High-performance GPU-accelerated particle filter methods are critical fo...
Interior point methods are widely used for different types of mathematic...
This paper presents the design and development of an Anderson Accelerate...
FFTc is a Domain-Specific Language (DSL) for designing and generating Fa...
Quantum computer simulators are crucial for the development of quantum
c...
Large-scale plasma simulations are critical for designing and developing...
Cholesky factorization is a widely used method for solving linear system...
Comprehending the performance bottlenecks at the core of the intricate
h...
Physics-Informed Neural Networks (PINN) emerged as a powerful tool for
s...
We propose a spectral method for the 1D-1V Vlasov-Poisson system where t...
GROMACS is one of the most widely used HPC software packages using the
M...
The modern workflow for radiation therapy treatment planning involves
ma...
Discrete Fourier Transform (DFT) libraries are one of the most critical
...
Interactive urgent computing is a small but growing user of supercomputi...
We present our approach to making direct numerical simulations of turbul...
This paper studies the utility of using data analytics and machine learn...
Drug discovery is the most expensive, time demanding and challenging pro...
It is estimated that around 80% of the world's population live in areas
...
We present new results on the strong parallel scaling for the
OpenACC-ac...
The impending termination of Moore's law motivates the search for new fo...
One of the most promising approaches for data analysis and exploration o...
We design and develop a new Particle-in-Cell (PIC) method for plasma
sim...
Recent trends and advancement in including more diverse and heterogeneou...
The modern deep learning method based on backpropagation has surged in
p...
Radiation Treatment Planning (RTP) is the process of planning the approp...
Physics-Informed Neural Networks (PINN) are neural networks that encode ...
Improvements in computer systems have historically relied on two well-kn...
Numerical simulations of plasma flows are crucial for advancing our
unde...
Large-scale simulations of plasmas are essential for advancing our
under...
Machine Learning applications on HPC systems have been gaining popularit...
In the CFD solver Nek5000, the computation is dominated by the evaluatio...
CUDA Unified Memory improves the GPU programmability and also enables GP...
Even though automatic classification and interpretation would be highly
...
Floating-point operations can significantly impact the accuracy and
perf...
iPIC3D is a widely used massively parallel Particle-in-Cell code for the...
TensorFlow is a popular emerging open-source programming framework suppo...
The success of the exascale supercomputer is largely debated to remain
d...
In this work, we consider the integration of MPI one-sided communication...
Upcoming HPC clusters will feature hybrid memories and storage devices p...
The performance of Deep-Learning (DL) computing frameworks rely on the
p...
SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a Europ...
One of the major performance and scalability bottlenecks in large scient...
We aim to implement a Big Data/Extreme Computing (BDEC) capable system
i...
The NVIDIA Volta GPU microarchitecture introduces a specialized unit, ca...