zama-ai/tfhe-rs
Nov 2024 – Mar 2026
16 Months active
Languages Used
C++CUDARustMakefileCudaMarkdownCCMake
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 support multi-GPU execution, 128-bit programmable bootstrapping, and efficient compression routines, while integrating Rust bindings for seamless cross-language operation. His work addressed memory management, type safety, and benchmarking accuracy, enabling robust, high-throughput encrypted computations. By refactoring APIs, enhancing test coverage, and improving documentation, Pedro ensured maintainable, reliable code that supports complex cryptographic workloads. His technical depth in CUDA, C++, and Rust advanced both performance and reliability across the codebase.
Overall Statistics
Feature vs Bugs
58%Features
Repository Contributions
82Total
Bugs
16
Commits
82
Features
22
Lines of code
62,248
Activity Months16
Your Network
76 peopleWork History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) — tfhe-rs CUDA backend enhancements and benchmarking improvements. Key features delivered: updated tfhe-cuda-backend to 0.14.0 with removal of deprecated zkv1 GPU code path and API simplifications for the gpu_index parameter. Benchmarks were aligned to the benchmark_spec API and tests gained helper_profile support for improved profiling. Major bugs fixed: resolved compilation warnings in zk-cuda-backend and deprecated GPU code path removal, reducing maintenance burden and potential regressions. Overall impact: cleaner GPU surface, more reliable, observable benchmarks, and faster iteration with predictable performance for GPU-accelerated operations. Technologies/skills demonstrated: Rust, dependency management, GPU backend design, benchmarking tooling, profiling integration, and API maintainability.
2 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) — tfhe-rs CUDA backend enhancements and benchmarking improvements. Key features delivered: updated tfhe-cuda-backend to 0.14.0 with removal of deprecated zkv1 GPU code path and API simplifications for the gpu_index parameter. Benchmarks were aligned to the benchmark_spec API and tests gained helper_profile support for improved profiling. Major bugs fixed: resolved compilation warnings in zk-cuda-backend and deprecated GPU code path removal, reducing maintenance burden and potential regressions. Overall impact: cleaner GPU surface, more reliable, observable benchmarks, and faster iteration with predictable performance for GPU-accelerated operations. Technologies/skills demonstrated: Rust, dependency management, GPU backend design, benchmarking tooling, profiling integration, and API maintainability.
March 2026
February 2026
16 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for zama-ai/tfhe-rs focusing on GPU-accelerated cryptography backend and robustness improvements. Key features delivered: - GPU-accelerated cryptography backend and MSM acceleration: Introduced the zk-cuda-backend crate implementing GPU-accelerated MSM for BLS12-381 with CUDA kernels for finite field arithmetic (Fp, Fp2) and elliptic-curve operations, a Pippenger MSM implementation on GPU, Rust FFI bindings, comprehensive tests/benchmarks, and CI. API refactor to support MSM and CUDA backend. - Integration with tfhe-zk-pok: Integrated the zk-cuda-backend with tfhe-zk-pok to enable GPU-backed zk workflows across the TFHE ecosystem. Major bugs fixed: - CUDA LWE/GLWE processing safety and robustness: Strengthened validation with shape/dimension checks and bounding validations to prevent runtime errors, along with consistency checks across LWE/LWE representations and memory allocation adjustments (size_t safety) to avoid overflow. - Addressed several commit-level fixes to ensure correct processing paths in LWE/GLWE lists, compression metadata, and host–device data consistency. Overall impact and accomplishments: - Delivered a scalable GPU-accelerated path for MSM and related CUDA-accelerated cryptography, enabling higher throughput for zk schemes and improved performance of BLS12-381 operations. The work included API refactors and a strengthened safety framework, resulting in more maintainable code, safer CUDA processing, and better CI coverage. Technologies/skills demonstrated: - CUDA kernel development, PTX-level optimizations, GPU-accelerated MSM (Pippenger) on Rust, Rust FFI bindings, C++/CUDA interoperability, rigorous validation, CI automation, and cross-crate integration for zk workflows.
February 2026
16 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for zama-ai/tfhe-rs focusing on GPU-accelerated cryptography backend and robustness improvements. Key features delivered: - GPU-accelerated cryptography backend and MSM acceleration: Introduced the zk-cuda-backend crate implementing GPU-accelerated MSM for BLS12-381 with CUDA kernels for finite field arithmetic (Fp, Fp2) and elliptic-curve operations, a Pippenger MSM implementation on GPU, Rust FFI bindings, comprehensive tests/benchmarks, and CI. API refactor to support MSM and CUDA backend. - Integration with tfhe-zk-pok: Integrated the zk-cuda-backend with tfhe-zk-pok to enable GPU-backed zk workflows across the TFHE ecosystem. Major bugs fixed: - CUDA LWE/GLWE processing safety and robustness: Strengthened validation with shape/dimension checks and bounding validations to prevent runtime errors, along with consistency checks across LWE/LWE representations and memory allocation adjustments (size_t safety) to avoid overflow. - Addressed several commit-level fixes to ensure correct processing paths in LWE/GLWE lists, compression metadata, and host–device data consistency. Overall impact and accomplishments: - Delivered a scalable GPU-accelerated path for MSM and related CUDA-accelerated cryptography, enabling higher throughput for zk schemes and improved performance of BLS12-381 operations. The work included API refactors and a strengthened safety framework, resulting in more maintainable code, safer CUDA processing, and better CI coverage. Technologies/skills demonstrated: - CUDA kernel development, PTX-level optimizations, GPU-accelerated MSM (Pippenger) on Rust, Rust FFI bindings, C++/CUDA interoperability, rigorous validation, CI automation, and cross-crate integration for zk workflows.
January 2026
5 Commits • 1 Features
Jan 1, 2026January 2026 (2026-01) Monthly Summary for zama-ai/tfhe-rs. Focused on delivering business value by accelerating zero-knowledge proof generation and hardening the GPU-backed pipeline, while expanding test coverage and documentation to reduce risk and support future iterations.
5 Commits • 1 Features
Jan 1, 2026January 2026 (2026-01) Monthly Summary for zama-ai/tfhe-rs. Focused on delivering business value by accelerating zero-knowledge proof generation and hardening the GPU-backed pipeline, while expanding test coverage and documentation to reduce risk and support future iterations.
January 2026
November 2025
1 Commits
Nov 1, 2025November 2025 monthly summary for zama-ai/tfhe-rs: Focused on reliability and stability of LWE data compression in GPU workflows. Implemented a critical type-safety fix to prevent overflow when compressing large batches of LWE data by changing the type from int to uint32_t in the LWE compression path. This change reduces risk of data corruption and GPU-timeouts when processing large workloads, improving robustness and overall throughput of batch operations. The fix was applied in the tfhe-rs repository with commit 222a7e93c477cfbf12d658c18d9b750120e3fc4d.
November 2025
1 Commits
Nov 1, 2025November 2025 monthly summary for zama-ai/tfhe-rs: Focused on reliability and stability of LWE data compression in GPU workflows. Implemented a critical type-safety fix to prevent overflow when compressing large batches of LWE data by changing the type from int to uint32_t in the LWE compression path. This change reduces risk of data corruption and GPU-timeouts when processing large workloads, improving robustness and overall throughput of batch operations. The fix was applied in the tfhe-rs repository with commit 222a7e93c477cfbf12d658c18d9b750120e3fc4d.
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
Correctness92.6%
Maintainability86.8%
Architecture88.6%
Performance86.2%
AI Usage22.0%
Skills & Technologies
Programming Languages
CC++CMakeCUDACudaMakefileMarkdownRust
Technical Skills
API DesignAPI DevelopmentAlgorithm designBackend DevelopmentBenchmarkingC++C++ OverloadingC++ ProgrammingC++ Template MetaprogrammingC++ developmentC++ template metaprogrammingCUDACUDA Kernel OptimizationCUDA ProgrammingCUDA programming
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