Joseph John
Staff Scientist at National Computational Infrastructure
North Canberra, Australia
Summary
👤
Senior🎓
Top SchoolJoseph John is a Staff Scientist and HPC specialist with a decade of experience optimizing multi-threaded and distributed systems for performance and scalability. He combines deep academic rigor—currently completing a PhD in Computer Science at ANU—with hands-on expertise in MPI, OpenMP, CUDA and task-based programming models to deliver high-performance software for national research infrastructure. At the National Computational Infrastructure he moved from training roles into a technical staff position, translating research ideas into production-ready optimisations for large-scale compute environments. Previously he balanced academia and industry as a researcher, instructor and early-stage co-founder, giving him a rare mix of teaching, research and operational engineering experience. Based in North Canberra, he is particularly focused on squeezing efficiency from heterogeneous architectures and bridging the gap between HPC research and practical deployment.
10 years of coding experience
6 years of employment as a software developer
Australian National UniversityM-Tech, Network Engineering, M-Tech, Network Engineering at Rajagiri School of Engineering & TechnologyB-Tech, Computer Science and Engineering, B-Tech, Computer Science and Engineering at MES College of Engineering, Kuttippuram
Hindi, English, Malayalam
Github Skills (63)
numa10
parsec10
nodes10
computation9
dataflow-programming9
heterogeneous9
architecture8
communications8
configurations7
scheduler7
linux7
gpu7
sbml7
architectures7
gpu-acceleration7
Programming languages (6)
Github contributions (5)
josephjohnjj/OpenMP
Aug 2017 - Jan 2021
Contributions:19 commits, 1 PR, 21 pushes in 3 years 5 months
josephjohnjj/parsec
Mar 2022 - Dec 2023
PaRSEC is a generic framework for architecture aware scheduling and management of micro-tasks on distributed, GPU accelerated, many-core heterogeneous architectures. PaRSEC assigns computation threads to the cores, GPU accelerators, overlaps communications and computations and uses a dynamic, fully-distributed scheduler based on architectural features such as NUMA nodes and algorithmic features such as data reuse.
Contributions:461 pushes, 98 branches in 1 year 8 months
schedulerparsecreusethreadsalgorithmic-features
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