pq-code-package/mldsa-native
Sep 2025 – Dec 2025
4 Months active
Languages Used
AssemblyCPython
Technical Skills
AArch64 architectureAVX2 IntrinsicsAssembly language programmingC ProgrammingCode refactoringCryptography
jammychiou1
PROFILE
Jammychiou1
Worked on the pq-code-package/mldsa-native repository, delivering ten features over four months focused on cryptographic performance and maintainability. Developed and optimized architecture-specific routines for polynomial operations using C, Assembly, and AVX2 intrinsics, targeting both AArch64 and x86_64 platforms. Refactored signing workflows for reliability, centralized error handling, and improved code readability. Enhanced cross-architecture portability by integrating native implementations with conditional routing, while updating documentation to clarify mathematical reasoning and error bounds. Emphasized correctness through expanded testing and validation of Montgomery reduction bounds, and improved maintainability by unifying API documentation and aligning code conventions across multiple architectures.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
27Total
Bugs
0
Commits
27
Features
10
Lines of code
4,569
Activity Months4
Work History
December 2025
2 Commits • 1 Features
Dec 1, 2025Month: 2025-12 — Consolidated Barrett division documentation updates for polynomial decomposition in pq-code-package/mldsa-native. This work focused on clarifying the general algorithm, precision and error bounds, mathematical reasoning, and rounding processes, with explicit cross-architecture notes for NEON and AVX2 implementations. The changes improve user understanding, trust in the method, and maintainability of the numerical core.
2 Commits • 1 Features
Dec 1, 2025Month: 2025-12 — Consolidated Barrett division documentation updates for polynomial decomposition in pq-code-package/mldsa-native. This work focused on clarifying the general algorithm, precision and error bounds, mathematical reasoning, and rounding processes, with explicit cross-architecture notes for NEON and AVX2 implementations. The changes improve user understanding, trust in the method, and maintainability of the numerical core.
December 2025
November 2025
15 Commits • 3 Features
Nov 1, 20252025-11 monthly work summary for pq-code-package/mldsa-native: Delivered performance- and correctness-oriented updates across AVX2 and AArch64 decomposition paths, along with Montgomery reduction bound validation. These efforts deliver faster cryptographic polynomial operations, stronger safety guarantees, and clearer cross-architecture maintainability.
November 2025
15 Commits • 3 Features
Nov 1, 20252025-11 monthly work summary for pq-code-package/mldsa-native: Delivered performance- and correctness-oriented updates across AVX2 and AArch64 decomposition paths, along with Montgomery reduction bound validation. These efforts deliver faster cryptographic polynomial operations, stronger safety guarantees, and clearer cross-architecture maintainability.
October 2025
4 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for pq-code-package/mldsa-native: Focused on performance-oriented native optimizations and broader platform support for cryptographic primitives. Delivered architecture-aware native implementations on x86_64 and ARM64, enabling higher throughput while preserving portability through conditional usage. Key actions and outcomes: - AVX2 native polyz_unpack for GAMMA1 = 2^17 and 2^19: introduced native AVX2 implementations with new AVX2 C files and header/main updates to conditionally select the native path when available. Commit: f2df58e1aa4defdfd11b3f6b12d0addcc1ed0f20. - AArch64 native optimizations for cryptographic primitives: added native assembly implementations for polyz_unpack, poly_pointwise_montgomery, and polyvecl_pointwise_acc_montgomery, supported by new assembly files and header updates to expose and integrate native code on ARM64. Commits: 40fd58c1f08cc1bf2ecd363797cf22ad9d037807; 73833d62db675d82f04e4b0609be5c072756f932; a61b5e046e3458397a09ce2209bebaebcb9cef5f. - Integration and portability improvements: header/main changes to conditionally route to native implementations, ensuring seamless fallbacks and easier maintenance across architectures. Overall impact and business value: - Significant improvements in cryptographic throughput and latency for critical routines on both x86_64 and ARM64 platforms, supporting higher-performance production workloads. - Strengthened cross-architecture portability and maintainability by consolidating native code paths with clear integration points. - Demonstrated proficient use of AVX2 intrinsics and AArch64 assembly for cryptography, reflecting advanced performance engineering skills.
4 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for pq-code-package/mldsa-native: Focused on performance-oriented native optimizations and broader platform support for cryptographic primitives. Delivered architecture-aware native implementations on x86_64 and ARM64, enabling higher throughput while preserving portability through conditional usage. Key actions and outcomes: - AVX2 native polyz_unpack for GAMMA1 = 2^17 and 2^19: introduced native AVX2 implementations with new AVX2 C files and header/main updates to conditionally select the native path when available. Commit: f2df58e1aa4defdfd11b3f6b12d0addcc1ed0f20. - AArch64 native optimizations for cryptographic primitives: added native assembly implementations for polyz_unpack, poly_pointwise_montgomery, and polyvecl_pointwise_acc_montgomery, supported by new assembly files and header updates to expose and integrate native code on ARM64. Commits: 40fd58c1f08cc1bf2ecd363797cf22ad9d037807; 73833d62db675d82f04e4b0609be5c072756f932; a61b5e046e3458397a09ce2209bebaebcb9cef5f. - Integration and portability improvements: header/main changes to conditionally route to native implementations, ensuring seamless fallbacks and easier maintenance across architectures. Overall impact and business value: - Significant improvements in cryptographic throughput and latency for critical routines on both x86_64 and ARM64 platforms, supporting higher-performance production workloads. - Strengthened cross-architecture portability and maintainability by consolidating native code paths with clear integration points. - Demonstrated proficient use of AVX2 intrinsics and AArch64 assembly for cryptography, reflecting advanced performance engineering skills.
October 2025
September 2025
6 Commits • 4 Features
Sep 1, 2025September 2025 — Delivered reliability improvements and performance optimizations in the mldsa-native project, focusing on robustness of the signing workflow and architecture-specific speedups for polynomial operations. Key outcomes include centralized cleanup in the signing path, readability improvements in the signing module, and native implementations for poly_use_hint and poly_chknorm on AArch64 and AVX2, contributing to faster execution, reduced error risk, and easier long-term maintenance. These changes preserve existing functionality while reducing technical debt and enabling higher throughput in cryptographic operations.
September 2025
6 Commits • 4 Features
Sep 1, 2025September 2025 — Delivered reliability improvements and performance optimizations in the mldsa-native project, focusing on robustness of the signing workflow and architecture-specific speedups for polynomial operations. Key outcomes include centralized cleanup in the signing path, readability improvements in the signing module, and native implementations for poly_use_hint and poly_chknorm on AArch64 and AVX2, contributing to faster execution, reduced error risk, and easier long-term maintenance. These changes preserve existing functionality while reducing technical debt and enabling higher throughput in cryptographic operations.
Activity
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Quality Metrics
Correctness97.0%
Maintainability87.8%
Architecture89.0%
Performance93.4%
AI Usage22.2%
Skills & Technologies
Programming Languages
AssemblyCPython
Technical Skills
AArch64 architectureAPI developmentAPI documentationARM AssemblyAVX2 IntrinsicsAVX2 optimizationAVX2 programmingAssembly language programmingAssembly programmingC ProgrammingC programmingCode refactoringCryptographyEmbedded SystemsLow-level Optimization
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