zama-ai/tfhe-rs
Nov 2024 – Oct 2025
12 Months active
Languages Used
C++CUDARustMakefileCudaMarkdownC
Technical Skills
C++CUDACryptographyGPU ComputingGPU ProgrammingHomomorphic Encryption
Pedro Alves
PROFILE
Pedro Alves
Pedro Alves engineered GPU-accelerated cryptographic features for the zama-ai/tfhe-rs repository, focusing on scalable homomorphic encryption and zero-knowledge proof workflows. He designed and optimized CUDA kernels and C++ backend components to enable high-throughput programmable bootstrapping, multi-GPU execution, and 128-bit ciphertext operations. By integrating Rust bindings and refining memory management, Pedro improved reliability and performance for large-scale encrypted computations. His work included benchmarking infrastructure, API enhancements, and robust testing to ensure correctness across diverse hardware. Through deep expertise in CUDA, C++, and Rust, Pedro delivered maintainable, production-ready solutions that advanced the project’s cryptographic capabilities and supported complex, real-world workloads.
Overall Statistics
Feature vs Bugs
59%Features
Repository Contributions
58Total
Bugs
13
Commits
58
Features
19
Lines of code
25,671
Activity Months12
Your Network
69 people
Work History
October 2025
2 Commits • 2 Features
Oct 1, 2025Month: 2025-10 — TFHE-rs GPU-focused performance improvements and reliability enhancements. Delivered two major GPU-enabled features for zama-ai/tfhe-rs, with a targeted refactor to improve benchmarking accuracy and coverage, and a fix to ensure reliable measurements. Key features delivered: - GPU benchmarking extension for 128-bit integer compression (GLWE_packing_compression_128b): introduced a dedicated GPU benchmark, refactoring the benchmarking structure to support packing/unpacking tests, and improving accuracy and coverage of GPU performance testing. Commit: fix(gpu): fix 128-bit compression benchmark (70773e442cd3d8d077546cab585a93ea37459137). - GPU-based re-randomization for TFHE integer operations: added CUDA kernels and bindings to accelerate encrypted computations, with updated benchmarks and API integrations. Commit: feat(gpu): implement re-randomization (867f8fb57915345fa767abd8c207d20271c37d20). Major bugs fixed: - Fixed 128-bit compression benchmark to improve reliability and measurement consistency. (Associated with 70773e442cd3d8d077546cab585a93ea37459137) Overall impact and accomplishments: - Accelerated GPU-enabled TFHE workloads by introducing dedicated GPU benchmarks and re-randomization kernels, enabling faster performance evaluation and optimization. - Improved benchmarking structure to support packing/unpacking tests, enhancing test coverage and confidence in GPU performance results. - API and benchmark integrations updated to streamline usage and enable wider adoption in GPU-accelerated encryption workflows. Technologies/skills demonstrated: - CUDA kernel development and GPU acceleration for cryptographic primitives - Rust bindings and integration with CUDA kernels - Benchmarking refactor focused on packing/unpacking workflows - Performance engineering, measurements, and reliability improvements Business value: - Faster delivery of GPU-accelerated cryptographic features, with more reliable performance data driving optimization decisions and customer confidence.
2 Commits • 2 Features
Oct 1, 2025Month: 2025-10 — TFHE-rs GPU-focused performance improvements and reliability enhancements. Delivered two major GPU-enabled features for zama-ai/tfhe-rs, with a targeted refactor to improve benchmarking accuracy and coverage, and a fix to ensure reliable measurements. Key features delivered: - GPU benchmarking extension for 128-bit integer compression (GLWE_packing_compression_128b): introduced a dedicated GPU benchmark, refactoring the benchmarking structure to support packing/unpacking tests, and improving accuracy and coverage of GPU performance testing. Commit: fix(gpu): fix 128-bit compression benchmark (70773e442cd3d8d077546cab585a93ea37459137). - GPU-based re-randomization for TFHE integer operations: added CUDA kernels and bindings to accelerate encrypted computations, with updated benchmarks and API integrations. Commit: feat(gpu): implement re-randomization (867f8fb57915345fa767abd8c207d20271c37d20). Major bugs fixed: - Fixed 128-bit compression benchmark to improve reliability and measurement consistency. (Associated with 70773e442cd3d8d077546cab585a93ea37459137) Overall impact and accomplishments: - Accelerated GPU-enabled TFHE workloads by introducing dedicated GPU benchmarks and re-randomization kernels, enabling faster performance evaluation and optimization. - Improved benchmarking structure to support packing/unpacking tests, enhancing test coverage and confidence in GPU performance results. - API and benchmark integrations updated to streamline usage and enable wider adoption in GPU-accelerated encryption workflows. Technologies/skills demonstrated: - CUDA kernel development and GPU acceleration for cryptographic primitives - Rust bindings and integration with CUDA kernels - Benchmarking refactor focused on packing/unpacking workflows - Performance engineering, measurements, and reliability improvements Business value: - Faster delivery of GPU-accelerated cryptographic features, with more reliable performance data driving optimization decisions and customer confidence.
October 2025
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for zama-ai/tfhe-rs focusing on GPU-backed cryptography improvements. Key deliverables centered on reliability, performance, and developer experience in the TFHE-RS GPU backend. 1) LWE expansion indexing improvements: refactor the indexing logic and introduce helper structures for compact LWE lists and expand jobs to simplify data flow and improve maintainability. 2) Safety and memory fixes in GPU expansion: added an assertion to ensure the carry modulus is not smaller than the message modulus to prevent data corruption, and addressed potential overflow by using 64-bit sizing for large block allocations. 3) GPU PBS 128-bit multi-bit testing, benchmarking, and documentation: enhanced testing/benchmarking, removed outdated LUT index concepts, and added documentation for GPU-accelerated noise squashing with a Rust code example and configuration details. These changes collectively improve correctness, throughput, and developer onboarding for GPU-accelerated cryptographic workloads.
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for zama-ai/tfhe-rs focusing on GPU-backed cryptography improvements. Key deliverables centered on reliability, performance, and developer experience in the TFHE-RS GPU backend. 1) LWE expansion indexing improvements: refactor the indexing logic and introduce helper structures for compact LWE lists and expand jobs to simplify data flow and improve maintainability. 2) Safety and memory fixes in GPU expansion: added an assertion to ensure the carry modulus is not smaller than the message modulus to prevent data corruption, and addressed potential overflow by using 64-bit sizing for large block allocations. 3) GPU PBS 128-bit multi-bit testing, benchmarking, and documentation: enhanced testing/benchmarking, removed outdated LUT index concepts, and added documentation for GPU-accelerated noise squashing with a Rust code example and configuration details. These changes collectively improve correctness, throughput, and developer onboarding for GPU-accelerated cryptographic workloads.
August 2025
5 Commits • 2 Features
Aug 1, 2025Month: 2025-08 — TFHE-rs (zama-ai/tfhe-rs) delivered key GPU-related enhancements and a critical CUDA backend fix. The work focused on 128-bit compression and PBS on GPU, plus a signature fix to ensure consistent decompression across backends. These changes unlock larger-scale encrypted computations and improve CPU-GPU data transfer efficiency, with tests updated accordingly.
5 Commits • 2 Features
Aug 1, 2025Month: 2025-08 — TFHE-rs (zama-ai/tfhe-rs) delivered key GPU-related enhancements and a critical CUDA backend fix. The work focused on 128-bit compression and PBS on GPU, plus a signature fix to ensure consistent decompression across backends. These changes unlock larger-scale encrypted computations and improve CPU-GPU data transfer efficiency, with tests updated accordingly.
August 2025
July 2025
8 Commits • 2 Features
Jul 1, 2025Month: 2025-07 — GPU-focused performance and reliability work on zama-ai/tfhe-rs delivering measurable throughput gains, accurate benchmarking, and robust multi-GPU behavior. Key outcomes include (1) GPU PBS throughput improvements via refactor of the classical PBS entry point and introduction of a centered modulus switching technique (PBS_MS_REDUCTION_T), enabling stronger noise-reduction strategies and higher throughput, (commits: 22ddba7145..., 94d24e1f8b...); (2) ERC20 GPU throughput regression fixed by reverting changes and enforcing sequential processing to reflect true performance (commit: 1b98312e2c...); (3) benchmarking accuracy and multi-GPU throughput fixes improving reliability across CUDA streams, multi-GPU compression/expansion, and ZK throughput tests (commits: 23ebd42209..., 9960f5e8b6..., d3dd010deb...); (4) CUDA device indexing corrected to ensure the correct GPU is targeted (commit: 62e6504ef0...); (5) TFHE CUDA backend broadcast robustness improved by refactoring broadcast_lut for multi-GPU use (commit: 7ecda32b41...). These changes collectively increase performance, measurement fidelity, and deployment reliability.
July 2025
8 Commits • 2 Features
Jul 1, 2025Month: 2025-07 — GPU-focused performance and reliability work on zama-ai/tfhe-rs delivering measurable throughput gains, accurate benchmarking, and robust multi-GPU behavior. Key outcomes include (1) GPU PBS throughput improvements via refactor of the classical PBS entry point and introduction of a centered modulus switching technique (PBS_MS_REDUCTION_T), enabling stronger noise-reduction strategies and higher throughput, (commits: 22ddba7145..., 94d24e1f8b...); (2) ERC20 GPU throughput regression fixed by reverting changes and enforcing sequential processing to reflect true performance (commit: 1b98312e2c...); (3) benchmarking accuracy and multi-GPU throughput fixes improving reliability across CUDA streams, multi-GPU compression/expansion, and ZK throughput tests (commits: 23ebd42209..., 9960f5e8b6..., d3dd010deb...); (4) CUDA device indexing corrected to ensure the correct GPU is targeted (commit: 62e6504ef0...); (5) TFHE CUDA backend broadcast robustness improved by refactoring broadcast_lut for multi-GPU use (commit: 7ecda32b41...). These changes collectively increase performance, measurement fidelity, and deployment reliability.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focusing on key achievements in the TFHE GPU backend for the zama-ai/tfhe-rs repository. Emphasis on reliable memory handling for large parameter sets and enabling more complex homomorphic computations on the GPU, positioning the project for greater scalability and business value.
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focusing on key achievements in the TFHE GPU backend for the zama-ai/tfhe-rs repository. Emphasis on reliable memory handling for large parameter sets and enabling more complex homomorphic computations on the GPU, positioning the project for greater scalability and business value.
June 2025
May 2025
11 Commits • 3 Features
May 1, 2025Summary for 2025-05: tfhe-rs GPU backend delivered scalable multi-GPU execution and dynamic backend improvements, enhanced reliability across hardware, and strengthened QA. Key capabilities include user-selectable multi-GPU computation, removal of internal CUDA_STREAMS for simpler, more robust operation, dynamic switching between TBC and CG variants based on workload, and Hopper GPU compatibility fixes. Augmented benchmarking and testing ensure correctness and performance of GPU-accelerated features across configurations. Business impact: higher throughput for multi-GPU workflows, reduced maintenance burden, and broader hardware compatibility, enabling customers to run larger cryptographic workloads more efficiently.
May 2025
11 Commits • 3 Features
May 1, 2025Summary for 2025-05: tfhe-rs GPU backend delivered scalable multi-GPU execution and dynamic backend improvements, enhanced reliability across hardware, and strengthened QA. Key capabilities include user-selectable multi-GPU computation, removal of internal CUDA_STREAMS for simpler, more robust operation, dynamic switching between TBC and CG variants based on workload, and Hopper GPU compatibility fixes. Augmented benchmarking and testing ensure correctness and performance of GPU-accelerated features across configurations. Business impact: higher throughput for multi-GPU workflows, reduced maintenance burden, and broader hardware compatibility, enabling customers to run larger cryptographic workloads more efficiently.
April 2025
6 Commits • 2 Features
Apr 1, 2025Monthly Summary for 2025-04: Focused on enabling GPU-accelerated expand workflows in the tfhe-rs stack, with emphasis on test/benchmark tooling, memory reliability, and HL API integration. Delivered new parameter configurations, fixed critical resource leaks, and expanded GPU-backed capabilities to improve throughput and end-to-end performance for ZK proofs. Key Achievements (top 3-5): - GPU parameter configurations for tests/benchmarks and ZK-PKE: Added and updated multi-bit parameter sets to reflect current choices and improve expand throughput benchmarks; commits include updating C++ test/benchmark tools and adding multi-bit parameter sets for ZK expand. - ZK Expand memory leak fix on TFHE-rs GPU backend: Fixed a memory leak in zk_expand_mem destructor and ensured all temporary GPU buffers are released to prevent resource exhaustion during ZK operations on the GPU. - GPU acceleration for expand operations in High-Level API and related CUDA backend cleanup: Introduced GPU-accelerated expand for the HL API, refactored CUDA key switching handling, removed unnecessary synchronization alias, and extended GPU expand support to CompactCiphertextList. Overall impact: - Improved test/benchmark throughput and scenario coverage, enabling faster evaluation of parameter choices and ZK-PKE workflows. - Increased stability and resource reliability for GPU-backed ZK expand operations, reducing risk of memory exhaustion in long-running tasks. - Enhanced end-to-end performance for ZK proofs via the HL API, with broader GPU support and cleaner CUDA integration. Technologies/Skills demonstrated: - GPU acceleration (CUDA), GPU resource management, and memory lifecycle handling - High-Level API integration for GPU-accelerated expand - Parameter management and benchmark tooling for ZK workflows - Code hygiene and refactoring (removing synchronization alias, backend cleanup)
6 Commits • 2 Features
Apr 1, 2025Monthly Summary for 2025-04: Focused on enabling GPU-accelerated expand workflows in the tfhe-rs stack, with emphasis on test/benchmark tooling, memory reliability, and HL API integration. Delivered new parameter configurations, fixed critical resource leaks, and expanded GPU-backed capabilities to improve throughput and end-to-end performance for ZK proofs. Key Achievements (top 3-5): - GPU parameter configurations for tests/benchmarks and ZK-PKE: Added and updated multi-bit parameter sets to reflect current choices and improve expand throughput benchmarks; commits include updating C++ test/benchmark tools and adding multi-bit parameter sets for ZK expand. - ZK Expand memory leak fix on TFHE-rs GPU backend: Fixed a memory leak in zk_expand_mem destructor and ensured all temporary GPU buffers are released to prevent resource exhaustion during ZK operations on the GPU. - GPU acceleration for expand operations in High-Level API and related CUDA backend cleanup: Introduced GPU-accelerated expand for the HL API, refactored CUDA key switching handling, removed unnecessary synchronization alias, and extended GPU expand support to CompactCiphertextList. Overall impact: - Improved test/benchmark throughput and scenario coverage, enabling faster evaluation of parameter choices and ZK-PKE workflows. - Increased stability and resource reliability for GPU-backed ZK expand operations, reducing risk of memory exhaustion in long-running tasks. - Enhanced end-to-end performance for ZK proofs via the HL API, with broader GPU support and cleaner CUDA integration. Technologies/Skills demonstrated: - GPU acceleration (CUDA), GPU resource management, and memory lifecycle handling - High-Level API integration for GPU-accelerated expand - Parameter management and benchmark tooling for ZK workflows - Code hygiene and refactoring (removing synchronization alias, backend cleanup)
April 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025Month: 2025-03 — Focused delivery of GPU-accelerated Zero-Knowledge (ZK) expansion for TFHE in zama-ai/tfhe-rs. The work centers on enabling GPU-based expansion of compact ciphertexts by adding CUDA kernels and integrating them with the C++ backend and Rust bindings. Build scripts, headers, and backend components were updated to support the GPU path, setting the foundation for performance improvements in encrypted operations. No major bugs fixed were reported for this period; the primary objective was feature delivery and groundwork for scalable, GPU-accelerated cryptographic operations. The changes align with the roadmap for higher throughput and lower CPU load in real-world workloads. Business value: unlocks GPU offload for ZK expansion, enabling faster, more scalable encrypted computations in production and accelerating onboarding of GPU-accelerated cryptographic primitives. Technologies/skills demonstrated: CUDA kernel development, C++ backend integration, Rust-C++ bindings refinement, build-system modernization, cross-language GPU acceleration, cryptography primitives (TFHE).
March 2025
1 Commits • 1 Features
Mar 1, 2025Month: 2025-03 — Focused delivery of GPU-accelerated Zero-Knowledge (ZK) expansion for TFHE in zama-ai/tfhe-rs. The work centers on enabling GPU-based expansion of compact ciphertexts by adding CUDA kernels and integrating them with the C++ backend and Rust bindings. Build scripts, headers, and backend components were updated to support the GPU path, setting the foundation for performance improvements in encrypted operations. No major bugs fixed were reported for this period; the primary objective was feature delivery and groundwork for scalable, GPU-accelerated cryptographic operations. The changes align with the roadmap for higher throughput and lower CPU load in real-world workloads. Business value: unlocks GPU offload for ZK expansion, enabling faster, more scalable encrypted computations in production and accelerating onboarding of GPU-accelerated cryptographic primitives. Technologies/skills demonstrated: CUDA kernel development, C++ backend integration, Rust-C++ bindings refinement, build-system modernization, cross-language GPU acceleration, cryptography primitives (TFHE).
February 2025
6 Commits • 2 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for zama-ai/tfhe-rs: Implemented GPU-accelerated TFHE backend enhancements with CUDA API integration, refined GLWE→LWE extraction for granular control, and executed stability fixes and tighter memory management to improve accuracy and reliability of GPU operations. These changes deliver stronger performance for CUDA-backed workloads, better user control over ciphertext extraction, and higher correctness guarantees in compression/decompression paths.
6 Commits • 2 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for zama-ai/tfhe-rs: Implemented GPU-accelerated TFHE backend enhancements with CUDA API integration, refined GLWE→LWE extraction for granular control, and executed stability fixes and tighter memory management to improve accuracy and reliability of GPU operations. These changes deliver stronger performance for CUDA-backed workloads, better user control over ciphertext extraction, and higher correctness guarantees in compression/decompression paths.
February 2025
January 2025
7 Commits • 1 Features
Jan 1, 2025January 2025 performance snapshot: Strengthened multi-GPU support and device management for the tfhe-rs backend, delivering scalable, reliable multi-device integer operations and improved test coverage. The work emphasizes business value through robust, traceable, and high-throughput GPU processing for cryptographic workloads.
January 2025
7 Commits • 1 Features
Jan 1, 2025January 2025 performance snapshot: Strengthened multi-GPU support and device management for the tfhe-rs backend, delivering scalable, reliable multi-device integer operations and improved test coverage. The work emphasizes business value through robust, traceable, and high-throughput GPU processing for cryptographic workloads.
December 2024
1 Commits
Dec 1, 2024December 2024 - tfhe-rs: GPU TFHE Integer Compression LUT Delta-Precision Alignment. Fixed inconsistency in LUT generation for decompression by aligning the GPU LUT delta precision with the CPU implementation, improving correctness and reliability of compressed integer data processing. The fix was ported to the GPU compression encoding path and committed to zama-ai/tfhe-rs, ensuring cross-architecture consistency and reducing risk of data corruption. This work strengthens the GPU path without impacting CPU behavior, and sets the stage for future performance and correctness improvements.
1 Commits
Dec 1, 2024December 2024 - tfhe-rs: GPU TFHE Integer Compression LUT Delta-Precision Alignment. Fixed inconsistency in LUT generation for decompression by aligning the GPU LUT delta precision with the CPU implementation, improving correctness and reliability of compressed integer data processing. The fix was ported to the GPU compression encoding path and committed to zama-ai/tfhe-rs, ensuring cross-architecture consistency and reducing risk of data corruption. This work strengthens the GPU path without impacting CPU behavior, and sets the stage for future performance and correctness improvements.
December 2024
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for zama-ai/tfhe-rs focused on correctness hardening of GPU-backed PBS pathways and targeted CUDA backend performance optimizations. Delivered a GPU PBS correctness fix and refactor to improve accuracy and maintainability, and implemented CUDA backend performance enhancements to streamline integer operations and bit-length calculations. These changes reduced production risk and laid groundwork for higher cryptographic throughput in production workloads.
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for zama-ai/tfhe-rs focused on correctness hardening of GPU-backed PBS pathways and targeted CUDA backend performance optimizations. Delivered a GPU PBS correctness fix and refactor to improve accuracy and maintainability, and implemented CUDA backend performance enhancements to streamline integer operations and bit-length calculations. These changes reduced production risk and laid groundwork for higher cryptographic throughput in production workloads.
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Quality Metrics
Correctness91.0%
Maintainability87.0%
Architecture87.4%
Performance83.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CUDACudaMakefileMarkdownRust
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentBenchmarkingC++C++ ProgrammingC++ Template MetaprogrammingC++ template metaprogrammingCUDACUDA Kernel OptimizationCUDA ProgrammingCUDA programmingCode RefactoringCode ReviewCode refactoring
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