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Book ; Online: DAOS as HPC Storage

Jackson, Adrian / Manubens, Nicolau

Exploring Interfaces

2023

Abstract: This work in progress paper outlines research looking at the performance impact of using different storage interfaces to access the high performance object store DAOS. We demonstrate that using DAOS through a FUSE based filesystem interface can provide ... ...

Abstract	This work in progress paper outlines research looking at the performance impact of using different storage interfaces to access the high performance object store DAOS. We demonstrate that using DAOS through a FUSE based filesystem interface can provide high performance, but there are impacts when choosing what I/O library or interface to utilises, with HDF5 exhibiting the highest impact. However, this varied depending on what type of I/O operations were undertaken.
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing
Publishing date	2023-11-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Plug-and-play imaging with model uncertainty quantification in radio astronomy

Terris, Matthieu / Tang, Chao / Jackson, Adrian / Wiaux, Yves

2023

Abstract: Plug-and-Play (PnP) algorithms are appealing alternatives to proximal algorithms when solving inverse imaging problems. By learning a Deep Neural Network (DNN) behaving as a proximal operator, one waives the computational complexity of optimisation ... ...

Abstract	Plug-and-Play (PnP) algorithms are appealing alternatives to proximal algorithms when solving inverse imaging problems. By learning a Deep Neural Network (DNN) behaving as a proximal operator, one waives the computational complexity of optimisation algorithms induced by sophisticated image priors, and the sub-optimality of handcrafted priors compared to DNNs. At the same time, these methods inherit the versatility of optimisation algorithms allowing the minimisation of a large class of objective functions. Such features are highly desirable in radio-interferometric (RI) imaging in astronomy, where the data size, the ill-posedness of the problem and the dynamic range of the target reconstruction are critical. In a previous work, we introduced a class of convergent PnP algorithms, dubbed AIRI, relying on a forward-backward algorithm, with a differentiable data-fidelity term and dynamic range-specific denoisers trained on highly pre-processed unrelated optical astronomy images. Here, we show that AIRI algorithms can benefit from a constrained data fidelity term at the mere cost of transferring to a primal-dual forward-backward algorithmic backbone. Moreover, we show that AIRI algorithms are robust to strong variations in the nature of the training dataset: denoisers trained on MRI images yield similar reconstructions to those trained on astronomical data. We additionally quantify the model uncertainty introduced by the randomness in the training process and suggest that AIRI algorithms are robust to model uncertainty. Finally, we propose an exhaustive comparison with methods from the radio-astronomical imaging literature and show the superiority of the proposed method over the current state-of-the-art.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Astrophysics - Instrumentation and Methods for Astrophysics
Subject code	006
Publishing date	2023-12-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Scalable precision wide-field imaging in radio interferometry

Wilber, Amanda G. / Dabbech, Arwa / Jackson, Adrian / Wiaux, Yves

I. uSARA validated on ASKAP data

2023

Abstract: As Part I of a paper series showcasing a new imaging framework, we consider the recently proposed unconstrained Sparsity Averaging Reweighted Analysis (uSARA) optimisation algorithm for wide-field, high-resolution, high-dynamic range, monochromatic ... ...

Abstract	As Part I of a paper series showcasing a new imaging framework, we consider the recently proposed unconstrained Sparsity Averaging Reweighted Analysis (uSARA) optimisation algorithm for wide-field, high-resolution, high-dynamic range, monochromatic intensity imaging. We reconstruct images from real radio-interferometric observations obtained with the Australian Square Kilometre Array Pathfinder (ASKAP) and present these results in comparison to the widely-used, state-of-the-art imager WSClean. Selected fields come from the ASKAP Early Science and Evolutionary Map of the Universe (EMU) Pilot surveys and contain several complex radio sources: the merging cluster system Abell 3391-95, the merging cluster SPT-CL 2023-5535, and many extended, or bent-tail, radio galaxies, including the X-shaped radio galaxy PKS 2014-558 and the ``dancing ghosts'', known collectively as PKS 2130-538. The modern framework behind uSARA utilises parallelisation and automation to solve for the w-effect and efficiently compute the measurement operator, allowing for wide-field reconstruction over the full field-of-view of individual ASKAP beams (up to 3.3 deg each). The precision capability of uSARA produces images with both super-resolution and enhanced sensitivity to diffuse components, surpassing traditional CLEAN algorithms which typically require a compromise between such yields. Our resulting monochromatic uSARA-ASKAP images of the selected data highlight both extended, diffuse emission and compact, filamentary emission at very high resolution (up to 2.2 arcsec), revealing never-before-seen structure. Here we present a validation of our uSARA-ASKAP images by comparing the morphology of reconstructed sources, measurements of diffuse flux, and spectral index maps with those obtained from images made with WSClean. Comment: Accepted for publication in MNRAS
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Electrical Engineering and Systems Science - Image and Video Processing ; Electrical Engineering and Systems Science - Signal Processing
Subject code	520
Publishing date	2023-02-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Deep network series for large-scale high-dynamic range imaging

Aghabiglou, Amir / Terris, Matthieu / Jackson, Adrian / Wiaux, Yves

2022

Abstract: We propose a new approach for large-scale high-dynamic range computational imaging. Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging problems almost instantaneously. While unfolded architectures provide robustness to ... ...

Abstract	We propose a new approach for large-scale high-dynamic range computational imaging. Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging problems almost instantaneously. While unfolded architectures provide robustness to measurement setting variations, embedding large-scale measurement operators in DNN architectures is impractical. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, have proven effective to address scalability and high-dynamic range challenges, but rely on highly iterative algorithms. We propose a residual DNN series approach, also interpretable as a learned version of matching pursuit, where the reconstructed image is a sum of residual images progressively increasing the dynamic range, and estimated iteratively by DNNs taking the back-projected data residual of the previous iteration as input. We demonstrate on radio-astronomical imaging simulations that a series of only few terms provides a reconstruction quality competitive with PnP, at a fraction of the cost. Comment: Accepted for publication in IEEE Proc. ICASSP 2023
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-10-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Scalable precision wide-field imaging in radio interferometry

Wilber, Amanda G. / Dabbech, Arwa / Terris, Matthieu / Jackson, Adrian / Wiaux, Yves

II. AIRI validated on ASKAP data

2023

Abstract: Accompanying Part I, this sequel delineates a validation of the recently proposed AI for Regularisation in radio-interferometric Imaging (AIRI) algorithm on observations from the Australian Square Kilometre Array Pathfinder (ASKAP). The monochromatic ... ...

Abstract	Accompanying Part I, this sequel delineates a validation of the recently proposed AI for Regularisation in radio-interferometric Imaging (AIRI) algorithm on observations from the Australian Square Kilometre Array Pathfinder (ASKAP). The monochromatic AIRI-ASKAP images showcased in this work are formed using the same parallelised and automated imaging framework described in Part I: ``uSARA validated on ASKAP data''. Using a Plug-and-Play approach, AIRI differs from uSARA by substituting a trained denoising deep neural network (DNN) for the proximal operator in the regularisation step of the forward-backward algorithm during deconvolution. We build a trained shelf of DNN denoisers which target the estimated image-dynamic-ranges of our selected data. Furthermore, we quantify variations of AIRI reconstructions when selecting the nearest DNN on the shelf versus using a universal DNN with the highest dynamic range, opening the door to a more complete framework that not only delivers image estimation but also quantifies epistemic model uncertainty. We continue our comparative analysis of source structure, diffuse flux measurements, and spectral index maps of selected target sources as imaged by AIRI and the algorithms in Part I -- uSARA and WSClean. Overall we see an improvement over uSARA and WSClean in the reconstruction of diffuse components in AIRI images. The scientific potential delivered by AIRI is evident in further imaging precision, more accurate spectral index maps, and a significant acceleration in deconvolution time, whereby AIRI is four times faster than its sub-iterative sparsity-based counterpart uSARA. Comment: Accepted for publication in MNRAS
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing ; Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2023-02-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: DAOS as HPC Storage, a view from Numerical Weather Prediction

Manubens, Nicolau / Quintino, Tiago / Smart, Simon D. / Danovaro, Emanuele / Jackson, Adrian

2022

Abstract: Object storage solutions potentially address long-standing performance issues with POSIX file systems for certain I/O workloads, and new storage technologies offer promising performance characteristics for data-intensive use cases. In this work, we ... ...

Abstract	Object storage solutions potentially address long-standing performance issues with POSIX file systems for certain I/O workloads, and new storage technologies offer promising performance characteristics for data-intensive use cases. In this work, we present a preliminary assessment of Intel's Distributed Asynchronous Object Store (DAOS), an emerging high-performance object store, in conjunction with non-volatile storage and evaluate its potential use for HPC storage. We demonstrate DAOS can provide the required performance, with bandwidth scaling linearly with additional DAOS server nodes in most cases, although choices in configuration and application design can impact achievable bandwidth. We describe a new I/O benchmark and associated metrics that address object storage performance from application-derived workloads.
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing
Publishing date	2022-08-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: First AI for deep super-resolution wide-field imaging in radio astronomy

Dabbech, Arwa / Terris, Matthieu / Jackson, Adrian / Ramatsoku, Mpati / Smirnov, Oleg M. / Wiaux, Yves

unveiling structure in ESO 137--006

2022

Abstract: We introduce the first AI-based framework for deep, super-resolution, wide-field radio-interferometric imaging, and demonstrate it on observations of the ESO~137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image ... ...

Abstract	We introduce the first AI-based framework for deep, super-resolution, wide-field radio-interferometric imaging, and demonstrate it on observations of the ESO~137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image reconstruction builds on a recent ``plug-and-play'' scheme whereby a denoising operator is injected as an image regulariser in an optimisation algorithm, which alternates until convergence between denoising steps and gradient-descent data-fidelity steps. We investigate handcrafted and learned variants of high-resolution high-dynamic range denoisers. We propose a parallel algorithm implementation relying on automated decompositions of the image into facets and the measurement operator into sparse low-dimensional blocks, enabling scalability to large data and image dimensions. We validate our framework for image formation at a wide field of view containing ESO~137-006, from 19 gigabytes of MeerKAT data at 1053 and 1399 MHz. The recovered maps exhibit significantly more resolution and dynamic range than CLEAN, revealing collimated synchrotron threads close to the galactic core. Comment: accepted for publication in ApJL
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Astrophysics - Astrophysics of Galaxies ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing
Subject code	006
Publishing date	2022-07-22
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA)

Thouvenin, Pierre-Antoine / Dabbech, Arwa / Jiang, Ming / Abdulaziz, Abdullah / Thiran, Jean-Philippe / Jackson, Adrian / Wiaux, Yves

II. Code and real data proof of concept

2022

Abstract: In a companion paper, a faceted wideband imaging technique for radio interferometry, dubbed Faceted HyperSARA, has been introduced and validated on synthetic data. Building on the recent HyperSARA approach, Faceted HyperSARA leverages the splitting ... ...

Abstract	In a companion paper, a faceted wideband imaging technique for radio interferometry, dubbed Faceted HyperSARA, has been introduced and validated on synthetic data. Building on the recent HyperSARA approach, Faceted HyperSARA leverages the splitting functionality inherent to the underlying primal-dual forward-backward algorithm to decompose the image reconstruction over multiple spatio-spectral facets. The approach allows complex regularization to be injected into the imaging process while providing additional parallelization flexibility compared to HyperSARA. The present paper introduces new algorithm functionalities to address real datasets, implemented as part of a fully fledged MATLAB imaging library made available on Github. A large scale proof-of-concept is proposed to validate Faceted HyperSARA in a new data and parameter scale regime, compared to the state-of-the-art. The reconstruction of a 15 GB wideband image of Cyg A from 7.4 GB of VLA data is considered, utilizing 1440 CPU cores on a HPC system for about 9 hours. The conducted experiments illustrate the reconstruction performance of the proposed approach on real data, exploiting new functionalities to leverage known direction-dependent effects (DDEs), for an accurate model of the measurement operator, and an effective noise level accounting for imperfect calibration. They also demonstrate that, when combined with a further dimensionality reduction functionality, Faceted HyperSARA enables the recovery of a 3.6 GB image of Cyg A from the same data using only 91 CPU cores for 39 hours. In this setting, the proposed approach is shown to provide a superior reconstruction quality compared to the state-of-the-art wideband CLEAN-based algorithm of the WSClean software. Comment: To appear in MNRAS
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Electrical Engineering and Systems Science - Image and Video Processing ; Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2022-09-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Optimised hybrid parallelisation of a CFD code on Many Core architectures

Jackson, Adrian / Campobasso, M. Sergio

2013

Abstract: COSA is a novel CFD system based on the compressible Navier-Stokes model for unsteady aerodynamics and aeroelasticity of fixed structures, rotary wings and turbomachinery blades. It includes a steady, time domain, and harmonic balance flow solver. COSA ... ...

Abstract	COSA is a novel CFD system based on the compressible Navier-Stokes model for unsteady aerodynamics and aeroelasticity of fixed structures, rotary wings and turbomachinery blades. It includes a steady, time domain, and harmonic balance flow solver. COSA has primarily been parallelised using MPI, but there is also a hybrid parallelisation that adds OpenMP functionality to the MPI parallelisation to enable larger number of cores to be utilised for a given simulation as the MPI parallelisation is limited to the number of geometric partitions (or blocks) in the simulation, or to exploit multi-threaded hardware where appropriate. This paper outlines the work undertaken to optimise these two parallelisation strategies, improving the efficiency of both and therefore reducing the computational time required to compute simulations. We also analyse the power consumption of the code on a range of leading HPC systems to further understand the performance of the code. Comment: Submitted to the SC13 conference, 10 pages with 8 figures
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing
Subject code	621
Publishing date	2013-04-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: BeatBox-HPC simulation environment for biophysically and anatomically realistic cardiac electrophysiology.

Antonioletti, Mario / Biktashev, Vadim N / Jackson, Adrian / Kharche, Sanjay R / Stary, Tomas / Biktasheva, Irina V

PloS one

2017 Volume 12, Issue 5, Page(s) e0172292

Abstract: The BeatBox simulation environment combines flexible script language user interface with the robust computational tools, in order to setup cardiac electrophysiology in-silico experiments without re-coding at low-level, so that cell excitation, tissue/ ... ...

Abstract	The BeatBox simulation environment combines flexible script language user interface with the robust computational tools, in order to setup cardiac electrophysiology in-silico experiments without re-coding at low-level, so that cell excitation, tissue/anatomy models, stimulation protocols may be included into a BeatBox script, and simulation run either sequentially or in parallel (MPI) without re-compilation. BeatBox is a free software written in C language to be run on a Unix-based platform. It provides the whole spectrum of multi scale tissue modelling from 0-dimensional individual cell simulation, 1-dimensional fibre, 2-dimensional sheet and 3-dimensional slab of tissue, up to anatomically realistic whole heart simulations, with run time measurements including cardiac re-entry tip/filament tracing, ECG, local/global samples of any variables, etc. BeatBox solvers, cell, and tissue/anatomy models repositories are extended via robust and flexible interfaces, thus providing an open framework for new developments in the field. In this paper we give an overview of the BeatBox current state, together with a description of the main computational methods and MPI parallelisation approaches.
MeSH term(s)	Action Potentials ; Cardiovascular Diseases/physiopathology ; Computer Simulation ; Electrocardiography ; Heart/physiology ; Humans ; Software
Language	English
Publishing date	2017-05-03
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0172292
Database	MEDical Literature Analysis and Retrieval System OnLINE

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