
Worked on the openssl/openssl repository to optimize ML-DSA cryptographic workloads for s390x enterprise hardware. Developed a vectorized implementation of the number theoretic transform (NTT) using C, leveraging 128-bit vector registers and the vec_mulh instruction to accelerate both forward and inverse NTT paths. This approach resulted in a throughput increase of approximately two to three times on z17-class systems. The work demonstrated expertise in low-level programming, cryptography, and performance optimization, and involved close collaboration with project maintainers to ensure correctness and maintainability. The feature was reviewed, approved, and merged, aligning with OpenSSL’s performance roadmap.
OpenSSL performance optimization for ML-DSA on s390x: Delivered a vectorized (inverse) NTT path that leverages 128-bit vector registers via vec_mulh, enabling substantial throughput gains on s390x hardware. The feature, committed as 24486294f7e8ec69318c3608edb191d7377735f1 and merged into PR 30812, increased ML-DSA performance by roughly 2x to 3.4x on z17-class systems. This work was reviewed and approved by key maintainers (Shane Lontis, Saša Nedvědický) and represents a notable step in OpenSSL's performance roadmap for cryptographic workloads on enterprise architectures. The effort demonstrates deep proficiency in vectorization, cross-architecture optimization, and collaboration in open-source development.
OpenSSL performance optimization for ML-DSA on s390x: Delivered a vectorized (inverse) NTT path that leverages 128-bit vector registers via vec_mulh, enabling substantial throughput gains on s390x hardware. The feature, committed as 24486294f7e8ec69318c3608edb191d7377735f1 and merged into PR 30812, increased ML-DSA performance by roughly 2x to 3.4x on z17-class systems. This work was reviewed and approved by key maintainers (Shane Lontis, Saša Nedvědický) and represents a notable step in OpenSSL's performance roadmap for cryptographic workloads on enterprise architectures. The effort demonstrates deep proficiency in vectorization, cross-architecture optimization, and collaboration in open-source development.

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