime Systems
Despite advancements in the areas of parallel and distributed computing, the complexity of programming on High Performance Computing (HPC) resources has deterred many domain experts, especially in the areas of machine learning and artificial intelligence (AI), from utilizing performance benefits of such systems.
Programming Languages
An asynchronous and task-based implementation of Peridynamics utilizing HPX -- the C++ standard library for parallelism and concurrency
The scalability of asynchronous task-based implementation is to be in agreement with theoretical estimations.
Distributed, Parallel, and Cluster Computing Computational Physics
HPX Smart Executors
Moreover, we advocate a novel method by introducing HPX smart executors for determining the execution policy, chunk size, and prefetching distance of an HPX loop to achieve higher possible performance by feeding static information captured during compilation and runtime-based dynamic information to our learning model.
