zama-ai/tfhe-rs
Oct 2024 – Jan 2025
4 Months active
Languages Used
C++RustShellYAMLPythonTOMLCUDAMakefile
Technical Skills
Build SystemsC++CI/CDCUDACryptographyGPU Computing
Agnes Leroy
PROFILE
Agnes Leroy
Agnes Leroy contributed to the zama-ai/tfhe-rs repository by engineering GPU-accelerated cryptographic features and optimizing backend infrastructure for secure computation. She developed asynchronous CUDA APIs and multi-GPU benchmarking workflows, standardizing parameters across CPU and GPU to ensure consistent performance and maintainability. Her work included refactoring memory management and data transfer layers using Rust and C++, enhancing reliability and throughput for homomorphic encryption operations. Agnes also improved CI/CD pipelines with targeted GPU testing and artifact management, reducing feedback time and increasing test coverage. Through detailed documentation and practical usage examples, she enabled faster adoption and safer releases for GPU-centric cryptographic workloads.
Overall Statistics
Feature vs Bugs
93%Features
Repository Contributions
48Total
Bugs
1
Commits
48
Features
13
Lines of code
24,246
Activity Months4
Your Network
69 people
Work History
January 2025
10 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for zama-ai/tfhe-rs focused on performance, reliability, and developer productivity: CUDA backend Async API and enhanced data transfer unlocks asynchronous GPU operations; standardized multi-bit parameters across CPU/GPU for consistent performance and easier maintenance; CI/workflow improvements improve green builds and faster feedback; updated benchmark docs enable accurate performance-driven decisions across teams.
10 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for zama-ai/tfhe-rs focused on performance, reliability, and developer productivity: CUDA backend Async API and enhanced data transfer unlocks asynchronous GPU operations; standardized multi-bit parameters across CPU/GPU for consistent performance and easier maintenance; CI/workflow improvements improve green builds and faster feedback; updated benchmark docs enable accurate performance-driven decisions across teams.
January 2025
December 2024
19 Commits • 4 Features
Dec 1, 2024December 2024 — TFHE-RS (zama-ai/tfhe-rs): Delivered substantial GPU-focused improvements and backend reliability enhancements. Implemented GPU Benchmarking Enhancements with multi-GPU support and new VM target configurations, optimizing throughput and enabling broader hardware coverage. Consolidated and optimized the GPU backend core (memory management, comparisons, carries, LUT usage) to improve performance and correctness across CUDA/Rust backends. Released GPU documentation and practical usage examples, including 2D array usage with GpuFheUint32Array and ClearArray, to accelerate user adoption. Strengthened CI/validation with unique artifact naming and randomized long-run tests for CPU and GPU, increasing test coverage and reducing release risk.
December 2024
19 Commits • 4 Features
Dec 1, 2024December 2024 — TFHE-RS (zama-ai/tfhe-rs): Delivered substantial GPU-focused improvements and backend reliability enhancements. Implemented GPU Benchmarking Enhancements with multi-GPU support and new VM target configurations, optimizing throughput and enabling broader hardware coverage. Consolidated and optimized the GPU backend core (memory management, comparisons, carries, LUT usage) to improve performance and correctness across CUDA/Rust backends. Released GPU documentation and practical usage examples, including 2D array usage with GpuFheUint32Array and ClearArray, to accelerate user adoption. Strengthened CI/validation with unique artifact naming and randomized long-run tests for CPU and GPU, increasing test coverage and reducing release risk.
November 2024
13 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for zama-ai/tfhe-rs focused on scaling GPU-accelerated cryptography, improving CI/CD reliability for GPUBenchmarks, and expanding testing coverage. Key outcomes include robust multi-GPU CUDA backend, new GPU-centric APIs, enhanced testing and benchmarking workflows, and increased test coverage for PBS noise distribution. Business value: unlocked scalable GPU-accelerated cryptographic operations across multiple GPUs, delivering higher throughput and correctness; reduced build/test feedback time via optimized CI; broader validation coverage lowering production risk in crypto workflows. Notable context: work aligns with demands for high-throughput, GPU-centric cryptographic workloads and robust CI for GPU pipelines, enabling faster experiments and safer production releases.
13 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for zama-ai/tfhe-rs focused on scaling GPU-accelerated cryptography, improving CI/CD reliability for GPUBenchmarks, and expanding testing coverage. Key outcomes include robust multi-GPU CUDA backend, new GPU-centric APIs, enhanced testing and benchmarking workflows, and increased test coverage for PBS noise distribution. Business value: unlocked scalable GPU-accelerated cryptographic operations across multiple GPUs, delivering higher throughput and correctness; reduced build/test feedback time via optimized CI; broader validation coverage lowering production risk in crypto workflows. Notable context: work aligns with demands for high-throughput, GPU-centric cryptographic workloads and robust CI for GPU pipelines, enabling faster experiments and safer production releases.
November 2024
October 2024
6 Commits • 2 Features
Oct 1, 2024October 2024 (2024-10) — GPU-focused delivery for tfhe-rs: refined GPU CI/test infrastructure, a critical memory alignment fix for GPU PBS, and backend/build enhancements with broader hardware support. These changes reduce CI waste, improve stability of GPU-accelerated PBS operations, and simplify cross-language builds, enabling faster secure computation on GPUs.
October 2024
6 Commits • 2 Features
Oct 1, 2024October 2024 (2024-10) — GPU-focused delivery for tfhe-rs: refined GPU CI/test infrastructure, a critical memory alignment fix for GPU PBS, and backend/build enhancements with broader hardware support. These changes reduce CI waste, improve stability of GPU-accelerated PBS operations, and simplify cross-language builds, enabling faster secure computation on GPUs.
Activity
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Quality Metrics
Correctness88.8%
Maintainability89.0%
Architecture86.2%
Performance81.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDACUDA C++MakefileMarkdownPythonRustShellTOMLYAML
Technical Skills
Asynchronous ProgrammingAutomationBackend DevelopmentBenchmarkingBuild SystemsC++CI/CDCUDACode RefactoringConfiguration ManagementCryptographyData StructuresDependency ManagementDevOpsDocumentation
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