arXiv subjects

Jamie J. Alnasir

Publications and source records attributed to Jamie J. Alnasir.

6 recordsLinked to original sources

arXiv2026

AI-Driven Scientific Computing Workflows: A Systems Review of Orchestration, Execution, Reproducibility and Provenance

Artificial intelligence (AI) is increasingly embedded within scientific computing workflows that combine simulation, data processing, optimisation, visualisation and experimental or observational components. Learned models may serve as explicit workflow components, retain persistent state and, in adaptive settings, influence subsequent computation. Existing work has characterised scientific workflow management systems, dynamic and steered workflows, AI--HPC coupling motifs and the machine-learning lifecycle, although these areas are often discussed separately. This review brings them together from a systems perspective. We distinguish conventional scientific workflows, machine-learning pipelines, AI-coupled high-performance computing (HPC) workflows and broader automated research workflows, and propose a continuum describing the depth of AI participation from a computational stage to co-adaptive workflow control. The associated systems requirements are organised around five concerns: control and orchestration; compute and execution; data and model state; reproducibility and provenance; and governance and assurance. Representative systems and applications include AI-steered molecular simulation, drug and materials discovery, simulation--surrogate coupling and distributed self-driving laboratories. Workflow-level evaluation is considered in terms of scientific progress, execution cost, data movement, resource use, resilience and decision traceability. We conclude by identifying open problems in dynamic workflow representation, state-aware recovery, heterogeneous scheduling, interoperable data planes, model-mediated decision provenance and reproducible adaptive execution.

cs.DC↗

arXiv2026

Twelve quick tips for designing AI-driven HPC workflows

High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation, traditionally executing deterministic, linear pipelines optimised for predictable performance. However, the pervasive integration of artificial intelligence (AI) and foundation models into scientific research has introduced a fundamentally new computational paradigm. AI-driven workflows are characteristically iterative, data-driven, and probabilistic, introducing unique challenges regarding data gravity, heterogeneous resource management, and complex workflow orchestration. This guide provides twelve practical tips designed to help researchers design efficient, scalable, and reproducible AI-driven HPC workflows. By addressing critical system-level bottlenecks - such as containerisation for environment portability, strategic deployment of job arrays, explicit feedback loop mechanics, and I/O optimisation for small files - this article offers a framework for transitioning from rigid execution pipelines to adaptive, intelligent computational environments. While these architectural principles are broadly applicable across distributed environments, they are particularly tailored to the resource-intensive throughput demands of modern computational biology.

cs.DC↗

arXiv2026

Reimagining the Traditional Flight Computer: E6BJA as a Modern, Multi-Platform Tool for Flight Calculations and Training

Traditional flight computers -- including mechanical "whiz-wheels" (e.g. E6B, CRP series) and electronic flight calculators (e.g. ASA CX-3, Sportys E6-B) -- have long played a central role in flight planning and training within general aviation (GA). While these tools remain pedagogically valuable, their fixed form factors, constrained interaction models, and limited extensibility are increasingly misaligned with the expectations and workflows of pilots operating in modern digital environments. This paper presents E6BJA (Jamie's Flight Computer), a fully featured, multi-platform, software-based flight computer designed natively for Apple iOS, Android, and Microsoft Windows devices, with a complementary web-based implementation. E6BJA reproduces the core calculations of traditional flight computers while extending them through enhanced modelling capabilities and more accurate atmospheric (i.e. ISA-based) and performance calculations, including carburettor icing risk estimation and aircraft-specific weight and balance modelling for common GA aircraft. Each calculator is accompanied by embedded educational monographs explaining underlying assumptions, variables, and equations. We compare E6BJA with mechanical and electronic flight computers across functional, cognitive, and technical dimensions, demonstrating improvements in accuracy, error reduction, discoverability, and educational value. We also discuss design trade-offs associated with native multi-platform development and examine how contemporary mobile computing environments can support safer and more intuitive pre-flight planning. By combining the conceptual rigour of traditional flight planning with modern human-computer interaction design, E6BJA represents a meaningful evolution in pilot-facing flight tools, supporting both computation and instruction in aviation training contexts.

cs.HC↗

arXiv2023

Survey of Information Encoding Techniques for DNA

The yearly global production of data is growing exponentially, outpacing the capacity of existing storage media, such as tape and disk, and surpassing our ability to store it. DNA storage - the representation of arbitrary information as sequences of nucleotides - offers a promising storage medium. DNA is nature's information-storage molecule of choice and has a number of key properties: it is extremely dense, offering the theoretical possibility of storing 455 EB/g; it is durable, with a half-life of approximately 520 years that can be increased to thousands of years when DNA is chilled and stored dry; and it is amenable to automated synthesis and sequencing. Furthermore, biochemical processes that act on DNA potentially enable highly parallel data manipulation. Whilst biological information is encoded in DNA via a specific mapping from triplet sequences of nucleotides to amino acids, DNA storage is not limited to a single encoding scheme, and there are many possible ways to map data to chemical sequences of nucleotides for synthesis, storage, retrieval and data manipulation. However, there are several biological, error-tolerance and information-retrieval considerations that an encoding scheme needs to address to be viable. This comprehensive review focuses on comparing existing work done in encoding arbitrary data within DNA in terms of their encoding schemes, methods to address biological constraints and measures to provide error correction. We compare encoding approaches on the overall information density and coverage they achieve, as well as the data-retrieval method they use (i.e., sequential or random access). We also discuss the background and evolution of the encoding schemes.

q-bio.QM↗

arXiv2022

DNA Storage Error Simulator: A Tool for Simulating Errors in Synthesis, Storage, PCR and Sequencing

DNA has many valuable characteristics that make it suitable for a long-term storage medium, in particular its durability and high information density. DNA can be stored safely for hundreds of years with virtually no degradation, in contrast to hard disk drives which typically last for about 5 years. Furthermore, the duration of DNA-Storage can be extended to potentially up to thousands of years if it is desiccated and cooled in storage. Advances in DNA technologies have made it possible to store the entirety of Wikipedia in a test tube and read that information using a handheld sequencing device, although imperfections in writing (synthesis) and reading (sequencing) need to be mitigated for it to be viable as a mainstream storage medium. New sequencing technologies, such as nanopore sequencing, aim to penetrate the consumer world, thanks to their affordability and size. However, the error characteristics of nanopore sequencing are not yet well characterised. DNA Storage Error Simulator models errors that can be introduced in all the phases of DNA storage workflow, including synthesis, storage, PCR for amplification and finally sequencing. The error characteristics for sequencing and synthesis can be configured in all necessary detail or can be chosen from a predefined set of values based on available literature and our own analysis. Availability: DNA Storage Error Simulator can be accessed online from: https://master.dbahb2jho41s4.amplifyapp.com (https://dnastorage.doc.ic.ac.uk/DNA-error-simulator)

q-bio.QM↗

arXiv2021

Ten Simple Rules for Success with HPC, i.e. Responsibly BASHing that Linux Cluster

High-performance computing (HPC) clusters are widely used in-house at scientific and academic research institutions. For some users, the transition from running their analyses on a single workstation to running them on a complex, multi-tenanted cluster, usually employing some degree of parallelism, can be challenging, if not bewildering, especially for users whose role is not predominantly computational in nature. On the other hand, there are more experienced users, who can benefit from pointers on how to get the best from their use of HPC. This Ten Simple Rules guide is aimed at helping you identify ways to improve your utilisation of HPC, avoiding common pitfalls that can negatively impact other users and will also help ease the load (pun intended) on your HPC sysadmin. It is intended to provide technical advice common to the use of HPC platforms such as LSF, Slurm, PBS/Torque, SGE, LoadLeveler and YARN, the scheduler used with Hadoop/Spark platform.

cs.DC↗