zama-ai/tfhe-rs
May 2025 – Apr 2026
9 Months active
Languages Used
C++RustCUDA
Technical Skills
Backend DevelopmentC++CUDAGPU ComputingHomomorphic EncryptionRefactoring
Enzo Di Maria
PROFILE
Enzo Di Maria
Enzo Di Maria contributed to the zama-ai/tfhe-rs repository by engineering GPU-accelerated cryptographic primitives and backend infrastructure for homomorphic encryption workloads. Over nine months, he consolidated and refactored CUDA and Rust code paths, optimizing scalar arithmetic, memory management, and parallel processing for improved maintainability and performance. Enzo implemented GPU-accelerated AES, Trivium, and Kreyvium encryption, introduced multi-GPU support, and enhanced OPRF and integer operations. His work addressed reliability through targeted bug fixes and benchmarking, while reducing memory footprint and streamlining backend logic. Using C++, CUDA, and Rust, Enzo delivered robust, scalable solutions that improved throughput and reliability for secure computation.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
39Total
Bugs
2
Commits
39
Features
17
Lines of code
35,750
Activity Months9
Your Network
76 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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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Quality Metrics
Correctness92.0%
Maintainability87.6%
Architecture92.0%
Performance86.6%
AI Usage22.0%
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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