atcollab/at
Apr 2025 – Feb 2026
4 Months active
Languages Used
CC++PythonMATLAB
Technical Skills
C++CUDAGPU ComputingOpenCLParallel ComputingPerformance Optimization
Jean-Luc PONS
PROFILE
Jean-luc Pons
Worked on the atcollab/at repository to deliver GPU-accelerated particle tracking and simulation features for particle accelerator modeling. Developed core functionality in C++ and Python, integrating CUDA and OpenCL to enable high-throughput, scalable simulations. Implemented GPU context management, kernel generation, and Python-based workflow selection, allowing users to leverage GPU resources efficiently. Enhanced simulation fidelity by adding GPU-accelerated corrector element modeling and flexible element tracking for targeted analysis. Optimized performance for boundary projections and improved robustness through error handling and documentation updates. Addressed compatibility issues in GPU tracking workflows, demonstrating strong debugging skills and attention to stability in complex parallel computing environments.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
3
Lines of code
7,716
Activity Months4
Your Network
30 peopleSame Organization
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Work History
February 2026
1 Commits
Feb 1, 2026February 2026: Focused on stabilizing GPU tracking workflows by addressing keyword compatibility. Delivered a targeted bug fix in the atcollab/at repository to prevent errors when GPU tracking is enabled by removing unsupported parameters (harmonic_number and fillpattern) from the GPU path.
1 Commits
Feb 1, 2026February 2026: Focused on stabilizing GPU tracking workflows by addressing keyword compatibility. Delivered a targeted bug fix in the atcollab/at repository to prevent errors when GPU tracking is enabled by removing unsupported parameters (harmonic_number and fillpattern) from the GPU path.
February 2026
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025: Delivered targeted simulation analysis enhancements and performance optimizations for atcollab/at. The new flexible element-tracking feature allows specifying start/end elements to focus analyses on specific lattice sections, enabling more precise diagnostics and reporting. Optimized the acceptance boundary projection for radial and Cartesian grid modes, reducing compute time during projections and boundary checks. Introduced an integrator flag to enable GPU-based simulations, paving the way for improved throughput on GPU-enabled workloads. Implemented minor robustness fixes and documentation updates to improve reliability and clarity. These changes improve analysis precision, reduce runtime, and support scalable simulations.
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025: Delivered targeted simulation analysis enhancements and performance optimizations for atcollab/at. The new flexible element-tracking feature allows specifying start/end elements to focus analyses on specific lattice sections, enabling more precise diagnostics and reporting. Optimized the acceptance boundary projection for radial and Cartesian grid modes, reducing compute time during projections and boundary checks. Introduced an integrator flag to enable GPU-based simulations, paving the way for improved throughput on GPU-enabled workloads. Implemented minor robustness fixes and documentation updates to improve reliability and clarity. These changes improve analysis precision, reduce runtime, and support scalable simulations.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for atcollab/at. Key feature delivered: GPU-Accelerated CorrectorPass integration for atgpu, enabling simulation of corrector elements with specified kick angles. Build system and lattice mapping updated to support accurate magnetic-element modeling in the GPU-accelerated path. Impact: higher fidelity simulations, potential performance gains on GPU, and a stronger foundation for expanding GPU-accelerated accelerator modeling. Major bugs fixed: none reported this month. Technologies demonstrated: GPU-based computation, module integration, build-system enhancements, lattice mapping.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for atcollab/at. Key feature delivered: GPU-Accelerated CorrectorPass integration for atgpu, enabling simulation of corrector elements with specified kick angles. Build system and lattice mapping updated to support accurate magnetic-element modeling in the GPU-accelerated path. Impact: higher fidelity simulations, potential performance gains on GPU, and a stronger foundation for expanding GPU-accelerated accelerator modeling. Major bugs fixed: none reported this month. Technologies demonstrated: GPU-based computation, module integration, build-system enhancements, lattice mapping.
June 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered GPU-accelerated particle tracking in the Accelerator Toolbox (AT) library with CUDA and OpenCL support. Implemented GPU context management, GPU kernel generation, and Python integration to select and execute GPU-accelerated workflows. This work lays the groundwork for significantly faster particle-tracking simulations and enables scalable GPU utilization across AT workloads.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered GPU-accelerated particle tracking in the Accelerator Toolbox (AT) library with CUDA and OpenCL support. Implemented GPU context management, GPU kernel generation, and Python integration to select and execute GPU-accelerated workflows. This work lays the groundwork for significantly faster particle-tracking simulations and enables scalable GPU utilization across AT workloads.
Activity
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Quality Metrics
Correctness90.0%
Maintainability82.6%
Architecture90.0%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++MATLABPython
Technical Skills
C++CUDACode RefactoringError handlingGPU ComputingGPU programmingMATLABOpenCLParallel ComputingParticle Accelerator SimulationPerformance OptimizationPythonPython developmentTesting
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