zama-ai/tfhe-rs
Jun 2024 – Mar 2026
19 Months active
Languages Used
CMakeRustC++CUDACUDA C++MarkdownYAMLC
Technical Skills
CUDAGPU ProgrammingProfilingGPU programmingMathematicsParallel computing
Guillermo Oyarzun
PROFILE
Guillermo Oyarzun
Guillermo Oyarzun engineered GPU-accelerated cryptographic primitives and backend infrastructure for the zama-ai/tfhe-rs repository, focusing on high-throughput, reliable fully homomorphic encryption. He implemented CUDA-based programmable bootstrapping, noise reduction, and memory management optimizations, enabling scalable encrypted computation across multi-GPU environments. Guillermo’s work included architecture-aware kernel tuning, robust error handling, and extensive test automation to ensure correctness and performance under concurrent workloads. Leveraging C++, Rust, and CUDA, he delivered features such as GLWE packing, FFT optimizations, and dynamic benchmarking, while addressing memory leaks and race conditions. His contributions demonstrated deep technical rigor and improved both the reliability and maintainability of the codebase.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
79Total
Bugs
11
Commits
79
Features
29
Lines of code
33,952
Activity Months19
Your Network
76 peopleWork History
March 2026
7 Commits • 1 Features
Mar 1, 2026March 2026 — Zama TFHE-RS (zama-ai/tfhe-rs) Key features delivered: - Multi-bit Noise Testing Enhancements for GPU Backend: added new test patterns br_dp_ks_ms, packing_ks_ms, cpk_ks_ms, and pbs128, improving coverage, performance, and 128-bit PBS support. Major bugs fixed: - GPU Execution Reliability: fixed panic in GPU stream creation due to incorrect GPU count validation; ensured proper synchronization before copying data to CPU; streamlined CUDA bindings to improve maintainability. Overall impact and accomplishments: - Increased reliability and performance of GPU-accelerated cryptographic operations; expanded test coverage enabling faster validation cycles and safer production deployments; codebase is easier to maintain with simplified CUDA bindings. Technologies/skills demonstrated: - GPU programming (CUDA bindings), cryptographic testing patterns, performance tuning, and maintainability improvements.
7 Commits • 1 Features
Mar 1, 2026March 2026 — Zama TFHE-RS (zama-ai/tfhe-rs) Key features delivered: - Multi-bit Noise Testing Enhancements for GPU Backend: added new test patterns br_dp_ks_ms, packing_ks_ms, cpk_ks_ms, and pbs128, improving coverage, performance, and 128-bit PBS support. Major bugs fixed: - GPU Execution Reliability: fixed panic in GPU stream creation due to incorrect GPU count validation; ensured proper synchronization before copying data to CPU; streamlined CUDA bindings to improve maintainability. Overall impact and accomplishments: - Increased reliability and performance of GPU-accelerated cryptographic operations; expanded test coverage enabling faster validation cycles and safer production deployments; codebase is easier to maintain with simplified CUDA bindings. Technologies/skills demonstrated: - GPU programming (CUDA bindings), cryptographic testing patterns, performance tuning, and maintainability improvements.
March 2026
February 2026
11 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for zama-ai/tfhe-rs: Delivered substantial GPU-focused improvements to PBS and keyswitch workflows, plus expanded GPU testability and benchmarking for TFHE on GPU hardware. Key achievements include architecture-specific PBS optimizations (512-thread configuration for pbs128, multi-GPU thresholds, architecture-aware PBS layouts, dynamic thresholds aligned to GPU capabilities), a TBC version of PBS for 128-bit inputs, and code cleanup for FFT paths and memory optimizations. Implemented robust GPU synchronization and race-condition fixes in PBS and keyswitch reduction to ensure correct results under concurrent execution. Expanded GPU testing/benchmarking suite to cover noise distribution, re-rand processes, and tuned benchmarking parameters for hardware optimization. These efforts collectively improve throughput, stability, and scalability of GPU-accelerated TFHE workloads, delivering tangible business value in faster secure computations and more efficient hardware utilization. Technologies demonstrated include GPU programming, parallelization, architecture-aware optimization, memory management, synchronization, benchmarking, and test automation.
February 2026
11 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for zama-ai/tfhe-rs: Delivered substantial GPU-focused improvements to PBS and keyswitch workflows, plus expanded GPU testability and benchmarking for TFHE on GPU hardware. Key achievements include architecture-specific PBS optimizations (512-thread configuration for pbs128, multi-GPU thresholds, architecture-aware PBS layouts, dynamic thresholds aligned to GPU capabilities), a TBC version of PBS for 128-bit inputs, and code cleanup for FFT paths and memory optimizations. Implemented robust GPU synchronization and race-condition fixes in PBS and keyswitch reduction to ensure correct results under concurrent execution. Expanded GPU testing/benchmarking suite to cover noise distribution, re-rand processes, and tuned benchmarking parameters for hardware optimization. These efforts collectively improve throughput, stability, and scalability of GPU-accelerated TFHE workloads, delivering tangible business value in faster secure computations and more efficient hardware utilization. Technologies demonstrated include GPU programming, parallelization, architecture-aware optimization, memory management, synchronization, benchmarking, and test automation.
January 2026
4 Commits • 2 Features
Jan 1, 2026January 2026 highlights on zama-ai/tfhe-rs focused on GPU backend stability and testing efficiency across multi-GPU environments. Implemented robust resource management for CUDA operations and tightened correctness checks, while enhancing the GPU test suite to improve focus and feedback loops. The combination of stability improvements and targeted testing supports more reliable cryptographic operations and faster, safer releases.
4 Commits • 2 Features
Jan 1, 2026January 2026 highlights on zama-ai/tfhe-rs focused on GPU backend stability and testing efficiency across multi-GPU environments. Implemented robust resource management for CUDA operations and tightened correctness checks, while enhancing the GPU test suite to improve focus and feedback loops. The combination of stability improvements and targeted testing supports more reliable cryptographic operations and faster, safer releases.
January 2026
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 — Key feature delivered: GPU-accelerated GLWE packing and programmable bootstrap enhancements in zama-ai/tfhe-rs, consolidating two commits into a cohesive GPU-backend upgrade. Implemented new GLWE ciphertext extraction support, added tests for noise distribution and failure probabilities in packing keyswitching, and tuned data types to align with GPU register limits for memory efficiency. Fixed a GPU regression by restoring 64 registers in multi-bit PBS, improving stability and throughput. Impact: faster, more reliable homomorphic encryption primitives with better memory utilization; groundwork for higher production throughput. Technologies demonstrated: GPU kernel optimization, Rust (tfhe-rs), GPU backends, testing strategies, and memory alignment.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 — Key feature delivered: GPU-accelerated GLWE packing and programmable bootstrap enhancements in zama-ai/tfhe-rs, consolidating two commits into a cohesive GPU-backend upgrade. Implemented new GLWE ciphertext extraction support, added tests for noise distribution and failure probabilities in packing keyswitching, and tuned data types to align with GPU register limits for memory efficiency. Fixed a GPU regression by restoring 64 registers in multi-bit PBS, improving stability and throughput. Impact: faster, more reliable homomorphic encryption primitives with better memory utilization; groundwork for higher production throughput. Technologies demonstrated: GPU kernel optimization, Rust (tfhe-rs), GPU backends, testing strategies, and memory alignment.
November 2025
4 Commits • 2 Features
Nov 1, 2025November 2025 monthly work summary for zama-ai/tfhe-rs focused on GPU-accelerated TFHE back-end stability and scalability. Delivered safer LWE chunk sizing, robust GPU memory error handling, and improved PBS stability for small datasets. These changes increased reliability, reduced overflow risk, and improved multi-GPU utilization, enabling scalable cryptographic workloads and safer deployment.
4 Commits • 2 Features
Nov 1, 2025November 2025 monthly work summary for zama-ai/tfhe-rs focused on GPU-accelerated TFHE back-end stability and scalability. Delivered safer LWE chunk sizing, robust GPU memory error handling, and improved PBS stability for small datasets. These changes increased reliability, reduced overflow risk, and improved multi-GPU utilization, enabling scalable cryptographic workloads and safer deployment.
November 2025
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for zama-ai/tfhe-rs focusing on GPU-driven arithmetic propagation improvements. Delivered GPU-accelerated full propagation for addition and subtraction, with new event handling and memory management utilities to optimize performance and ensure correct carry and overflow behavior. This work lays groundwork for higher-throughput homomorphic operations on GPU and improves resource utilization in the cryptographic arithmetic path.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for zama-ai/tfhe-rs focusing on GPU-driven arithmetic propagation improvements. Delivered GPU-accelerated full propagation for addition and subtraction, with new event handling and memory management utilities to optimize performance and ensure correct carry and overflow behavior. This work lays groundwork for higher-throughput homomorphic operations on GPU and improves resource utilization in the cryptographic arithmetic path.
June 2024
1 Commits • 1 Features
Jun 1, 2024June 2024 monthly summary for zama-ai/tfhe-rs: No major bugs fixed; focus was on expanding observability through GPU profiling. Implemented NVTX-based GPU profiling to enable performance analysis and debugging of CUDA workloads in the TFHE Rust library. Added conditional compilation for profiling to minimize overhead when profiling is disabled and integrated NVTX range markers within CUDA functions to capture execution time and resource usage. This work delivers observability improvements, supports data-driven optimization decisions for GPU workloads, and accelerates debugging and performance tuning for GPU-accelerated components.
1 Commits • 1 Features
Jun 1, 2024June 2024 monthly summary for zama-ai/tfhe-rs: No major bugs fixed; focus was on expanding observability through GPU profiling. Implemented NVTX-based GPU profiling to enable performance analysis and debugging of CUDA workloads in the TFHE Rust library. Added conditional compilation for profiling to minimize overhead when profiling is disabled and integrated NVTX range markers within CUDA functions to capture execution time and resource usage. This work delivers observability improvements, supports data-driven optimization decisions for GPU workloads, and accelerates debugging and performance tuning for GPU-accelerated components.
June 2024
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Quality Metrics
Correctness91.0%
Maintainability85.4%
Architecture87.2%
Performance87.2%
AI Usage21.8%
Skills & Technologies
Programming Languages
CC++CMakeCUDACUDA C++JavaScriptMakefileMarkdownPythonRust
Technical Skills
API DevelopmentAlgorithm ImplementationAlgorithm OptimizationBackend DevelopmentBenchmarkingBuild System ConfigurationBuild SystemsC APIC++C++ ProgrammingC++ Template MetaprogrammingC++ developmentCI/CDCUDACUDA Programming
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