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Jonathan Deakin

PROFILE

Jonathan Deakin

Jonathan Deakin enhanced AArch64 development workflows in the graphcore/pytorch-fork repository by building CI script improvements that enabled local development with custom ComputeLibrary directories, incremental builds, and full CPU-core utilization. Using Python scripting and DevOps practices, he optimized build processes to reduce iteration time and improve reliability for cross-architecture projects. In the uxlfoundation/oneDNN repository, Jonathan extended BrGEMM convolution support on AArch64 to leverage SVE_128 vector extensions, implementing runtime configuration and ISA checks to ensure compatibility and performance. His work demonstrated depth in ARM architecture, low-level C++ programming, and CPU optimization, addressing hardware-specific challenges in modern neural network pipelines.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

2Total
Bugs
0
Commits
2
Features
2
Lines of code
174
Activity Months2

Work History

August 2025

1 Commits • 1 Features

Aug 1, 2025

August 2025: Delivered targeted performance improvement for the uxlfoundation/oneDNN project by extending BrGEMM Convolution support on AArch64 with SVE_128. Implemented enabling/configuration for SVE_128 usage, ensuring correct handling of SIMD widths and ISA compatibility to unlock optimized convolution paths on eligible hardware. This aligns with the roadmap to leverage modern ARM vector extensions and expand hardware coverage.

May 2025

1 Commits • 1 Features

May 1, 2025

Month: 2025-05 Key features delivered: - AArch64 CI Script Enhancements for Local Development and Parallel Builds in graphcore/pytorch-fork, enabling custom ComputeLibrary directories, incremental builds, and full CPU-core utilization to speed up local development and CI. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Improved developer productivity and CI throughput for AArch64 workflows; established scalable baseline for local development and parallel builds, reducing iteration time and enhancing reliability. Technologies/skills demonstrated: - CI scripting, cross-architecture (AArch64), incremental and parallel builds, build optimization, version-control traceability.

Activity

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Quality Metrics

Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

C++Python

Technical Skills

ARM ArchitectureCPU OptimizationContinuous IntegrationConvolutional Neural NetworksDevOpsLow-Level ProgrammingPython ScriptingSIMD Instructions

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

graphcore/pytorch-fork

May 2025 May 2025
1 Month active

Languages Used

Python

Technical Skills

Continuous IntegrationDevOpsPython Scripting

uxlfoundation/oneDNN

Aug 2025 Aug 2025
1 Month active

Languages Used

C++

Technical Skills

ARM ArchitectureCPU OptimizationConvolutional Neural NetworksLow-Level ProgrammingSIMD Instructions

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