QMCSoftware/QMCSoftware
Oct 2024 – Jan 2026
15 Months active
Languages Used
CC++PythonJupyter NotebookNumPyPyTorchSciPyTorch
Technical Skills
Algorithm OptimizationCode RefactoringDocumentationKernel MethodsLinear AlgebraNumerical Analysis
alegresor
PROFILE
Alegresor
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
411Total
Bugs
48
Commits
411
Features
127
Lines of code
1,205,931
Activity Months15
Work History
January 2026
12 Commits • 3 Features
Jan 1, 2026January 2026 performance summary for QMCSoftware/QMCSoftware focused on reliability, usability, and maintainability of the DigitalNet family and related tooling. Delivered user guidance, improved error handling, extended base support, and comprehensive documentation updates, reinforcing business value through reduced misuse, better diagnostics, and easier future enhancements.
12 Commits • 3 Features
Jan 1, 2026January 2026 performance summary for QMCSoftware/QMCSoftware focused on reliability, usability, and maintainability of the DigitalNet family and related tooling. Delivered user guidance, improved error handling, extended base support, and comprehensive documentation updates, reinforcing business value through reduced misuse, better diagnostics, and easier future enhancements.
January 2026
December 2025
30 Commits • 11 Features
Dec 1, 2025December 2025 performance for QMCSoftware/QMCSoftware: Delivered foundational API enhancements, production-readiness work, and robust testing/documentation improvements. Key progress spans API interoperability, groundwork for advanced models, release readiness, and contributor experience improvements, driving both research reliability and faster deployment.
December 2025
30 Commits • 11 Features
Dec 1, 2025December 2025 performance for QMCSoftware/QMCSoftware: Delivered foundational API enhancements, production-readiness work, and robust testing/documentation improvements. Key progress spans API interoperability, groundwork for advanced models, release readiness, and contributor experience improvements, driving both research reliability and faster deployment.
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Focused on improving sampling reliability and performance in the ACM TOMs demo within the QMCSoftware/QMCSoftware repository. Delivered a targeted feature that updates pointsets to 2^10 samples, significantly increasing accuracy and demo throughput. Also optimized sample generation and adjusted execution counts to enhance efficiency. No major bugs fixed in this period.
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Focused on improving sampling reliability and performance in the ACM TOMs demo within the QMCSoftware/QMCSoftware repository. Delivered a targeted feature that updates pointsets to 2^10 samples, significantly increasing accuracy and demo throughput. Also optimized sample generation and adjusted execution counts to enhance efficiency. No major bugs fixed in this period.
October 2025
September 2025
20 Commits • 5 Features
Sep 1, 2025September 2025 monthly summary for QMCSoftware/QMCSoftware focused on expanding community contribution capabilities, improving probabilistic analysis UX, and strengthening kernel modeling capabilities, while modernizing packaging and internal APIs. Delivered governance-ready contribution workflows, enhanced end-user notebooks and plots for probabilistic failure analysis, and a suite of adaptive/multitask kernel enhancements. Packaging/versioning updates and API modernization reduced maintenance burden and set the foundation for faster future releases. Result: clearer contribution paths, faster scientific workflows, improved model flexibility and stability, and a streamlined release process.
September 2025
20 Commits • 5 Features
Sep 1, 2025September 2025 monthly summary for QMCSoftware/QMCSoftware focused on expanding community contribution capabilities, improving probabilistic analysis UX, and strengthening kernel modeling capabilities, while modernizing packaging and internal APIs. Delivered governance-ready contribution workflows, enhanced end-user notebooks and plots for probabilistic failure analysis, and a suite of adaptive/multitask kernel enhancements. Packaging/versioning updates and API modernization reduced maintenance burden and set the foundation for faster future releases. Result: clearer contribution paths, faster scientific workflows, improved model flexibility and stability, and a streamlined release process.
August 2025
21 Commits • 6 Features
Aug 1, 2025Concise monthly summary for QMCSoftware/QMCSoftware (2025-08): Delivered a comprehensive upgrade to documentation, tests, and CI processes, enabling more reliable builds, better docs rendering, and streamlined deployment pipelines. Released QMCPy v2.0 with supporting version bumps (including v2.0 and v1.6.3.2a) to tighten release governance and reproducibility. Stabilized FastGPS integration and dtype handling, reducing runtime issues and improving numerical correctness. Enabled GBM demo to run end-to-end within batch workflows, expanding demonstrable functionality. Enhanced notebook and documentation coverage (ACM TOMS, JOSS notebooks, and doctests) plus sponsor logos to improve visibility and onboarding.
21 Commits • 6 Features
Aug 1, 2025Concise monthly summary for QMCSoftware/QMCSoftware (2025-08): Delivered a comprehensive upgrade to documentation, tests, and CI processes, enabling more reliable builds, better docs rendering, and streamlined deployment pipelines. Released QMCPy v2.0 with supporting version bumps (including v2.0 and v1.6.3.2a) to tighten release governance and reproducibility. Stabilized FastGPS integration and dtype handling, reducing runtime issues and improving numerical correctness. Enabled GBM demo to run end-to-end within batch workflows, expanding demonstrable functionality. Enhanced notebook and documentation coverage (ACM TOMS, JOSS notebooks, and doctests) plus sponsor logos to improve visibility and onboarding.
August 2025
July 2025
64 Commits • 18 Features
Jul 1, 2025July 2025 performance snapshot for QMCSoftware/QMCSoftware. This cycle focused on delivering a refreshed and expanded Demos Suite, strengthening cross‑platform reliability, and advancing kernel/mathematics capabilities while improving documentation and testing practices. Key deliveries and outcomes: Key features delivered - Demos Suite Expansion and Modernization: added/upgraded demos (dnb2, lattice random generator, ray tracing, asian option MC, CV demo, option pricing demo) and completed blog integration to support code demos and marketing use cases. Representative commits included demo updates and blog demo integration. - Build Configuration and Versioning: patching pyproject.toml to reflect demos/tooling changes and version bumps to reflect the new tooling stack. - Windows Compatibility Enhancements: improved Windows setup by enabling manual C library loading, setup verification patches, and compatibility validation. - Documentation and Testing Improvements: comprehensive documentation enhancements, doctest cleanup, aliases/document updates, and testing infrastructure refinements. - Kernel and Math Library Enhancements: added support for common kernels, scaffolded multitask kernels, introduced kernel derivatives, and progressed Gaussian integrals tests; positioned for torch/backed implementations. Major bugs fixed - Codebase cleanup to reduce clutter and improve maintainability. - Testing adjustments and locale/test fixes to stabilize cross-backend behavior. - Minimal Working Examples (MWEs) correctness fixes. - Patch corrections for FastGPS and related testing patches. Overall impact and accomplishments - Business value: richer, verifiable demos enabling customer demonstrations and faster feature evaluation; improved reliability and onboarding through cross‑platform stability and better docs/tests; groundwork for performance benchmarks and deeper analytics with timing improvements. - Technical leadership: advanced kernel/maths capabilities, modular refactoring, and a more robust testing/doctest ecosystem to accelerate future iterations. Technologies/skills demonstrated - Python packaging and tooling (pyproject.toml, v1.6.x and qmctoolscl upgrades). - Cross‑platform software engineering (Windows setup, C library integration). - Documentation engineering (MWE docs, doctests, doc snippets). - Kernel development and mathematical tooling (common kernels, multitask kernels, derivatives, Gaussian computations).
July 2025
64 Commits • 18 Features
Jul 1, 2025July 2025 performance snapshot for QMCSoftware/QMCSoftware. This cycle focused on delivering a refreshed and expanded Demos Suite, strengthening cross‑platform reliability, and advancing kernel/mathematics capabilities while improving documentation and testing practices. Key deliveries and outcomes: Key features delivered - Demos Suite Expansion and Modernization: added/upgraded demos (dnb2, lattice random generator, ray tracing, asian option MC, CV demo, option pricing demo) and completed blog integration to support code demos and marketing use cases. Representative commits included demo updates and blog demo integration. - Build Configuration and Versioning: patching pyproject.toml to reflect demos/tooling changes and version bumps to reflect the new tooling stack. - Windows Compatibility Enhancements: improved Windows setup by enabling manual C library loading, setup verification patches, and compatibility validation. - Documentation and Testing Improvements: comprehensive documentation enhancements, doctest cleanup, aliases/document updates, and testing infrastructure refinements. - Kernel and Math Library Enhancements: added support for common kernels, scaffolded multitask kernels, introduced kernel derivatives, and progressed Gaussian integrals tests; positioned for torch/backed implementations. Major bugs fixed - Codebase cleanup to reduce clutter and improve maintainability. - Testing adjustments and locale/test fixes to stabilize cross-backend behavior. - Minimal Working Examples (MWEs) correctness fixes. - Patch corrections for FastGPS and related testing patches. Overall impact and accomplishments - Business value: richer, verifiable demos enabling customer demonstrations and faster feature evaluation; improved reliability and onboarding through cross‑platform stability and better docs/tests; groundwork for performance benchmarks and deeper analytics with timing improvements. - Technical leadership: advanced kernel/maths capabilities, modular refactoring, and a more robust testing/doctest ecosystem to accelerate future iterations. Technologies/skills demonstrated - Python packaging and tooling (pyproject.toml, v1.6.x and qmctoolscl upgrades). - Cross‑platform software engineering (Windows setup, C library integration). - Documentation engineering (MWE docs, doctests, doc snippets). - Kernel development and mathematical tooling (common kernels, multitask kernels, derivatives, Gaussian computations).
June 2025
77 Commits • 20 Features
Jun 1, 2025June 2025 monthly summary for QMCSoftware/QMCSoftware focusing on delivering business-value features, stabilizing the codebase, and expanding capabilities across Monte Carlo methods, ML integrations, and demonstrations.
77 Commits • 20 Features
Jun 1, 2025June 2025 monthly summary for QMCSoftware/QMCSoftware focusing on delivering business-value features, stabilizing the codebase, and expanding capabilities across Monte Carlo methods, ML integrations, and demonstrations.
June 2025
May 2025
34 Commits • 14 Features
May 1, 2025Monthly Summary for 2025-05 (QMCSoftware/QMCSoftware): Key features delivered: - Documentation improvements: index.md for docs and cleanup of discrete distributions docs, enabling easier onboarding and better reference for users. (Commits aa56ae641eedc5cd9a3389f609129588aabbc99d; 921ab3df40ec27dd96e60532f8593c994300875b) - True measures module: Introduced true measures and updated them; linting started and finished to ensure code quality and maintainability. (Commits 343bbebcd2cd796078e487bd1f5f1c447427554f; ed3734d24eff2785da4452de1b2924bbf85c1be1; 998e73022e09b8f5dee25c01480f0fc0a8bb10a8; ffe4bc18bc212d929d00be8e0dd62c9092fe9332) - Halton, uniform, and lattice improvements: cleanup of iid std uniform and lattice, updates to uniform, and Halton-related tests and default backend to improve reliability and performance. (Commits 4a8ebf73474bb58fcaf6c08750bbf30d15329e6a; 376ef695abc74edcdeb171012d0dc80a46dbbfd7; be7afd6c7fd9c3450c855e25f46db1ce84f03904; 9a0a110b30031d9fb0ffe8533cec82944e029d26) - Integrands and sensitivity analysis: Ishigami patch, conversion of some integrands, added sensitivity indices, and European option modeling to broaden analysis capabilities. (Commits fdd3cfcf92774d057278b0752b99158fa4a458e5; 9e7be2cd353a1bfefdc3666bb61aef95f4197994; 8cec0faf3929943ef25900f5a8aedf5f517cea0a; 2e0d11922a731bfd3030ac5974a2a7e174cc7208) - DNB2 implementation: Added DNB2 component to extend robustness of the derivative-based modeling stack. (Commit cf4a7aaa05ea0f89b128db98eb7af1bff6d837cc) - Financial option modeling and observations: Initial unified financial option implementation with docs and both fine and coarse observations, expanding business applicability. (Commits 70f1581c35054e6d4b1b10407bb3c2e70bf2fad2; b1ae2ad036e3999447f4bcb70102a3b90e658e64; 93cf47fa9d4a711ed3bd7cff69fddb6fa80fd188) - Multilevel groundwork: Preliminary scaffolding and experimental groundwork to enable multilevel methods. (Commit 87f04befe39dfabe0fb4f4d58095cddb4e4ed5f9) - Batch shapes UX improvements: Made batch shapes more intuitive and predictable for users. (Commit 80f012af2435ab488904a76a1e4cbf1afa080a54) - Add __call__ API to integrands: Exposed __call__ for direct evaluation in user code, simplifying usage. (Commit 4156542757756d3955036aa6086b90863301b21e) - Cub QMC CLT and LDG development: Progress across scalar functions, sensitivity indices, lattice and vectorized implementations, and related updates. (Multiple commits: 6cebba208ef11bd7d03ffb7590727596f227a5e6; 394c8d92fc9669b0bf530e9ee2ea0a5a711f3b37; 38954bff08df0c16a6363a53684677ece9e8c313; 121eec2ba8331967933968215bedb3bf6a35fb09; 87f4162f3246003beb926e6fa532bf1bb7bdf6d1; 2b035ae5b3692f7b5417425109b68fc83aedac2c; 101dccea4b812c61434c888410f2cf466a1fe38a) - Vectorized integrand implementation: Reworked to a vectorized approach to boost performance and scalability. (Commit 74ca5096c6709a011d4440ea794096d814156bfc) - Doctests passing/QA stabilization: Fixed doctests to ensure reliable test outcomes and improve QA stability. (Commit e7ebcb1dca426ecda4315118bdabf2e7f4e11211) - Start CVS module initialization: Initiated cross-validation workflow/module initialization to support broader model validation. (Commit 19cbdf17f73fd79e124a7425d13e0e6614281f97) - Control variates complete: Implemented and completed the control variates variance reduction technique to improve estimation efficiency. (Commit e092459110f60468b411699b312c9a31da4475ca) Major bugs fixed: - Patch Bayesian logistic regression coefficients to fix numerical issues and improve stability. - AbstractStoppingCriterion rename: Refactor rename for clarity and consistency. - Doctests passing: Stabilized doctest behavior to improve QA reliability. Overall impact and accomplishments: - Substantial improvement in code quality, reliability, and test stability across the project, with a stronger foundation for statistical methods and QMC-based acceleration. - Significant performance enhancements through vectorization, multilevel groundwork, and lattice-based methods, enabling faster experiments and scalable analyses. - Expanded business value through a unified financial option modeling framework, DNB2 integration, and cross-validation tooling, broadening applicability and confidence in results. Technologies/skills demonstrated: - Python-based numerical methods, QMC, lattice methods, vectorization, and sensitivity analysis. - Software engineering practices including linting, doctest stabilization, and documentation discipline. - Multilevel and cross-validation concepts, performance-oriented refactoring, and UX improvements for end users.
May 2025
34 Commits • 14 Features
May 1, 2025Monthly Summary for 2025-05 (QMCSoftware/QMCSoftware): Key features delivered: - Documentation improvements: index.md for docs and cleanup of discrete distributions docs, enabling easier onboarding and better reference for users. (Commits aa56ae641eedc5cd9a3389f609129588aabbc99d; 921ab3df40ec27dd96e60532f8593c994300875b) - True measures module: Introduced true measures and updated them; linting started and finished to ensure code quality and maintainability. (Commits 343bbebcd2cd796078e487bd1f5f1c447427554f; ed3734d24eff2785da4452de1b2924bbf85c1be1; 998e73022e09b8f5dee25c01480f0fc0a8bb10a8; ffe4bc18bc212d929d00be8e0dd62c9092fe9332) - Halton, uniform, and lattice improvements: cleanup of iid std uniform and lattice, updates to uniform, and Halton-related tests and default backend to improve reliability and performance. (Commits 4a8ebf73474bb58fcaf6c08750bbf30d15329e6a; 376ef695abc74edcdeb171012d0dc80a46dbbfd7; be7afd6c7fd9c3450c855e25f46db1ce84f03904; 9a0a110b30031d9fb0ffe8533cec82944e029d26) - Integrands and sensitivity analysis: Ishigami patch, conversion of some integrands, added sensitivity indices, and European option modeling to broaden analysis capabilities. (Commits fdd3cfcf92774d057278b0752b99158fa4a458e5; 9e7be2cd353a1bfefdc3666bb61aef95f4197994; 8cec0faf3929943ef25900f5a8aedf5f517cea0a; 2e0d11922a731bfd3030ac5974a2a7e174cc7208) - DNB2 implementation: Added DNB2 component to extend robustness of the derivative-based modeling stack. (Commit cf4a7aaa05ea0f89b128db98eb7af1bff6d837cc) - Financial option modeling and observations: Initial unified financial option implementation with docs and both fine and coarse observations, expanding business applicability. (Commits 70f1581c35054e6d4b1b10407bb3c2e70bf2fad2; b1ae2ad036e3999447f4bcb70102a3b90e658e64; 93cf47fa9d4a711ed3bd7cff69fddb6fa80fd188) - Multilevel groundwork: Preliminary scaffolding and experimental groundwork to enable multilevel methods. (Commit 87f04befe39dfabe0fb4f4d58095cddb4e4ed5f9) - Batch shapes UX improvements: Made batch shapes more intuitive and predictable for users. (Commit 80f012af2435ab488904a76a1e4cbf1afa080a54) - Add __call__ API to integrands: Exposed __call__ for direct evaluation in user code, simplifying usage. (Commit 4156542757756d3955036aa6086b90863301b21e) - Cub QMC CLT and LDG development: Progress across scalar functions, sensitivity indices, lattice and vectorized implementations, and related updates. (Multiple commits: 6cebba208ef11bd7d03ffb7590727596f227a5e6; 394c8d92fc9669b0bf530e9ee2ea0a5a711f3b37; 38954bff08df0c16a6363a53684677ece9e8c313; 121eec2ba8331967933968215bedb3bf6a35fb09; 87f4162f3246003beb926e6fa532bf1bb7bdf6d1; 2b035ae5b3692f7b5417425109b68fc83aedac2c; 101dccea4b812c61434c888410f2cf466a1fe38a) - Vectorized integrand implementation: Reworked to a vectorized approach to boost performance and scalability. (Commit 74ca5096c6709a011d4440ea794096d814156bfc) - Doctests passing/QA stabilization: Fixed doctests to ensure reliable test outcomes and improve QA stability. (Commit e7ebcb1dca426ecda4315118bdabf2e7f4e11211) - Start CVS module initialization: Initiated cross-validation workflow/module initialization to support broader model validation. (Commit 19cbdf17f73fd79e124a7425d13e0e6614281f97) - Control variates complete: Implemented and completed the control variates variance reduction technique to improve estimation efficiency. (Commit e092459110f60468b411699b312c9a31da4475ca) Major bugs fixed: - Patch Bayesian logistic regression coefficients to fix numerical issues and improve stability. - AbstractStoppingCriterion rename: Refactor rename for clarity and consistency. - Doctests passing: Stabilized doctest behavior to improve QA reliability. Overall impact and accomplishments: - Substantial improvement in code quality, reliability, and test stability across the project, with a stronger foundation for statistical methods and QMC-based acceleration. - Significant performance enhancements through vectorization, multilevel groundwork, and lattice-based methods, enabling faster experiments and scalable analyses. - Expanded business value through a unified financial option modeling framework, DNB2 integration, and cross-validation tooling, broadening applicability and confidence in results. Technologies/skills demonstrated: - Python-based numerical methods, QMC, lattice methods, vectorization, and sensitivity analysis. - Software engineering practices including linting, doctest stabilization, and documentation discipline. - Multilevel and cross-validation concepts, performance-oriented refactoring, and UX improvements for end users.
April 2025
44 Commits • 12 Features
Apr 1, 2025April 2025 monthly summary for QMCSoftware/QMCSoftware: Strengthened testing, modernized numerical backends, and aligned CI/packaging to boost reliability, reproducibility, and ML readiness. Key deliverables included test infrastructure improvements, SciPy-based linear algebra migrations, CI branch normalization, packaging and doctest fixes, and groundwork for Torch/GPyTorch/BoTorch integration. Notable reliability work fixed eigenvector sign normalization, RNG stability, and BM consistency checks, contributing to more stable numerical results and faster feedback loops.
44 Commits • 12 Features
Apr 1, 2025April 2025 monthly summary for QMCSoftware/QMCSoftware: Strengthened testing, modernized numerical backends, and aligned CI/packaging to boost reliability, reproducibility, and ML readiness. Key deliverables included test infrastructure improvements, SciPy-based linear algebra migrations, CI branch normalization, packaging and doctest fixes, and groundwork for Torch/GPyTorch/BoTorch integration. Notable reliability work fixed eigenvector sign normalization, RNG stability, and BM consistency checks, contributing to more stable numerical results and faster feedback loops.
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 performance highlights for QMCSoftware/QMCSoftware focused on internal stability, code hygiene, and deployment readiness. Delivered targeted stability improvements and a repo-wide version bump to support reliable downstream usage and deployments.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 performance highlights for QMCSoftware/QMCSoftware focused on internal stability, code hygiene, and deployment readiness. Delivered targeted stability improvements and a repo-wide version bump to support reliable downstream usage and deployments.
February 2025
12 Commits • 4 Features
Feb 1, 2025February 2025 monthly summary for QMCSoftware/QMCSoftware Key features delivered - IIDStdUniform API modernization: Refactored initialization parameters, moving 'replications' to the end for a clearer, more consistent API. (Commit 817d0fb2d11eecaec207c908267720906342feae) - Offline/Local-first LDData loading: Prioritized local data access and refined LDData handling to support offline usage; prevents treating .npy files as LDData strings. (Commit d9d7c009ccaef7ec1a9d70ca7bd03c536665e7b0) - New lattice generating vector and default usage update: Introduced a new 9125-d lattice generating vector file and updated the default generating vector in the Lattice class; stopping criterion results adjusted. (Commit 37698f891e11ad6668b68e315338f9ef4c3a211d) - Release upgrades and kernel-method scaffolding: Version bumps across packages and setup, plus new kernel-method directories to enable upcoming features. (Commits f2703273adff806bda8e7bcb4fd71a917fcf8c4d; d7f4fe087c9ac698b1feac2ae7d7f6a01ad4c260; f7d0c0a66329653296db98bbf649754ec4e35be7) - Cleanup and stability improvements: Remove extraneous artifacts and enhance numerical robustness with hpc-stabilize changes. (Commits 29f0366521850af4691d403ae39f426eed85a976; 50d838c451f01ac08afe97cbe50cb101ab939870) Major bugs fixed - Restored unsigned integer size utilities and tests: Re-enabled get_unsigned_long_size and get_unsigned_long_long_size and the related tests to ensure correct handling in tests and the FWHT implementation. (Commits f32d263f9c27cd6e362560226ec907eb36782a44; 4f4522dbcb425b2d554a15a7dac151b2be8f7336) - Test suite accuracy updates for lattice and Gram matrix: Updated discrete distribution tests and Sobol index tolerances to reflect precision changes. (Commits 1eb9da06b2d52e2cebcc046ad776f3d4e3371920; 1810568444de28cd4fe372d52ee72e36ebb03292) - Cleanup extraneous artifacts: Removed unintended artifact files to maintain codebase hygiene. (Commit 29f0366521850af4691d403ae39f426eed85a976) - Stability improvements and minor bug fixes: Refactored internal computations and improved numerical error handling. (Commit 50d838c451f01ac08afe97cbe50cb101ab939870) Overall impact and accomplishments - Strengthened API clarity and offline data capabilities, enabling resilient workflows even with intermittent connectivity. - Improved correctness and reliability through restored utilities and updated tests, reducing risk in FWHT and lattice calculations. - Readiness for upcoming features via kernel-method scaffolding and packaging upgrades, accelerating future delivery. - Repo hygiene enhancements reduce maintenance overhead and support faster onboarding. Technologies/skills demonstrated - API design and refactoring, Python code quality, and maintainability. - Data handling and offline-first data access patterns. - Numerical robustness, test suite modernization, and CI readiness. - Software packaging, versioning, and project scaffolding for new modules.
12 Commits • 4 Features
Feb 1, 2025February 2025 monthly summary for QMCSoftware/QMCSoftware Key features delivered - IIDStdUniform API modernization: Refactored initialization parameters, moving 'replications' to the end for a clearer, more consistent API. (Commit 817d0fb2d11eecaec207c908267720906342feae) - Offline/Local-first LDData loading: Prioritized local data access and refined LDData handling to support offline usage; prevents treating .npy files as LDData strings. (Commit d9d7c009ccaef7ec1a9d70ca7bd03c536665e7b0) - New lattice generating vector and default usage update: Introduced a new 9125-d lattice generating vector file and updated the default generating vector in the Lattice class; stopping criterion results adjusted. (Commit 37698f891e11ad6668b68e315338f9ef4c3a211d) - Release upgrades and kernel-method scaffolding: Version bumps across packages and setup, plus new kernel-method directories to enable upcoming features. (Commits f2703273adff806bda8e7bcb4fd71a917fcf8c4d; d7f4fe087c9ac698b1feac2ae7d7f6a01ad4c260; f7d0c0a66329653296db98bbf649754ec4e35be7) - Cleanup and stability improvements: Remove extraneous artifacts and enhance numerical robustness with hpc-stabilize changes. (Commits 29f0366521850af4691d403ae39f426eed85a976; 50d838c451f01ac08afe97cbe50cb101ab939870) Major bugs fixed - Restored unsigned integer size utilities and tests: Re-enabled get_unsigned_long_size and get_unsigned_long_long_size and the related tests to ensure correct handling in tests and the FWHT implementation. (Commits f32d263f9c27cd6e362560226ec907eb36782a44; 4f4522dbcb425b2d554a15a7dac151b2be8f7336) - Test suite accuracy updates for lattice and Gram matrix: Updated discrete distribution tests and Sobol index tolerances to reflect precision changes. (Commits 1eb9da06b2d52e2cebcc046ad776f3d4e3371920; 1810568444de28cd4fe372d52ee72e36ebb03292) - Cleanup extraneous artifacts: Removed unintended artifact files to maintain codebase hygiene. (Commit 29f0366521850af4691d403ae39f426eed85a976) - Stability improvements and minor bug fixes: Refactored internal computations and improved numerical error handling. (Commit 50d838c451f01ac08afe97cbe50cb101ab939870) Overall impact and accomplishments - Strengthened API clarity and offline data capabilities, enabling resilient workflows even with intermittent connectivity. - Improved correctness and reliability through restored utilities and updated tests, reducing risk in FWHT and lattice calculations. - Readiness for upcoming features via kernel-method scaffolding and packaging upgrades, accelerating future delivery. - Repo hygiene enhancements reduce maintenance overhead and support faster onboarding. Technologies/skills demonstrated - API design and refactoring, Python code quality, and maintainability. - Data handling and offline-first data access patterns. - Numerical robustness, test suite modernization, and CI readiness. - Software packaging, versioning, and project scaffolding for new modules.
February 2025
January 2025
5 Commits • 2 Features
Jan 1, 2025Monthly summary for January 2025 for QMCSoftware/QMCSoftware focusing on delivering business value through feature enhancements, reliability improvements, and skill development. Highlights include a significant notebook-based ML demo upgrade, improved PDE-related demonstrations, and a more robust testing/infrastructure, setting the stage for scalable PDE kernel work and cross-platform reliability.
January 2025
5 Commits • 2 Features
Jan 1, 2025Monthly summary for January 2025 for QMCSoftware/QMCSoftware focusing on delivering business value through feature enhancements, reliability improvements, and skill development. Highlights include a significant notebook-based ML demo upgrade, improved PDE-related demonstrations, and a more robust testing/infrastructure, setting the stage for scalable PDE kernel work and cross-platform reliability.
December 2024
15 Commits • 5 Features
Dec 1, 2024December 2024 – QMCSoftware/QMCSoftware Overview: Delivered end-to-end PDE solving workflows and foundational ML-driven operator-learning components, achieving measurable improvements in robustness, performance, and usability. This month focused on notebooks, solver robustness, operator learning, and demo capabilities, with a solid foundation for production-grade simulations and visualization. Key deliverables: - Kernel PDE Notebooks Suite (1D/2D/3D) and demos: adds and refines kernel-based PDE solving notebooks (GaussKI, SIKI, fast SIKI) for 1D/2D, plus a 3D Allen-Cahn notebook; refactors for improved collocation, solver parameters, and visualization. Commits: a704ab9ee90bd3c51f26fa91355fa4eb383c9882; 60a9890e3a72c5c581cd33403ec384cce72e0f60; 2f6022df313b19341e0ad5d0ff9a8913a6803991; 3bc2cb3446efff455355e20e538164b4fdbb881a; cdaac0adcecff28bbbbe5361f8c404b9ce27aea7 - PDE Gram Matrix Solver Robustness and Performance: improves robustness and performance with a new preconditioner, enhanced input validation, and optimized data handling for cumulative sums and matrix operations. Commits: f1b20387e5758911e89a6f91abbdd2a398d0875e; ec7337c6fa5701e60a5b8a2bb9d42446a2b24d9d; d2fd25f69e6a6e61757e964c0da7dcd0b23ebbe8; e965c487242cc1269b6fe2cc46aea6fea2fdda2c - Operator Learning Kernel for PDEs: Introduces an operator-learning kernel (including MLP kernel) for elliptic PDEs, with training/inference improvements and visualization of solutions and derived quantities. Commits: 6a91e2e54e1a627c072e01e876f4d58ee74207aa; 712d64699f0aa2ad5ad1cb8fc0784103f3eb868c; ade1ed81a0ab2b9b8d98d2e1db932ea160ad8c25 - LM Scheme Development and Integration: Advances in the LM scheme, including refactor of difference term and matrix-vector multiply, plus notebook/environment prep for LM implementation. Commits: 76c1a032647ddf2afcfcd59a5f10c5d70b2d5234; 7db301309ab0e9d8072ad5d0644a0d98ffdf85e2 - Super-resolution Working Example: Adds a working example demonstrating super-resolution capabilities within the project workflow. Commit: 3ef6be713535ff9a060e4eac9a62612e706b187b Impact and value: These deliverables provide a more robust, scalable, and observable PDE solving framework, enabling faster iteration, more reliable simulations, and richer visualization for stakeholders. The updates establish production-grade foundations for onboarding newcomers and extending usage in research and development pipelines. Technologies/skills demonstrated: - Numerical linear algebra optimization (preconditioners, Cholesky variants, cumulative sums optimization) - Operator-learning models and training/inference pipelines - Python notebook development and environment preparation for advanced PDE workflows - Visualization and solution diagnostics for PDEs - Refactoring for readability, performance, and maintainability
15 Commits • 5 Features
Dec 1, 2024December 2024 – QMCSoftware/QMCSoftware Overview: Delivered end-to-end PDE solving workflows and foundational ML-driven operator-learning components, achieving measurable improvements in robustness, performance, and usability. This month focused on notebooks, solver robustness, operator learning, and demo capabilities, with a solid foundation for production-grade simulations and visualization. Key deliverables: - Kernel PDE Notebooks Suite (1D/2D/3D) and demos: adds and refines kernel-based PDE solving notebooks (GaussKI, SIKI, fast SIKI) for 1D/2D, plus a 3D Allen-Cahn notebook; refactors for improved collocation, solver parameters, and visualization. Commits: a704ab9ee90bd3c51f26fa91355fa4eb383c9882; 60a9890e3a72c5c581cd33403ec384cce72e0f60; 2f6022df313b19341e0ad5d0ff9a8913a6803991; 3bc2cb3446efff455355e20e538164b4fdbb881a; cdaac0adcecff28bbbbe5361f8c404b9ce27aea7 - PDE Gram Matrix Solver Robustness and Performance: improves robustness and performance with a new preconditioner, enhanced input validation, and optimized data handling for cumulative sums and matrix operations. Commits: f1b20387e5758911e89a6f91abbdd2a398d0875e; ec7337c6fa5701e60a5b8a2bb9d42446a2b24d9d; d2fd25f69e6a6e61757e964c0da7dcd0b23ebbe8; e965c487242cc1269b6fe2cc46aea6fea2fdda2c - Operator Learning Kernel for PDEs: Introduces an operator-learning kernel (including MLP kernel) for elliptic PDEs, with training/inference improvements and visualization of solutions and derived quantities. Commits: 6a91e2e54e1a627c072e01e876f4d58ee74207aa; 712d64699f0aa2ad5ad1cb8fc0784103f3eb868c; ade1ed81a0ab2b9b8d98d2e1db932ea160ad8c25 - LM Scheme Development and Integration: Advances in the LM scheme, including refactor of difference term and matrix-vector multiply, plus notebook/environment prep for LM implementation. Commits: 76c1a032647ddf2afcfcd59a5f10c5d70b2d5234; 7db301309ab0e9d8072ad5d0644a0d98ffdf85e2 - Super-resolution Working Example: Adds a working example demonstrating super-resolution capabilities within the project workflow. Commit: 3ef6be713535ff9a060e4eac9a62612e706b187b Impact and value: These deliverables provide a more robust, scalable, and observable PDE solving framework, enabling faster iteration, more reliable simulations, and richer visualization for stakeholders. The updates establish production-grade foundations for onboarding newcomers and extending usage in research and development pipelines. Technologies/skills demonstrated: - Numerical linear algebra optimization (preconditioners, Cholesky variants, cumulative sums optimization) - Operator-learning models and training/inference pipelines - Python notebook development and environment preparation for advanced PDE workflows - Visualization and solution diagnostics for PDEs - Refactoring for readability, performance, and maintainability
December 2024
November 2024
50 Commits • 19 Features
Nov 1, 2024November 2024 Monthly Summary for QMCSoftware/QMCSoftware: Delivered architectural refinements, performance optimizations, and stability fixes across the PDE/GramMatrix stack, enabling scalable batch processing and more reliable solver performance. Key outcomes include scalable PDE Gram matrix computations, improved kernel initialization for compute pipelines, modularized code aligned with qmcpy architecture, and robust fixes to CUDA/device issues, doctests, and PCG workflow. The work establishes a stronger foundation for future features (preconditioning, adaptive noise, and PyTorch integration) while driving tangible business value through faster, more reliable simulations and easier experimentation.
November 2024
50 Commits • 19 Features
Nov 1, 2024November 2024 Monthly Summary for QMCSoftware/QMCSoftware: Delivered architectural refinements, performance optimizations, and stability fixes across the PDE/GramMatrix stack, enabling scalable batch processing and more reliable solver performance. Key outcomes include scalable PDE Gram matrix computations, improved kernel initialization for compute pipelines, modularized code aligned with qmcpy architecture, and robust fixes to CUDA/device issues, doctests, and PCG workflow. The work establishes a stronger foundation for future features (preconditioning, adaptive noise, and PyTorch integration) while driving tangible business value through faster, more reliable simulations and easier experimentation.
October 2024
24 Commits • 6 Features
Oct 1, 2024October 2024 performance highlights: Delivered foundational kernel methods infrastructure with gram matrix capabilities, established robust parameter handling for betas and input vectors, implemented copy method and code quality utilities, refined Multiply functionality, and strengthened test coverage with noise cleanup. These workstreams collectively improve modeling flexibility, numerical correctness, and developer velocity, enabling faster, reliable kernel-based method iterations.
24 Commits • 6 Features
Oct 1, 2024October 2024 performance highlights: Delivered foundational kernel methods infrastructure with gram matrix capabilities, established robust parameter handling for betas and input vectors, implemented copy method and code quality utilities, refined Multiply functionality, and strengthened test coverage with noise cleanup. These workstreams collectively improve modeling flexibility, numerical correctness, and developer velocity, enabling faster, reliable kernel-based method iterations.
October 2024
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Quality Metrics
Correctness86.0%
Maintainability85.8%
Architecture82.8%
Performance77.0%
AI Usage21.4%
Skills & Technologies
Programming Languages
BibTeXCC++CythonEPSJSONJupyter NotebookMakefileMarkdownNumPy
Technical Skills
3D RenderingAPI DesignAPI DevelopmentAPI DocumentationAPI ImprovementAPI IntegrationAPI designAbstract ClassesAlgorithm ImplementationAlgorithm OptimizationAlgorithm RefactoringAlgorithm RefinementArray ManipulationAutomatic DifferentiationBayesian Inference
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