zama-ai/tfhe-rs
May 2025 – Apr 2026
10 Months active
Languages Used
C++RustCUDA
Technical Skills
Backend DevelopmentC++CUDAGPU ComputingHomomorphic EncryptionRefactoring
Enzo Di Maria
PROFILE
Enzo Di Maria
Over ten months, contributed to the zama-ai/tfhe-rs repository by engineering GPU-accelerated cryptographic primitives and optimizing backend infrastructure for homomorphic encryption workloads. Focused on consolidating CUDA and Rust backend logic, refactoring scalar arithmetic and vector operations, and implementing GPU-accelerated AES, Trivium, and Kreyvium ciphers. Enhanced performance and maintainability through memory management improvements, benchmarking integration, and multi-GPU support. Addressed reliability by fixing critical bugs in GPU compute paths and cryptographic routines, including AES S-box correctness. Leveraged C++, CUDA, and Rust to deliver scalable, production-ready features that reduced latency, improved throughput, and established a robust foundation for future GPU optimizations.
Overall Statistics
Feature vs Bugs
90%Features
Repository Contributions
40Total
Bugs
2
Commits
40
Features
18
Lines of code
38,440
Activity Months10
Your Network
80 peopleWork History
April 2026
1 Commits
Apr 1, 2026April 2026 monthly summary for zama-ai/tfhe-rs focusing on reliability and cryptographic correctness in GPU-accelerated operations. The month centered on stabilizing the AES vectorized S-box path to reduce noise and enforcement of correct flush behavior, ensuring encryption accuracy and robustness in production use.
1 Commits
Apr 1, 2026April 2026 monthly summary for zama-ai/tfhe-rs focusing on reliability and cryptographic correctness in GPU-accelerated operations. The month centered on stabilizing the AES vectorized S-box path to reduce noise and enforcement of correct flush behavior, ensuring encryption accuracy and robustness in production use.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for zama-ai/tfhe-rs focusing on GPU-accelerated cryptographic operations and reliability improvements. Key features delivered include GPU-accelerated Kreyvium/Trivium cipher optimizations with noise reduction and persistent state management across GPU operations, accompanied by updated initialization and step functions to enhance performance and reliability. A dedicated benchmarking pass was added to reflect the changes for accurate performance metrics.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for zama-ai/tfhe-rs focusing on GPU-accelerated cryptographic operations and reliability improvements. Key features delivered include GPU-accelerated Kreyvium/Trivium cipher optimizations with noise reduction and persistent state management across GPU operations, accompanied by updated initialization and step functions to enhance performance and reliability. A dedicated benchmarking pass was added to reflect the changes for accurate performance metrics.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — zama-ai/tfhe-rs: Focused on GPU compute efficiency. Delivered a targeted feature by reducing LUT usage in vector_find GPU paths from 2 to 1, lowering memory footprint and enabling further performance optimizations. Implemented a related bug fix to align LUT accounting. These changes establish a simpler, more scalable GPU compute path and provide measurable resource savings for large-scale deployments.
1 Commits • 1 Features
Feb 1, 2026February 2026 — zama-ai/tfhe-rs: Focused on GPU compute efficiency. Delivered a targeted feature by reducing LUT usage in vector_find GPU paths from 2 to 1, lowering memory footprint and enabling further performance optimizations. Implemented a related bug fix to align LUT accounting. These changes establish a simpler, more scalable GPU compute path and provide measurable resource savings for large-scale deployments.
February 2026
December 2025
6 Commits • 3 Features
Dec 1, 2025December 2025 monthly summary for zama-ai/tfhe-rs focused on GPU-accelerated crypto primitives and performance-oriented refactors. Delivered high-impact GPU backend improvements, CUDA-accelerated Trivium and Kreyvium implementations, and compatibility fixes to support enterprise GPU clusters while establishing benchmarking capabilities to quantify gains.
December 2025
6 Commits • 3 Features
Dec 1, 2025December 2025 monthly summary for zama-ai/tfhe-rs focused on GPU-accelerated crypto primitives and performance-oriented refactors. Delivered high-impact GPU backend improvements, CUDA-accelerated Trivium and Kreyvium implementations, and compatibility fixes to support enterprise GPU clusters while establishing benchmarking capabilities to quantify gains.
November 2025
10 Commits • 2 Features
Nov 1, 2025November 2025 monthly work summary for zama-ai/tfhe-rs focused on GPU backend stabilization and cryptographic capability enhancements. Delivered core GPU backend enhancements with performance and reliability improvements, migrated key vector operations to the backend, and introduced a scalable CUDA streams infrastructure. Added AES-256 encryption in CTR mode on the GPU with benchmarking safeguards. Implemented targeted bug fixes to kernel block sizing and match_value/outputs handling, improving correctness and resilience under memory pressure. These changes position TFHE-rs for higher throughput in GPU-accelerated homomorphic encryption workloads while reducing bench-time failures.
10 Commits • 2 Features
Nov 1, 2025November 2025 monthly work summary for zama-ai/tfhe-rs focused on GPU backend stabilization and cryptographic capability enhancements. Delivered core GPU backend enhancements with performance and reliability improvements, migrated key vector operations to the backend, and introduced a scalable CUDA streams infrastructure. Added AES-256 encryption in CTR mode on the GPU with benchmarking safeguards. Implemented targeted bug fixes to kernel block sizing and match_value/outputs handling, improving correctness and resilience under memory pressure. These changes position TFHE-rs for higher throughput in GPU-accelerated homomorphic encryption workloads while reducing bench-time failures.
November 2025
October 2025
5 Commits • 3 Features
Oct 1, 2025October 2025: Delivered GPU-accelerated OPRF capabilities and testing improvements in the zama-ai/tfhe-rs repository. Key refactors streamlined GPU code paths, introduced custom-range OPRF on GPU, added multibit PBS decompression support, and expanded cross-CPU/GPU OPRF test coverage. These changes increase potential performance, reliability, and maintainability, and lay a solid foundation for future GPU optimizations.
October 2025
5 Commits • 3 Features
Oct 1, 2025October 2025: Delivered GPU-accelerated OPRF capabilities and testing improvements in the zama-ai/tfhe-rs repository. Key refactors streamlined GPU code paths, introduced custom-range OPRF on GPU, added multibit PBS decompression support, and expanded cross-CPU/GPU OPRF test coverage. These changes increase potential performance, reliability, and maintainability, and lay a solid foundation for future GPU optimizations.
August 2025
2 Commits • 2 Features
Aug 1, 2025August 2025 highlights for zama-ai/tfhe-rs: two GPU-focused features that boost performance and cryptographic capabilities, supported by a critical GPU backend bug fix. These changes deliver tangible business value through lower latency and higher throughput for encrypted analytics and secure computation on GPU-backed workloads.
2 Commits • 2 Features
Aug 1, 2025August 2025 highlights for zama-ai/tfhe-rs: two GPU-focused features that boost performance and cryptographic capabilities, supported by a critical GPU backend bug fix. These changes deliver tangible business value through lower latency and higher throughput for encrypted analytics and secure computation on GPU-backed workloads.
August 2025
July 2025
6 Commits • 4 Features
Jul 1, 2025July 2025 (zama-ai/tfhe-rs): Delivered targeted GPU backend improvements focused on performance, scalability, and maintainability. Key features include: 1) Scalar Division and FFI scaffolding for faster GPU math with CudaScalarDivisorFFI; 2) OPRF optimizations enabling grouped processing and multi-GPU execution; 3) Integer operations and compression enhancements with new bit-count/log2 helpers and CUDA LWE/GLWE FFI structures; 4) Buffer allocation cleanup and API simplification to improve code maintainability. These changes collectively increase throughput for cryptographic workloads, reduce latency in multi-GPU configurations, and establish a cleaner foundation for future optimization.
July 2025
6 Commits • 4 Features
Jul 1, 2025July 2025 (zama-ai/tfhe-rs): Delivered targeted GPU backend improvements focused on performance, scalability, and maintainability. Key features include: 1) Scalar Division and FFI scaffolding for faster GPU math with CudaScalarDivisorFFI; 2) OPRF optimizations enabling grouped processing and multi-GPU execution; 3) Integer operations and compression enhancements with new bit-count/log2 helpers and CUDA LWE/GLWE FFI structures; 4) Buffer allocation cleanup and API simplification to improve code maintainability. These changes collectively increase throughput for cryptographic workloads, reduce latency in multi-GPU configurations, and establish a cleaner foundation for future optimization.
June 2025
6 Commits • 1 Features
Jun 1, 2025June 2025 performance highlights for zama-ai/tfhe-rs focused on GPU backend consolidation and codebase clean-up to pave scalable GPU performance. Delivered migration of scalar arithmetic operations and division from the GPU path into a unified backend, consolidating six operations: scalar_mul_high_async, unchecked_scalar_div_async, get_scalar_div_size_on_gpu, sub_assign_async, signed_scalar_div_async, and extend_radix_with_sign_msb_async. The migration involved updating backend interfaces, aligning tests, and ensuring stable GPU test results. Impact: Improved code organization, reduced duplication, and a cleaner, more maintainable foundation for GPU optimization work. This sets the stage for targeted performance tuning of scalar arithmetic on the GPU and smoother onboarding of future backend-driven enhancements. Business value: Higher maintainability and extensibility reduce time-to-delivery for GPU-related features, improve test reliability, and enable more aggressive performance improvements in future sprints. Technologies/skills demonstrated: Rust-based GPU/backend refactoring, modular backend design, cross-cutting testing and test fixes for GPU paths, and system-wide impact analysis for performance-oriented changes.
6 Commits • 1 Features
Jun 1, 2025June 2025 performance highlights for zama-ai/tfhe-rs focused on GPU backend consolidation and codebase clean-up to pave scalable GPU performance. Delivered migration of scalar arithmetic operations and division from the GPU path into a unified backend, consolidating six operations: scalar_mul_high_async, unchecked_scalar_div_async, get_scalar_div_size_on_gpu, sub_assign_async, signed_scalar_div_async, and extend_radix_with_sign_msb_async. The migration involved updating backend interfaces, aligning tests, and ensuring stable GPU test results. Impact: Improved code organization, reduced duplication, and a cleaner, more maintainable foundation for GPU optimization work. This sets the stage for targeted performance tuning of scalar arithmetic on the GPU and smoother onboarding of future backend-driven enhancements. Business value: Higher maintainability and extensibility reduce time-to-delivery for GPU-related features, improve test reliability, and enable more aggressive performance improvements in future sprints. Technologies/skills demonstrated: Rust-based GPU/backend refactoring, modular backend design, cross-cutting testing and test fixes for GPU paths, and system-wide impact analysis for performance-oriented changes.
June 2025
May 2025
2 Commits • 1 Features
May 1, 2025Month: 2025-05 – Delivered backend-focused CUDA radix operation consolidation in tfhe-rs, improving maintainability and paving the way for GPU path performance optimizations. Centralized CUDA-specific logic by moving extend_radix_with_trivial_zero_blocks_msb and trim_radix_blocks_lsb_async into backend-specific CUDA/Rust bindings and host support, updated the CudaRadixCiphertextInfo struct for backend awareness, and added new utilities (trim_radix_blocks_lsb_64 and host_trim_radix_blocks_lsb) to support extended CUDA paths. No major bugs fixed this month; testing and refinement ongoing. This work enhances GPU path consistency, reduces cross-language divergence, and supports targeted performance improvements in the CUDA backend.
May 2025
2 Commits • 1 Features
May 1, 2025Month: 2025-05 – Delivered backend-focused CUDA radix operation consolidation in tfhe-rs, improving maintainability and paving the way for GPU path performance optimizations. Centralized CUDA-specific logic by moving extend_radix_with_trivial_zero_blocks_msb and trim_radix_blocks_lsb_async into backend-specific CUDA/Rust bindings and host support, updated the CudaRadixCiphertextInfo struct for backend awareness, and added new utilities (trim_radix_blocks_lsb_64 and host_trim_radix_blocks_lsb) to support extended CUDA paths. No major bugs fixed this month; testing and refinement ongoing. This work enhances GPU path consistency, reduces cross-language divergence, and supports targeted performance improvements in the CUDA backend.
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Quality Metrics
Correctness91.8%
Maintainability87.6%
Architecture91.8%
Performance86.6%
AI Usage22.6%
Skills & Technologies
Programming Languages
C++CUDARust
Technical Skills
AES CryptographyAPI DesignAlgorithm optimizationBackend DevelopmentBackend developmentBenchmarkingC++C++ ProgrammingC++ developmentCUDACode CleanupCode RefactoringCryptographyData StructuresFFI (Foreign Function Interface)
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