arXiv subjects

Mosè Giordano

Publications and source records attributed to Mosè Giordano.

9 recordsLinked to original sources

arXiv2026

Accuracy of Mathematical Functions in Julia

Basic computer arithmetic operations, such as $+$, $\times$, or $÷$ are correctly rounded, whilst mathematical functions such as $e^x$, $\ln(x)$, or $\sin(x)$ in general are not, meaning that separate implementations may provide different results when presented with an exact same input, and that their accuracy may differ. We present a methodology and a software tool that is suited for exhaustive and non-exhaustive testing of mathematical functions available as part of the Julia programming language, in various floating-point formats. The software tool is useful to the users of Julia, to quantise the level of accuracy of the mathematical functions and interpret possible effects of errors on their scientific computation codes that depend on these functions. It is also useful to the developers and maintainers of the functions in Julia Base, to test the modifications to existing functions and to test the accuracy of new functions. The software (a test bench) is designed to be easy to set up for running the accuracy tests in automatic regression testing. Our focus is to provide software that is user friendly and allows to avoid the need for specialised knowledge of floating-point arithmetic or the workings of mathematical functions; users only need to supply a list of formats, choose the rounding modes, and specify the input space search strategies based on how long they can afford the testing to run. We have utilized the test bench to determine the errors of a subset of mathematical functions in Julia 1.12.7, for binary16, binary32, and binary64 IEEE 754 floating-point formats, and found $0.49$ to $0.51$ULPs in binary16, and $0.49$ to $2.4$ULPs of error in binary32 and binary64.

cs.MS↗

arXiv2025

Extrae.jl: Julia bindings for the Extrae HPC Profiler

The Julia programming language has gained acceptance within the High-Performance Computing (HPC) community due to its ability to tackle two-language problem: Julia code feels as high-level as Python but allows developers to tune it to C-level performance. But to squeeze every drop of performance, Julia needs to integrate with advanced performance analysis tools, also known as profilers. In this work, we present Extrae.jl, a Julia package to interface with the Extrae profiler.

cs.DC↗

arXiv2022

Bridging HPC Communities through the Julia Programming Language

The Julia programming language has evolved into a modern alternative to fill existing gaps in scientific computing and data science applications. Julia leverages a unified and coordinated single-language and ecosystem paradigm and has a proven track record of achieving high performance without sacrificing user productivity. These aspects make Julia a viable alternative to high-performance computing's (HPC's) existing and increasingly costly many-body workflow composition strategy in which traditional HPC languages (e.g., Fortran, C, C++) are used for simulations, and higher-level languages (e.g., Python, R, MATLAB) are used for data analysis and interactive computing. Julia's rapid growth in language capabilities, package ecosystem, and community make it a promising universal language for HPC. This paper presents the views of a multidisciplinary group of researchers from academia, government, and industry that advocate for an HPC software development paradigm that emphasizes developer productivity, workflow portability, and low barriers for entry. We believe that the Julia programming language, its ecosystem, and its community provide modern and powerful capabilities that enable this group's objectives. Crucially, we believe that Julia can provide a feasible and less costly approach to programming scientific applications and workflows that target HPC facilities. In this work, we examine the current practice and role of Julia as a common, end-to-end programming model to address major challenges in scientific reproducibility, data-driven AI/machine learning, co-design and workflows, scalability and performance portability in heterogeneous computing, network communication, data management, and community education. As a result, the diversification of current investments to fulfill the needs of the upcoming decade is crucial as more supercomputing centers prepare for the exascale era.

cs.DC↗

arXiv2022

Productivity meets Performance: Julia on A64FX

The Fujitsu A64FX ARM-based processor is used in supercomputers such as Fugaku in Japan and Isambard 2 in the UK and provides an interesting combination of hardware features such as Scalable Vector Extension (SVE), and native support for reduced-precision floating-point arithmetic. The goal of this paper is to explore performance of the Julia programming language on the A64FX processor, with a particular focus on reduced precision. Here, we present a performance study on axpy to verify the compilation pipeline, demonstrating that Julia can match the performance of tuned libraries. Additionally, we investigate Message Passing Interface (MPI) scalability and throughput analysis on Fugaku showing next to no significant overheads of Julia of its MPI interface. To explore the usability of Julia to target various floating-point precisions, we present results of ShallowWaters.jl, a shallow water model that can be executed a various levels of precision. Even for such complex applications, Julia's type-flexible programming paradigm offers both, productivity and performance.

cs.DC↗

arXiv2018

Towards new solutions for scientific computing: the case of Julia

This year marks the consolidation of Julia (https://julialang.org/), a programming language designed for scientific computing, as the first stable version (1.0) has been released, in August 2018. Among its main features, expressiveness and high execution speeds are the most prominent: the performance of Julia code is similar to statically compiled languages, yet Julia provides a nice interactive shell and fully supports Jupyter; moreover, it can transparently call external codes written in C, Fortran, and even Python and R without the need of wrappers. The usage of Julia in the astronomical community is growing, and a GitHub organization named JuliaAstro takes care of coordinating the development of packages. In this paper, we present the features and shortcomings of this language and discuss its application in astronomy and astrophysics.

astro-ph.IM↗

arXiv2016

Timing analysis in microlensing

Timing analysis is a powerful tool used to determine periodic features of physical phenomena. Here we review two applications of timing analysis to gravitational microlensing events. The first one, in particular cases, allows the estimation of the orbital period of binary lenses, which in turn enables the breaking of degeneracies. The second one is a method to measure the rotation period of the lensed star by observing signatures due to stellar spots on its surface.

astro-ph.SR↗

arXiv2016

Uncertainty propagation with functionally correlated quantities

Many uncertainty propagation software exist, written in different programming languages, but not all of them are able to handle functional correlation between quantities. In this paper we review one strategy to deal with uncertainty propagation of quantities that are functionally correlated, and introduce a new software offering this feature: the Julia package Measurements.jl. It supports real and complex numbers with uncertainty, arbitrary-precision calculations, mathematical and linear algebra operations with matrices and arrays.

physics.data-an↗

arXiv2016

The Scales of Gravitational Lensing

After exactly a century since the formulation of the general theory of relativity, the phenomenon of gravitational lensing is still an extremely powerful method for investigating in astrophysics and cosmology. Indeed, it is adopted to study the distribution of the stellar component in the Milky Way, to study dark matter and dark energy on very large scales and even to discover exoplanets. Moreover, thanks to technological developments, it will allow the measure of the physical parameters (mass, angular momentum and electric charge) of supermassive black holes in the center of ours and nearby galaxies.

astro-ph.CO↗

arXiv2015

Starspot induced effects in microlensing events with rotating source star

We consider the effects induced by the presence of hot and cold spots on the source star in the light curves of simulated microlensing events due to either single or binary lenses taking into account the rotation of the source star and the orbital motion of the lens system. Our goal is to study the anomalies induced by these effects on simulated microlensing light curves.

astro-ph.SR↗