pq-code-package/mldsa-native
Sep 2025 – Oct 2025
2 Months active
Languages Used
AssemblyC
Technical Skills
AArch64 architectureAVX2 IntrinsicsAssembly language programmingC ProgrammingCode refactoringCryptography
jammychiou1
PROFILE
Jammychiou1
Jammy Chiou contributed to the pq-code-package/mldsa-native repository by engineering architecture-specific performance optimizations and reliability improvements for cryptographic routines. Over two months, Jammy developed native implementations in C and assembly for AArch64 and AVX2, targeting polynomial operations such as poly_use_hint, poly_chknorm, and polyz_unpack. These enhancements improved throughput and reduced latency on both x86_64 and ARM64 platforms while maintaining portability through conditional integration. Jammy also refactored the signing workflow to centralize cleanup and error handling, increasing maintainability and reducing technical debt. The work demonstrated deep expertise in low-level programming, cryptography, and performance optimization for embedded systems.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
6
Lines of code
1,787
Activity Months2
Work History
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
Correctness98.0%
Maintainability85.0%
Architecture88.0%
Performance98.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
AssemblyC
Technical Skills
AArch64 architectureARM AssemblyAVX2 IntrinsicsAssembly language programmingC ProgrammingCode refactoringCryptographyEmbedded SystemsLow-level OptimizationLow-level ProgrammingLow-level programmingPerformance OptimizationPerformance optimization
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