October 2025 performance summary for ammarhakim/gkeyll and ammarhakim/gkylcas. Delivered a major Collisionless GK module and flux/kernels refactor with multiblock support, updated device code, and expanded tests. Implemented GK multiblock core improvements (no_collisionless_terms) with extended tests and header cleanup. Enabled GK_GEOMETRY_FROMFILE multiblock geometry by populating jacobgeo_inv_sync on read and allocating it on the GPU. Strengthened stability and accuracy with precomputed Jacobian ratios at interblock boundaries for collisionless flux, and completed memory bug fixes with CUDA/code cleanup and memory-layout optimizations. Reworked LBO/BGK internals with function pointers for runtime selection and memory layout improvements to reduce branchiness and improve performance. Expanded regression test coverage for time-independent collisions and normNu-based collision tables. These changes improve cross-block conservation, robustness, GPU readiness, and maintainability, translating to more reliable simulations and faster iteration cycles.

September 2025 monthly summary: Key ongoing work centered on expanding, stabilizing, and proving value from anomalous diffusion support and boundary-condition handling across two repositories (ammarhakim/gkylcas and ammarhakim/gkeyll). The main accomplishments include delivering a Maxima-based updater for anomalous diffusion and refactoring kernel generation to support multiple boundary conditions, sides, and neighbor-specific Jacobian inverses, enabling more accurate and flexible diffusion simulations. The work established a modular diffusion operator architecture (moved to its own app/module in gkeyll) and introduced a dedicated anomalous_diffusion input, with jacobgeo_inv propagation to relevant kernels, improving extensibility and conservation characteristics in single- and multi-block setups. These changes laid the groundwork for future enhancements (e.g., chi/D profiles) while preserving energy and particle conservation in core tests. In GK central code, unified particle/field BC types across SB/MB solvers, simplified BC specification, and updated regression tests for consistency, reducing test fragility and maintenance cost. Major bug fixes targeted GPU and stability issues, including: fixes to device boundary_flux and anomalous_diff updaters, correct copying of nodal arrays to GPU in the gk_geom path, resolution of segfaults in radiation code, corrected cflrate allocations, and alignment of diffusion-related jacobgeo_inv across blocks. Additional bug fixes addressed TES/BC interactions and device signatures for GK anomalous diffusion; reflect BC handling for GK species and prevention of invalid parallel-only setups. These fixes significantly improved runtime stability, memory correctness, and overall reliability of GK diffusion-related simulations. Business value and impact: broadened diffusion modeling capabilities with configurable BCs and robust multi-block support, enabling researchers to simulate more realistic physics with confidence. The refactorings and BC unification reduce maintenance burden and risk in future changes, while test suite improvements increase regression safety and deployment confidence. Overall, the month delivered measurable improvements in accuracy, stability, performance, and maintainability, directly supporting downstream physics campaigns and production runs. Technologies/skills demonstrated: CUDA/GPU memory management and kernel safety, boundary-condition modeling (including ghost/skin-cell interfaces), modular software architecture (apps/modules), BC unification, input system evolution, regression/test harness improvements, and performance-focused refactoring and code cleanup.

In August 2025, GK and GKylCAS projects advanced build reliability, feature delivery, diagnostics, and maintainability across CPU and GPU workflows. Major improvements include build-system hardening for nvcc compatibility and ADAS integration, improved path handling for ADAS assets, and expanded Makefile capabilities; addition of a GK app heat source with a regression test and new diagnostics for the GK heating term; enhanced boundary and diffusion diagnostics with updated kernels (including GK collisionless updates and multi-equation boundary flux support); substantive code cleanup and refactor to improve initialization and maintainability; documentation updates for single-app builds and usage of Valgrind/compute-sanitizer; and GKylCAS diffusion kernel generation enhancements that improve boundary diffusion accuracy. These changes collectively improve reliability, observability, and performance readiness for GPU-enabled simulations while strengthening maintainability and reproducibility.

July 2025 monthly summary for ammarhakim/gkeyll focusing on robustness, configurability, and repository hygiene. Delivered numerical stability improvements in cross-primitive moment calculations, extended GPU kernel configurability, and improved code maintenance through git hygiene; verified stability via regression tests.

June 2025 performance highlights for ammarhakim/gkeyll: Delivered GPU-accelerated enhancements, strengthened gyrokinetic (GK) conservation diagnostics, improved neutrals/grid robustness, and expanded CLI tooling to enable parameter scans and batch simulations. These changes accelerate simulations on GPUs, reduce memory and floating-point stability risks, and improve workflow efficiency for large-scale campaigns.

Month: 2025-05. Delivered substantial GK LBO and ecosystem improvements across gkeyll and gkylcas, boosting numerical stability, energy conservation, diagnostic reliability, and restart robustness while laying groundwork for future moments API.

April 2025 monthly summary for developer performance review. The month saw targeted numerical robustness and usability improvements across two repositories (ammarhakim/gkeyll and ammarhakim/gkylcas), with a clear emphasis on stability, accuracy, and maintainability in production-like runs. Key features were implemented with unit tests and diagnostic considerations, while memory hygiene and test stability were greatly improved to enable longer, more reliable simulations.

March 2025 was focused on delivering scalable, physics-enabled capabilities across gkeyll and gkylcas, while tightening reliability and deployment workflows. Key features delivered include infrastructure for perpendicular Poisson solves across radially connected multiblocks in gkeyll, and a respected refactor of gk_multib_field_new into smaller, reusable methods to support scalable multi-block Poisson solves. In the gflux/bflux workflow, we introduced a Flux Moment API, stored boundary flux in a separate array for the new flow, added an interface to write boundary flux moments, and enabled diagnostic moments for gk_species_bflux, all contributing to more accurate boundary physics. We also extended input and deployment capabilities with a multiblock ASDEX input file and updates to the Perlmutter SLURM script to streamline deployment, plus related advances in input-driven modeling. In gkylcas, translation-dimension kernel generation (translate_dim maxima) enables translating configuration-space fields to lower dimensions with flexible evaluation at cell boundaries or centers. Major bugs fixed include: missing headers, corrected scaling of sheath values and averaging, wrong parent-range handling for perp field solve subranges, protections for perpendicular multiblock solve initialization at cdim=1, and tokamak geometry tracing corrections; as well as boundary surface kernel fix, memory management and deallocation improvements, initial neutral density Jacobian enablement on GPU, and broader GPU kernel stability improvements. Overall impact: more robust, scalable, and realistic simulations, improved energy conservation controls, and streamlined development/deployment. Technologies demonstrated: GPU kernel debugging and stabilization, configuration-space field translation/projection, memory management, code refactoring for maintainability, automated testing, and deployment automation.

February 2025 focused on stabilizing GK physics, expanding geometry support, and strengthening diagnostics to improve observability, reliability, and scalability of GK simulations across CPU/GPU platforms. Major improvements include feature additions for initial field specification and rich diagnostics, along with substantial bug fixes that improved energy balance and boundary handling in nonperiodic and curved geometries.

Concise monthly summary for 2025-01 focusing on key features delivered, major bugs fixed, impact, and technologies demonstrated across two repositories (ammarhakim/gkeyll and ammarhakim/gkylcas).

December 2024: Delivered DG interpolation core enhancements in ammarhakim/gkeyll, including a directional updater that interpolates along a single direction, new 1D kernels, and a recursive multi-dimensional interpolation approach, accompanied by a GPU revamp and expanded unit tests. Fixed a memory error issue in the DG interp unit tests to ensure sanitizer/valgrind compatibility and preserved moment conservation across resolutions. Refined Array integration API to simplify volume term configuration and support separate weight ranges, with unit tests passing and GK energy consistent across 2x/3x configurations. Added a GK app L2 norm diagnostic to gauge the severity of L2 norm problems and guide tuning. Introduced HamiltonianMoment support for GK moment updaters and the GPU portion of mom_gyrokinetic, enabling H = m v^2/2 + mu B + q phi moments with tests passing on CPU and GPU.

Monthly Summary for 2024-11: Focused on delivering memory-efficient kernel improvements, enhanced interpolation capabilities, restart-enabled workflows, GPU readiness, and stabilization across MB GK runs. Key results span two repositories (ammarhakim/gkeyll and ammarhakim/gkylcas) with improvements that translate to higher throughput, better scalability, and more reliable simulations for production workloads. Key achievements (top 5): - New ssfg kernels with fixed memory usage (ammarhakim/gkeyll): reduced memory footprint for SSFG kernels, enabling larger problem instances and more predictable resource utilization. Commit: 93832f25026de609cfee5fb51419e07136cd15a4. - DG/interpolation restart workflow: Plug in DG interpolation to GK app to support restarts with different resolution (gkyl-based); enables flexible restart strategies and smoother transitions between resolutions. Commit: f00da2ca54b0e7bb91c2ce670edb03a34e0801a7. - Interpolation kernel enhancements for multi-dimensional interpolation (ammarhakim/gkylcas): consolidated edits to interpolation kernel generation for multi-dim usage and code-path simplifications to enable higher-dimension interpolation while maintaining testing integrity. Commits: 8666602e2e7bb612326499d6e4939635790355bd; 752105c8dcee9bb97fefd3e28f14178081159f19. - GPU readiness: rescale_ghost_jacf: GPU support/mods with unit tests passing; MB GK Sol simulation also runs on GPUs, improving throughput for GPU-enabled runs. Commit: 03a56f152473f8e747cdbd3ad7fa6d80758630b8; related test: 864fc2893d399620241c8fcdba818e9311f6f9b4. - Stability in VP restarts and multi-block runs: fixes for segfaults during VP restarts and restart-related test discrepancies, including ensuring restart parity with single-block runs and updated external-field recomputation. Commits: ebb70020d79372e73842a353cafa81245ccecf9a, plus subsequent fixes in 8df7a9bc08af26dbbf8af2496d6bcbc3af0e7b37. Major bugs addressed (selected): - MB GK sync ranges creation: corrected the logic for MB GK sync ranges to ensure proper range construction across blocks. Commit: fc692f6a30752b36b9be74a0494438d2d9c23d0a. - Global skin/ghost range creation affecting div/mul by jacobian: fixed border handling to ensure correct operations across cross-block boundaries. Commit: 64c55b68a4a1c45f90f18ea51fd8f49931d04d43. - Wrong basis passed to updater in MB app; multib_step_b1b2b3 test: corrected basis passing to updater and ensured tests cover 1,2,3,4 configurations as applicable. Commit: c65c9ae3ab2ddf0341b10e79a8e9d7d0a53f4629. - Te_min vs Te_min_J naming (SI units): clarified SI-unit semantics and headers; updated tests accordingly. Commit: d8639660f062fe7e12ee6968ebfd84faf93b7bd0. - Boundary_flux GPU compatibility: fixed to work identically on GPU and CPU, aligning results. Commit: 1084b0be561b8811ba266aa15594bd5f14e56c86. Overall impact and business value: - Improved memory efficiency enables larger, more accurate simulations on the same hardware, expanding the practical problem size and reducing per-simulation costs. - Enhanced interpolation and restart capabilities reduce workflow friction, enabling flexible experiment design and faster iteration cycles. - GPU readiness accelerates compute-heavy workloads, delivering higher throughput for production runs and enabling more timely results. - Strengthened stability and regression coverage across MB GK and VP restart scenarios improves reliability and trust in simulation outcomes for product teams. Technologies/skills demonstrated: - C/C++ performance-oriented development, kernel design, and memory management for GK/SFG workloads. - GPU acceleration readiness (CUDA) and GPU-enabled MB GK scenarios. - Advanced interpolation algorithms and code generation for multi-dimensional workloads. - Test-driven improvements with unit and regression tests, and script-driven kernel generation via Maxima-driven tooling. - Cross-repo collaboration and integration between ammarhakim/gkeyll and ammarhakim/gkylcas to advance end-to-end capabilities.

Monthly summary for 2024-10: Focused on delivering a critical feature update for gyrokinetic simulations and strengthening test infrastructure. Key outcomes include a new updater with flip-before-mul behavior and enhanced boundary condition handling, plus modernization and stabilization of regression tests to ensure host/device compatibility and valgrind cleanliness. These efforts improve simulation accuracy, robustness, and maintainability, supporting more reliable performance on GPU-accelerated workflows when resources are available. Business value and technical impact: - More accurate and robust gyrokinetic simulations due to improved boundary handling and unit-test coverage. - Reduced risk in code changes through stabilized regression tests and valgrind-clean verification. - Clearer, more maintainable test infrastructure and documentation through test renames and code fixes. - Preparedness for GPU-accelerated execution with groundwork for GPU compatibility and error fixes, awaiting hardware.