
Over nine months, Jargh contributed to awslabs/s2n-bignum by engineering cross-platform cryptographic primitives and formal verification infrastructure. He expanded SIMD and AVX2 support for ARM and x86, optimized ML-KEM and SHA3 routines in C and assembly, and automated C89 header generation to improve portability. His work included refactoring Keccak hashing, enhancing proof automation, and modernizing build systems for BSD/Linux compatibility. Jargh applied formal verification using OCaml and HOL Light, ensuring correctness and maintainability. By integrating low-level optimizations, modular API design, and robust documentation, he delivered scalable, reliable cryptographic components and streamlined verification workflows across diverse hardware architectures.

September 2025 monthly summary: Delivered cross-platform portability enhancements for awslabs/s2n-bignum and automated generation of the S2N Bignum C89 header, improving BSD/Linux parity and reducing manual maintenance. Major fixes addressed BSD portability issues, enhancing CI reliability and build stability. This work demonstrates strong low‑level portability, build automation, and adherence to C89 standards, delivering business value by broadening deployment readiness and accelerating integration efforts.
September 2025 monthly summary: Delivered cross-platform portability enhancements for awslabs/s2n-bignum and automated generation of the S2N Bignum C89 header, improving BSD/Linux parity and reducing manual maintenance. Major fixes addressed BSD portability issues, enhancing CI reliability and build stability. This work demonstrates strong low‑level portability, build automation, and adherence to C89 standards, delivering business value by broadening deployment readiness and accelerating integration efforts.
August 2025 performance for awslabs/s2n-bignum: Delivered two major features strengthening formal verification and alignment accuracy, reduced workaround code, and improved proof performance, enabling scalable validation of larger operands. The work enhances reliability of the verification model and sets the stage for future architecture support across broader operand lengths.
August 2025 performance for awslabs/s2n-bignum: Delivered two major features strengthening formal verification and alignment accuracy, reduced workaround code, and improved proof performance, enabling scalable validation of larger operands. The work enhances reliability of the verification model and sets the stage for future architecture support across broader operand lengths.
June 2025 monthly summary for awslabs/s2n-bignum: Delivered architectural refactors and verification-focused improvements to ML-KEM, refined formal specification style for verification readability, and updated ARM-specific documentation; stabilized CI/test environment with HOL Light alignment and resolved a memory leak in test suite, improving reliability and maintainability across the bignum verification workflow.
June 2025 monthly summary for awslabs/s2n-bignum: Delivered architectural refactors and verification-focused improvements to ML-KEM, refined formal specification style for verification readability, and updated ARM-specific documentation; stabilized CI/test environment with HOL Light alignment and resolved a memory leak in test suite, improving reliability and maintainability across the bignum verification workflow.
May 2025 monthly summary for awslabs/s2n-bignum: Delivered ML-KEM core enhancements with ARM model integration and licensing modernization. Focused on performance readiness, portability, and maintainability with extensive code improvements and refactors. No critical bugs reported this month; groundwork laid for ARM deployments and broader collaboration.
May 2025 monthly summary for awslabs/s2n-bignum: Delivered ML-KEM core enhancements with ARM model integration and licensing modernization. Focused on performance readiness, portability, and maintainability with extensive code improvements and refactors. No critical bugs reported this month; groundwork laid for ARM deployments and broader collaboration.
March 2025 performance and verification-focused month across two repositories. Key feature deliveries include: (1) ML-KEM Keccak 4-way batch and ARM assembly optimizations enabling higher throughput on cryptographic operations; (2) ML-KEM polynomial reduction (mlkem_poly_reduce) for canonical modular reduction with optimized assembly; (3) ML-KEM proofs and verification enhancements, generalizing automation for congruence-and-bound checks, adding signs handling and Montgomery reduction, and including correctness statements; (4) Polynomial modular multiplication proof automation improvements for aarch64, refactoring to integer specs and supporting automation. No major bugs fixed; emphasis on feature delivery, correctness, and automation. Impact: accelerated cryptographic operations, stronger verification coverage, and broader architecture portability, enabling faster secure key exchange workflows and more reliable proofs. Technologies/skills demonstrated include Keccak-f1600, 4-way batch processing, ARM assembly optimization, Montgomery reduction, integer congruences, and formal verification tooling.
March 2025 performance and verification-focused month across two repositories. Key feature deliveries include: (1) ML-KEM Keccak 4-way batch and ARM assembly optimizations enabling higher throughput on cryptographic operations; (2) ML-KEM polynomial reduction (mlkem_poly_reduce) for canonical modular reduction with optimized assembly; (3) ML-KEM proofs and verification enhancements, generalizing automation for congruence-and-bound checks, adding signs handling and Montgomery reduction, and including correctness statements; (4) Polynomial modular multiplication proof automation improvements for aarch64, refactoring to integer specs and supporting automation. No major bugs fixed; emphasis on feature delivery, correctness, and automation. Impact: accelerated cryptographic operations, stronger verification coverage, and broader architecture portability, enabling faster secure key exchange workflows and more reliable proofs. Technologies/skills demonstrated include Keccak-f1600, 4-way batch processing, ARM assembly optimization, Montgomery reduction, integer congruences, and formal verification tooling.
February 2025 monthly summary for awslabs/s2n-bignum focused on performance optimization, cross-platform ARM improvements, and proof-assisted crypto primitives. Key work centered on optimizing critical paths, extending the ARM model, and improving build compatibility across architectures while maintaining formal verification efforts.
February 2025 monthly summary for awslabs/s2n-bignum focused on performance optimization, cross-platform ARM improvements, and proof-assisted crypto primitives. Key work centered on optimizing critical paths, extending the ARM model, and improving build compatibility across architectures while maintaining formal verification efforts.
Month: 2025-01 Key features delivered: - ML-KEM NTT constants table argument support: Updated forward/inverse NTT APIs to accept and pass table arguments (derived from zeta values), aligning with mlkem-native and improving modularity and maintainability of cryptographic operations. Commit: acd969b4ac35ffc4e43f1082ef27c02b5f70286a. Major bugs fixed: - None reported for awslabs/s2n-bignum this month. Overall impact and accomplishments: - Reduced coupling between NTT logic and constants, enabling easier maintenance, testing, and future refinements. Establishes a cleaner, more auditable cryptographic surface and lowers integration risk for dependent projects. Technologies/skills demonstrated: - Cryptographic primitives (ML-KEM, NTT), zeta-derived constants, API design, C/C++, modular architecture, version control hygiene.
Month: 2025-01 Key features delivered: - ML-KEM NTT constants table argument support: Updated forward/inverse NTT APIs to accept and pass table arguments (derived from zeta values), aligning with mlkem-native and improving modularity and maintainability of cryptographic operations. Commit: acd969b4ac35ffc4e43f1082ef27c02b5f70286a. Major bugs fixed: - None reported for awslabs/s2n-bignum this month. Overall impact and accomplishments: - Reduced coupling between NTT logic and constants, enabling easier maintenance, testing, and future refinements. Establishes a cleaner, more auditable cryptographic surface and lowers integration risk for dependent projects. Technologies/skills demonstrated: - Cryptographic primitives (ML-KEM, NTT), zeta-derived constants, API design, C/C++, modular architecture, version control hygiene.
December 2024 monthly summary for awslabs/s2n-bignum: Delivered key features expanding hardware support, improved cryptographic performance, and added assembly-optimized routines for SHA3. No major bugs fixed this month; focus remained on performance, portability, and test coverage.
December 2024 monthly summary for awslabs/s2n-bignum: Delivered key features expanding hardware support, improved cryptographic performance, and added assembly-optimized routines for SHA3. No major bugs fixed this month; focus remained on performance, portability, and test coverage.
November 2024 monthly summary for awslabs/s2n-bignum focused on expanding cross-ISA SIMD capabilities to accelerate large-number workloads and broaden platform support. Implemented ARM SIMD extension and width support, introducing broader data-size handling, 128-bit SIMD memory ops, element-wise SIMD multiply, and new instructions (SQDMULH, SQRDMULH, MLS, SRSHR, SSHR). Also enabled x86 AVX2 SIMD support with decoding/execution for VPXOR across 256-bit and 512-bit vectors, plus updated SIMD ABI and verification utilities. No standalone bug fixes were tracked this month; the work centered on capability enhancements, decoder robustness, and performance readiness to drive higher-throughput crypto/bignum workloads across ARM and x86 platforms.
November 2024 monthly summary for awslabs/s2n-bignum focused on expanding cross-ISA SIMD capabilities to accelerate large-number workloads and broaden platform support. Implemented ARM SIMD extension and width support, introducing broader data-size handling, 128-bit SIMD memory ops, element-wise SIMD multiply, and new instructions (SQDMULH, SQRDMULH, MLS, SRSHR, SSHR). Also enabled x86 AVX2 SIMD support with decoding/execution for VPXOR across 256-bit and 512-bit vectors, plus updated SIMD ABI and verification utilities. No standalone bug fixes were tracked this month; the work centered on capability enhancements, decoder robustness, and performance readiness to drive higher-throughput crypto/bignum workloads across ARM and x86 platforms.
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