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Multi-dimensional Fourier transforms are key mathematical building blocks that appear in a wide range of applications from materials science, physics, chemistry and even machine learning. Over the past years, a multitude of software…

We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations. We implement and validate the accuracy of a novel multi-grid scheme based on…

Page switching is a technique that increases the memory in microcontrollers without extending the address buses. This technique is widely used in the design of 8-bit MCUs. In this paper, we present an algorithm to reduce the overhead of…

Numerical simulations are ubiquitous in mathematics and computational science. Several industrial and clinical applications entail modeling complex multiphysics systems that evolve over a variety of spatial and temporal scales. This study…

State-of-the-art algorithms generate scattering amplitudes for high-energy physics at leading order for high-multiplicity processes as compiled code (in Fortran, C or C++). For complicated processes the size of these libraries can become…

The A64FX processor from Fujitsu, being designed for computational simulation and machine learning applications, has the potential for unprecedented performance in HPC systems. In this paper, we evaluate the A64FX by benchmarking against a…

We perform simulations of giant impacts onto the young Uranus using smoothed particle hydrodynamics (SPH) with over 100 million particles. This 100--1000$\times$ improvement in particle number reveals that simulations with below 10^7…

We have developed the astrophysical simulation code XFLAT to study neutrino oscillations in supernovae. XFLAT is designed to utilize multiple levels of parallelism through MPI, OpenMP, and SIMD instructions (vectorization). It can run on…

In this paper, we present the High Energy Physics data format, processing toolset and analysis library a4, providing fast I/O of structured data using the Google protocol buffer library. The overall goal of a4 is to provide physicists with…

High Energy Physics experiments like the LUX-ZEPLIN dark matter experiment face unique challenges when running their computation on High Performance Computing resources. In this paper, we describe some strategies to optimize memory usage of…

Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce near-real-time responses and exhibit broad hardware…

The study of the interaction between solid objects and magnetohydrodynamic (MHD) fluids is of great importance in physics as consequence of the significant phenomena generated, such as planets interacting with stellar wind produced by their…

We have successfully ported under GNU Linux ATLAS 9, the widely used stellar atmosphere modeling code, as well as both the SYNTHE suite of programs, its ``companion'' for spectral synthesis, and WIDTH, used to derive chemical abundances…

A computer code is described for the simulation of gravitational lensing data. The code incorporates adaptive mesh refinement in choosing which rays to shoot based on the requirements of the source size, location and surface brightness…

IRPF90 is a Fortran programming environment which helps the development of large Fortran codes. In Fortran programs, the programmer has to focus on the order of the instructions: before using a variable, the programmer has to be sure that…

The size and compute characteristics of modern large language models have led to an increased interest in developing specialized kernels tailored for particular training and inference workloads. Existing kernels primarily optimize for…

Large deep learning models have demonstrated strong ability to solve many tasks across a wide range of applications. Those large models typically require training and inference to be distributed. Tensor parallelism is a common technique…

Modern data science relies on data analytic pipelines to organize interdependent computational steps. Such analytic pipelines often involve different algorithms across multiple steps, each with its own hyperparameters. To achieve the best…

Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the…

Particle-Mesh (PM) codes are still very useful tools for testing predictions of cosmological models in cases when extra high resolution is not very important. We release for public use a cosmological PM N-body code. We provide a complete…