zama-ai/tfhe-rs
Nov 2024 – Oct 2025
12 Months active
Languages Used
C++CUDARustCUDA C++MarkdownYAMLCJavaScript
Technical Skills
CUDACryptographyGPU ComputingAlgorithm ImplementationC++C++ Programming
Guillermo Oyarzun
PROFILE
Guillermo Oyarzun
Guillermo Oyarzun engineered GPU-accelerated cryptographic features and backend enhancements for the zama-ai/tfhe-rs repository, focusing on scalable, high-performance fully homomorphic encryption. He implemented CUDA-based kernels for programmable bootstrapping, random number generation, and noise reduction, optimizing memory management and parallel computation to improve throughput and reliability. Guillermo addressed correctness and stability by fixing memory leaks, race conditions, and edge-case bugs, while refactoring code for maintainability and performance. Leveraging C++, Rust, and CUDA, he delivered robust API integrations and benchmarking tools, enabling efficient encrypted data operations. His work demonstrated deep technical understanding and delivered production-ready GPU cryptography solutions.
Overall Statistics
Feature vs Bugs
68%Features
Repository Contributions
49Total
Bugs
9
Commits
49
Features
19
Lines of code
19,284
Activity Months12
Your Network
69 people
Work History
October 2025
4 Commits • 1 Features
Oct 1, 2025Month: 2025-10 – Performance and reliability boost for TFHE GPU backend in zama-ai/tfhe-rs. Delivered GPU-accelerated classical PBS features, stabilized throughput benchmarks on multi-GPU setups, and eliminated a GPU memory leak in decompression. These changes collectively enhance throughput, correctness, and resource efficiency for secure computation workloads running on GPU.
4 Commits • 1 Features
Oct 1, 2025Month: 2025-10 – Performance and reliability boost for TFHE GPU backend in zama-ai/tfhe-rs. Delivered GPU-accelerated classical PBS features, stabilized throughput benchmarks on multi-GPU setups, and eliminated a GPU memory leak in decompression. These changes collectively enhance throughput, correctness, and resource efficiency for secure computation workloads running on GPU.
October 2025
September 2025
5 Commits • 3 Features
Sep 1, 2025During September 2025, the tfhe-rs GPU backend delivered notable performance and reliability improvements. The team implemented GPU-aware benchmarking to tailor workloads to hardware, added preallocated host buffers for LUT generation to enable concurrent streams, and cleaned up the build to reduce dependencies. In parallel, we addressed memory synchronization issues in the GPU backend to prevent race conditions, and simplified the build process by removing nvToolsExt dependency. These changes collectively enhance GPU utilization, throughput, and build reliability, delivering measurable business value in performance-sensitive cryptographic workloads.
September 2025
5 Commits • 3 Features
Sep 1, 2025During September 2025, the tfhe-rs GPU backend delivered notable performance and reliability improvements. The team implemented GPU-aware benchmarking to tailor workloads to hardware, added preallocated host buffers for LUT generation to enable concurrent streams, and cleaned up the build to reduce dependencies. In parallel, we addressed memory synchronization issues in the GPU backend to prevent race conditions, and simplified the build process by removing nvToolsExt dependency. These changes collectively enhance GPU utilization, throughput, and build reliability, delivering measurable business value in performance-sensitive cryptographic workloads.
August 2025
8 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for zama-ai/tfhe-rs focusing on GPU backend and parameterization improvements, with a notable memory leak fix and version alignment. Delivered multi-GPU performance enhancements, more flexible PBS/LUT handling, and enhanced profiling.
8 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for zama-ai/tfhe-rs focusing on GPU backend and parameterization improvements, with a notable memory leak fix and version alignment. Delivered multi-GPU performance enhancements, more flexible PBS/LUT handling, and enhanced profiling.
August 2025
July 2025
9 Commits • 3 Features
Jul 1, 2025Concise monthly summary for 2025-07 focused on delivering performance improvements and reliability for the TFHE GPU backend, with clear business value for encrypted workload throughput and stability.
July 2025
9 Commits • 3 Features
Jul 1, 2025Concise monthly summary for 2025-07 focused on delivering performance improvements and reliability for the TFHE GPU backend, with clear business value for encrypted workload throughput and stability.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for zama-ai/tfhe-rs focused on GPU backend enhancements to strengthen performance and reliability of GPU-based TFHE cryptography. The work consolidates two GPU backend improvements into a cohesive upgrade, delivering faster, more reliable operations and improved resource utilization for latency-sensitive workloads.
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for zama-ai/tfhe-rs focused on GPU backend enhancements to strengthen performance and reliability of GPU-based TFHE cryptography. The work consolidates two GPU backend improvements into a cohesive upgrade, delivering faster, more reliable operations and improved resource utilization for latency-sensitive workloads.
June 2025
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for zama-ai/tfhe-rs focused on GPU TFHE backend enhancements and robustness improvements. Key improvements include modulus handling enhancements, noise squashing, and a correctness fix to eliminate hardcoded modulus usage. These changes collectively improve performance, numerical correctness, and reliability of GPU-based homomorphic operations, aligning with business goals of faster, more scalable encrypted computation.
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for zama-ai/tfhe-rs focused on GPU TFHE backend enhancements and robustness improvements. Key improvements include modulus handling enhancements, noise squashing, and a correctness fix to eliminate hardcoded modulus usage. These changes collectively improve performance, numerical correctness, and reliability of GPU-based homomorphic operations, aligning with business goals of faster, more scalable encrypted computation.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered GPU-accelerated drift testing and noise-reduction enhancements for 128-bit PBS in tfhe-rs. Refactored key generation and bootstrapping to support a 128-bit data type and new noise-reduction parameters, improving reliability and accuracy of GPU-based programmable bootstrapping.
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered GPU-accelerated drift testing and noise-reduction enhancements for 128-bit PBS in tfhe-rs. Refactored key generation and bootstrapping to support a 128-bit data type and new noise-reduction parameters, improving reliability and accuracy of GPU-based programmable bootstrapping.
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: Key GPU acceleration work on zama-ai/tfhe-rs focused on modulus switch noise reduction and robust GPU backend checks. Cross-module CUDA integration and headers updates laid groundwork for faster TFHE computations and improved reliability.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: Key GPU acceleration work on zama-ai/tfhe-rs focused on modulus switch noise reduction and robust GPU backend checks. Cross-module CUDA integration and headers updates laid groundwork for faster TFHE computations and improved reliability.
February 2025
10 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for zama-ai/tfhe-rs: Delivered scalable GPU-backed TFHE enhancements on the CUDA backend with robust large-integer support, expanded PBS configurations, and improved data size/bit-width handling. Added GPU support for boolean if-then-else in the high-level API and completed a critical GPU division/remainder assertion fix. Performed targeted refactors and code hygiene improvements to boost reliability and maintainability. Focused on delivering business value through higher throughput, larger problem sizes, and parity between CPU and GPU paths.
10 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for zama-ai/tfhe-rs: Delivered scalable GPU-backed TFHE enhancements on the CUDA backend with robust large-integer support, expanded PBS configurations, and improved data size/bit-width handling. Added GPU support for boolean if-then-else in the high-level API and completed a critical GPU division/remainder assertion fix. Performed targeted refactors and code hygiene improvements to boost reliability and maintainability. Focused on delivering business value through higher throughput, larger problem sizes, and parity between CPU and GPU paths.
February 2025
January 2025
2 Commits • 1 Features
Jan 1, 20252025-01 Monthly Summary for zama-ai/tfhe-rs: Delivered GPU-accelerated RNG for fully homomorphic encryption with CUDA kernels and API bindings, enabling faster generation of encrypted random values on the GPU. Implemented robustness improvements in GPU workflows, including fixing the match_value corner case by defaulting zero bits to one and adding empty-slice checks in vector_find and vector_find_radix to prevent panics and return a trivial ciphertext. Updated and benchmarked GPU-capable APIs, establishing a baseline for performance improvements and smoother integration into existing crypto workloads. These efforts improve throughput, reduce latency for GPU-assisted operations, and increase stability for production deployments.
January 2025
2 Commits • 1 Features
Jan 1, 20252025-01 Monthly Summary for zama-ai/tfhe-rs: Delivered GPU-accelerated RNG for fully homomorphic encryption with CUDA kernels and API bindings, enabling faster generation of encrypted random values on the GPU. Implemented robustness improvements in GPU workflows, including fixing the match_value corner case by defaulting zero bits to one and adding empty-slice checks in vector_find and vector_find_radix to prevent panics and return a trivial ciphertext. Updated and benchmarked GPU-capable APIs, establishing a baseline for performance improvements and smoother integration into existing crypto workloads. These efforts improve throughput, reduce latency for GPU-assisted operations, and increase stability for production deployments.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for zama-ai/tfhe-rs focusing on GPU-accelerated data operations for TFHE in Rust. Implemented GPU-accelerated subarray search and vector comparisons on CUDA, including GPU-backed integration of multiple univariate LUTs for subarray processing. These changes reduce latency and improve throughput for encrypted, array-like data operations and pave the way for scalable secure analytics. Primary focus this month was feature delivery and performance optimization with no mission-critical bug fixes recorded.
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for zama-ai/tfhe-rs focusing on GPU-accelerated data operations for TFHE in Rust. Implemented GPU-accelerated subarray search and vector comparisons on CUDA, including GPU-backed integration of multiple univariate LUTs for subarray processing. These changes reduce latency and improve throughput for encrypted, array-like data operations and pave the way for scalable secure analytics. Primary focus this month was feature delivery and performance optimization with no mission-critical bug fixes recorded.
December 2024
November 2024
1 Commits
Nov 1, 2024Month: 2024-11 — Focus on correctness and stability of the CUDA backend for TFHE in zama-ai/tfhe-rs. Delivered a targeted fix to the single-block signed overflow path, improving reliability of GPU-based homomorphic operations.
November 2024
1 Commits
Nov 1, 2024Month: 2024-11 — Focus on correctness and stability of the CUDA backend for TFHE in zama-ai/tfhe-rs. Delivered a targeted fix to the single-block signed overflow path, improving reliability of GPU-based homomorphic operations.
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Quality Metrics
Correctness89.8%
Maintainability86.8%
Architecture86.8%
Performance87.0%
AI Usage21.2%
Skills & Technologies
Programming Languages
CC++CUDACUDA C++JavaScriptMarkdownRustTOMLYAML
Technical Skills
API DevelopmentAlgorithm ImplementationAlgorithm OptimizationBackend DevelopmentBenchmarkingBuild System ConfigurationBuild SystemsC APIC++C++ ProgrammingC++ Template MetaprogrammingCI/CDCUDACUDA ProgrammingCode Cleanup
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