zama-ai/tfhe-rs
Nov 2024 – Oct 2025
8 Months active
Languages Used
C++RustCUDA
Technical Skills
Backend DevelopmentC++CUDAFFT AlgorithmsGPU ComputingGPU Programming
Beka Barbakadze
PROFILE
Beka Barbakadze
Beka Barbakadze contributed to the zama-ai/tfhe-rs repository by engineering GPU-accelerated features and stability improvements for homomorphic encryption workloads. Over eight months, Beka implemented optimized CUDA backends for FFT and integer operations, introduced parallel division across multiple GPUs, and enhanced memory management to reduce host-device transfers. Using C++, CUDA, and Rust, Beka refactored core cryptographic routines for higher throughput and precision, addressed edge-case bugs in comparison and memory allocation, and stabilized multi-GPU execution paths. The work demonstrated deep expertise in low-level optimization, parallel processing, and backend development, resulting in more robust, scalable, and performant cryptographic operations within the project.
Overall Statistics
Feature vs Bugs
64%Features
Repository Contributions
16Total
Bugs
4
Commits
16
Features
7
Lines of code
41,499
Activity Months8
Your Network
69 people
Work History
October 2025
1 Commits
Oct 1, 2025October 2025 focused on hardening the TFHE-rs CUDA backend for edge-case safety in comparison operations. Addressed robustness gaps when dealing with zero radix blocks by implementing initialization safeguards and introducing a new helper to manage edge-case ciphertexts. These changes reduced risk of runtime errors and improved reliability of CUDA-accelerated cryptographic operations within the repository.
1 Commits
Oct 1, 2025October 2025 focused on hardening the TFHE-rs CUDA backend for edge-case safety in comparison operations. Addressed robustness gaps when dealing with zero radix blocks by implementing initialization safeguards and introducing a new helper to manage edge-case ciphertexts. These changes reduced risk of runtime errors and improved reliability of CUDA-accelerated cryptographic operations within the repository.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for zama-ai/tfhe-rs focusing on feature delivery and performance improvements.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for zama-ai/tfhe-rs focusing on feature delivery and performance improvements.
July 2025
1 Commits
Jul 1, 2025July 2025 Monthly Summary for zama-ai/tfhe-rs: - Key feature delivered: PBS128 multi-GPU stability improvement. Specifically, corrected the device allocation step before scratchpad memory allocation to ensure the correct GPU is targeted when running multif GPU scenarios. This change reduces cross-GPU memory allocation issues and increases reliability of multi-GPU TFHE workloads. - Major bug fixed: PBS128 multi-GPU bug related to device selection prior to scratchpad allocation. The fix prevents incorrect device usage in multi-GPU environments, addressing instability and potential memory errors during cryptographic operations. - Scope and impact: Stabilizes multi-GPU operations in the TFHE Rust library, improving correctness, uptime, and predictability for deployments that rely on PBS128 across multiple GPUs. This supports higher-throughput cryptographic workloads and reduces maintenance overhead due to GPU-related failures. - Technologies and skills demonstrated: Rust-based GPU programming, memory management, multi-GPU synchronization, targeted debugging, and codebase traceability via commit history. The change is traceable to commit c6865ab88051b0ea6dd459f72dfe24496092673f with message "fix(gpu): fix pbs128 multi-gpu bug". Business value: Increased reliability and correctness of cryptographic operations in multi-GPU environments, enabling safer scaling of workloads and reducing downtime due to GPU allocation issues.
1 Commits
Jul 1, 2025July 2025 Monthly Summary for zama-ai/tfhe-rs: - Key feature delivered: PBS128 multi-GPU stability improvement. Specifically, corrected the device allocation step before scratchpad memory allocation to ensure the correct GPU is targeted when running multif GPU scenarios. This change reduces cross-GPU memory allocation issues and increases reliability of multi-GPU TFHE workloads. - Major bug fixed: PBS128 multi-GPU bug related to device selection prior to scratchpad allocation. The fix prevents incorrect device usage in multi-GPU environments, addressing instability and potential memory errors during cryptographic operations. - Scope and impact: Stabilizes multi-GPU operations in the TFHE Rust library, improving correctness, uptime, and predictability for deployments that rely on PBS128 across multiple GPUs. This supports higher-throughput cryptographic workloads and reduces maintenance overhead due to GPU-related failures. - Technologies and skills demonstrated: Rust-based GPU programming, memory management, multi-GPU synchronization, targeted debugging, and codebase traceability via commit history. The change is traceable to commit c6865ab88051b0ea6dd459f72dfe24496092673f with message "fix(gpu): fix pbs128 multi-gpu bug". Business value: Increased reliability and correctness of cryptographic operations in multi-GPU environments, enabling safer scaling of workloads and reducing downtime due to GPU allocation issues.
July 2025
June 2025
2 Commits • 1 Features
Jun 1, 20252025-06 Monthly Summary — zama-ai/tfhe-rs: GPU-accelerated sum_ciphertext optimization in the TFHE CUDA backend. This work consolidates GPU-side optimizations for summing ciphertexts, refactors the sum_ciphertext path, and improves memory management to minimize host data transfers. A final_calculation step keeps computation on the GPU, unlocking higher throughput for large ciphertext batches.
June 2025
2 Commits • 1 Features
Jun 1, 20252025-06 Monthly Summary — zama-ai/tfhe-rs: GPU-accelerated sum_ciphertext optimization in the TFHE CUDA backend. This work consolidates GPU-side optimizations for summing ciphertexts, refactors the sum_ciphertext path, and improves memory management to minimize host data transfers. A final_calculation step keeps computation on the GPU, unlocking higher throughput for large ciphertext batches.
March 2025
4 Commits • 1 Features
Mar 1, 2025March 2025 performance and delivery, tfhe-rs: Implemented 128-bit PBS on GPUs and 128-bit CG PBS (Cooperative Groups) with unified LUT and memory handling, enabling higher-precision homomorphic computations and GPU acceleration. Addressed a critical shared memory sizing bug for CG PBS, and streamlined code by removing obsolete index buffers for PBS128 across the GPU path. These changes improve precision, throughput, and resource efficiency in GPU-based homomorphic encryption workloads for Zama's products.
4 Commits • 1 Features
Mar 1, 2025March 2025 performance and delivery, tfhe-rs: Implemented 128-bit PBS on GPUs and 128-bit CG PBS (Cooperative Groups) with unified LUT and memory handling, enabling higher-precision homomorphic computations and GPU acceleration. Addressed a critical shared memory sizing bug for CG PBS, and streamlined code by removing obsolete index buffers for PBS128 across the GPU path. These changes improve precision, throughput, and resource efficiency in GPU-based homomorphic encryption workloads for Zama's products.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025Month: 2025-02. This monthly summary highlights performance-focused backend work on the TFHE Rust binding (zama-ai/tfhe-rs). Key features delivered center on CUDA backend optimizations to boost throughput and numerical stability for cryptographic workloads. Specifically: - TFHE CUDA backend: FFT twiddle factor initialization refactor to use hexadecimal representations for 64-bit twiddles in twiddles.cu, improving code clarity and potential optimization opportunities. - TFHE CUDA backend: replaced standard double2 arithmetic with CUDA intrinsics (__dadd_rn, __dsub_rn, __dmul_rn, __fma_rn, __drcp_rn) to enhance performance and precision for complex-number operations. No major bugs fixed this month; the focus was on performance, reliability, and maintainability of the GPU backend. The changes lay groundwork for faster TFHE operations on GPU and better scalability of cryptographic workloads. Technologies/skills demonstrated include CUDA programming, GPU intrinsics, FFT optimization, code refactoring for clarity, and performance-driven software engineering.
February 2025
2 Commits • 1 Features
Feb 1, 2025Month: 2025-02. This monthly summary highlights performance-focused backend work on the TFHE Rust binding (zama-ai/tfhe-rs). Key features delivered center on CUDA backend optimizations to boost throughput and numerical stability for cryptographic workloads. Specifically: - TFHE CUDA backend: FFT twiddle factor initialization refactor to use hexadecimal representations for 64-bit twiddles in twiddles.cu, improving code clarity and potential optimization opportunities. - TFHE CUDA backend: replaced standard double2 arithmetic with CUDA intrinsics (__dadd_rn, __dsub_rn, __dmul_rn, __fma_rn, __drcp_rn) to enhance performance and precision for complex-number operations. No major bugs fixed this month; the focus was on performance, reliability, and maintainability of the GPU backend. The changes lay groundwork for faster TFHE operations on GPU and better scalability of cryptographic workloads. Technologies/skills demonstrated include CUDA programming, GPU intrinsics, FFT optimization, code refactoring for clarity, and performance-driven software engineering.
December 2024
1 Commits
Dec 1, 2024December 2024 monthly summary for zama-ai/tfhe-rs: Stabilized the GPU path for homomorphic encryption by addressing a critical noise-level handling bug and reinforcing data integrity across ciphertext blocks. Focused on robustness, performance, and verifiability of noise propagation.
1 Commits
Dec 1, 2024December 2024 monthly summary for zama-ai/tfhe-rs: Stabilized the GPU path for homomorphic encryption by addressing a critical noise-level handling bug and reinforcing data integrity across ciphertext blocks. Focused on robustness, performance, and verifiability of noise propagation.
December 2024
November 2024
4 Commits • 3 Features
Nov 1, 2024November 2024 (2024-11) monthly summary for zama-ai/tfhe-rs focused on GPU backend enhancements in the TFHE-rs project. Three major GPU-related features were delivered, with no documented major bug fixes for this period. The updates improve accuracy, throughput, and performance for encrypted computations on CUDA-enabled hardware, aligning with performance and scalability goals for privacy-preserving workloads.
November 2024
4 Commits • 3 Features
Nov 1, 2024November 2024 (2024-11) monthly summary for zama-ai/tfhe-rs focused on GPU backend enhancements in the TFHE-rs project. Three major GPU-related features were delivered, with no documented major bug fixes for this period. The updates improve accuracy, throughput, and performance for encrypted computations on CUDA-enabled hardware, aligning with performance and scalability goals for privacy-preserving workloads.
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Quality Metrics
Correctness93.8%
Maintainability85.6%
Architecture88.2%
Performance90.0%
AI Usage21.2%
Skills & Technologies
Programming Languages
C++CUDARust
Technical Skills
Algorithm OptimizationBackend DevelopmentC++CUDAFFT AlgorithmsGPU ComputingGPU ProgrammingHigh-Performance ComputingHomomorphic EncryptionLow-level OptimizationLow-level ProgrammingLow-level SystemsMemory ManagementParallel ProcessingPerformance Optimization
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