zama-ai/tfhe-rs
Sep 2024 – Feb 2026
18 Months active
Languages Used
RustC++ShellYAMLPythonTOMLCUDAMakefile
Technical Skills
CryptographyGPU programmingRust programmingBuild SystemsC++CI/CD
Agnes Leroy
PROFILE
Agnes Leroy
Agnes Leroy engineered and maintained the GPU backend for the zama-ai/tfhe-rs repository, delivering robust cryptographic operations and scalable benchmarking infrastructure. She focused on memory management, multi-GPU orchestration, and API design, refactoring core CUDA and Rust components to improve reliability and performance for homomorphic encryption workloads. Agnes streamlined asynchronous and synchronous GPU APIs, enhanced CI pipelines for faster feedback, and implemented comprehensive memory tracking to support resource planning and test stability. Her work included debugging memory leaks, optimizing benchmarking for ERC20 and DEX scenarios, and aligning cryptographic logic with protocol specifications, demonstrating deep expertise in Rust, C++, and CUDA programming.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
254Total
Bugs
34
Commits
254
Features
57
Lines of code
122,758
Activity Months18
Your Network
76 peopleWork History
February 2026
17 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for zama-ai/tfhe-rs focusing on GPU robustness, CI/test workflow improvements, benchmarking enhancements, and code ownership/maintenance. Delivered targeted backend reliability improvements, expanded benchmarking coverage, and stabilized CI pipelines to reduce cycle time and increase confidence in GPU-accelerated cryptographic workloads.
17 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for zama-ai/tfhe-rs focusing on GPU robustness, CI/test workflow improvements, benchmarking enhancements, and code ownership/maintenance. Delivered targeted backend reliability improvements, expanded benchmarking coverage, and stabilized CI pipelines to reduce cycle time and increase confidence in GPU-accelerated cryptographic workloads.
February 2026
January 2026
9 Commits • 3 Features
Jan 1, 2026In January 2026, zama-ai/tfhe-rs delivered key GPU-focused enhancements and CI reliability improvements that strengthen multi-GPU workloads, expand benchmarking capabilities, and improve pipeline stability. Key outcomes include memory-management and safety hardening for CUDA operations, new GPU-based OPRF support across integer inputs, a negation operation for CUDA dedup benchmarking, and CI improvements to ensure reliable GPU tests.
January 2026
9 Commits • 3 Features
Jan 1, 2026In January 2026, zama-ai/tfhe-rs delivered key GPU-focused enhancements and CI reliability improvements that strengthen multi-GPU workloads, expand benchmarking capabilities, and improve pipeline stability. Key outcomes include memory-management and safety hardening for CUDA operations, new GPU-based OPRF support across integer inputs, a negation operation for CUDA dedup benchmarking, and CI improvements to ensure reliable GPU tests.
December 2025
10 Commits • 3 Features
Dec 1, 2025December 2025 performance summary for zama-ai/tfhe-rs. Focused on delivering robust GPU benchmarking, aligning critical transfer logic with litepaper specifications, and stabilizing the GPU test and memory subsystem to support faster, more reliable decision-making and product improvements.
10 Commits • 3 Features
Dec 1, 2025December 2025 performance summary for zama-ai/tfhe-rs. Focused on delivering robust GPU benchmarking, aligning critical transfer logic with litepaper specifications, and stabilizing the GPU test and memory subsystem to support faster, more reliable decision-making and product improvements.
December 2025
November 2025
9 Commits • 4 Features
Nov 1, 2025November 2025 (tfhe-rs): Key GPU backend improvements, expanded GPU testing, and automated validation delivering measurable business value. Implemented signed shift-right in the GPU decomposition algorithm for arithmetic correctness across data types; fixed a memory leak in the GPU rerandomization path; broadened GPU test coverage with classical PBS tests and expanded boolean/integer operation coverage; added a CI workflow to run GPU memory sanitization tests on H100 with remote setup and Slack notifications; improved GPU compression/decompression robustness with clearer error messages and better key management compatibility. These changes boost correctness, reliability, and developer productivity, enabling safer production deployments and more accurate pricing/viewing for users.
November 2025
9 Commits • 4 Features
Nov 1, 2025November 2025 (tfhe-rs): Key GPU backend improvements, expanded GPU testing, and automated validation delivering measurable business value. Implemented signed shift-right in the GPU decomposition algorithm for arithmetic correctness across data types; fixed a memory leak in the GPU rerandomization path; broadened GPU test coverage with classical PBS tests and expanded boolean/integer operation coverage; added a CI workflow to run GPU memory sanitization tests on H100 with remote setup and Slack notifications; improved GPU compression/decompression robustness with clearer error messages and better key management compatibility. These changes boost correctness, reliability, and developer productivity, enabling safer production deployments and more accurate pricing/viewing for users.
October 2025
16 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for zama-ai/tfhe-rs focused on stabilizing the GPU-backed TFHE workflow, simplifying the GPU API surface, and modernizing the codebase for maintainability and faster delivery. Key features delivered: - GPU API simplification: removed asynchronous variants and suffixes, replacing with synchronous counterparts, and streamlining function naming to reduce API surface area and future maintenance. This included internal renaming and removal of async entry points across radix operations, bitwise and arithmetic primitives, and AES paths. - Codebase modularization and build/docs improvements: modularized utilities, split large utility files, cleaned drift noise removal, updated CI/docs to reflect modernization, and adjusted build flags (e.g., disabling LTO in GPU benches) to improve maintainability and reducing build fragility. Major bugs fixed: - GPU synchronization and reliability fixes: addressed race conditions after kernel launches and during GPU memory releases across TFHE GPU backends; consolidated synchronization logic to improve stability and predictability. A perf regression introduced by prior async changes was addressed to restore performance consistency. Overall impact and accomplishments: - Increased stability and reliability of the TFHE GPU path, enabling safer production use and easier onboarding for downstream projects. - Reduced API complexity and maintenance burden through API consolidation and clearer naming, accelerating future feature work. - Improved build reliability and documentation, supporting faster cross-team collaboration and external contribution. Technologies/skills demonstrated: - GPU programming and synchronization, Rust-based codebase maintenance, API design and refactoring, build system optimization, CI/docs modernization, and performance debugging. Business value: - Lower incident risk and faster development cycles for GPU-backed TFHE features, with clearer APIs and better maintainability driving long-term product velocity and customer trust.
16 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for zama-ai/tfhe-rs focused on stabilizing the GPU-backed TFHE workflow, simplifying the GPU API surface, and modernizing the codebase for maintainability and faster delivery. Key features delivered: - GPU API simplification: removed asynchronous variants and suffixes, replacing with synchronous counterparts, and streamlining function naming to reduce API surface area and future maintenance. This included internal renaming and removal of async entry points across radix operations, bitwise and arithmetic primitives, and AES paths. - Codebase modularization and build/docs improvements: modularized utilities, split large utility files, cleaned drift noise removal, updated CI/docs to reflect modernization, and adjusted build flags (e.g., disabling LTO in GPU benches) to improve maintainability and reducing build fragility. Major bugs fixed: - GPU synchronization and reliability fixes: addressed race conditions after kernel launches and during GPU memory releases across TFHE GPU backends; consolidated synchronization logic to improve stability and predictability. A perf regression introduced by prior async changes was addressed to restore performance consistency. Overall impact and accomplishments: - Increased stability and reliability of the TFHE GPU path, enabling safer production use and easier onboarding for downstream projects. - Reduced API complexity and maintenance burden through API consolidation and clearer naming, accelerating future feature work. - Improved build reliability and documentation, supporting faster cross-team collaboration and external contribution. Technologies/skills demonstrated: - GPU programming and synchronization, Rust-based codebase maintenance, API design and refactoring, build system optimization, CI/docs modernization, and performance debugging. Business value: - Lower incident risk and faster development cycles for GPU-backed TFHE features, with clearer APIs and better maintainability driving long-term product velocity and customer trust.
October 2025
September 2025
13 Commits • 2 Features
Sep 1, 2025September 2025 Monthly Summary for zama-ai/tfhe-rs. Focused on stabilizing and accelerating the GPU backend for OPRF workloads and tightening GPU-related docs/configs. Delivered consolidated GPU Backend Reliability, Performance, and OPRF-related Improvements, plus Documentation and Configuration Maintenance for GPU. Key bugs fixed include memory leaks in multi-GPU calculations, missing broadcast LUT, and corrections to OPRF size and output degree computations. These changes improved multi-GPU throughput, memory safety, and GPU test/build stability. Technologies demonstrated include Rust-based GPU code, multi-GPU orchestration, memory management, code refactoring, and test/documentation automation. Business value: higher reliability and throughput for GPU-accelerated TFHE operations, enabling customers to run larger OPRF workloads with lower risk and faster feedback cycles.
September 2025
13 Commits • 2 Features
Sep 1, 2025September 2025 Monthly Summary for zama-ai/tfhe-rs. Focused on stabilizing and accelerating the GPU backend for OPRF workloads and tightening GPU-related docs/configs. Delivered consolidated GPU Backend Reliability, Performance, and OPRF-related Improvements, plus Documentation and Configuration Maintenance for GPU. Key bugs fixed include memory leaks in multi-GPU calculations, missing broadcast LUT, and corrections to OPRF size and output degree computations. These changes improved multi-GPU throughput, memory safety, and GPU test/build stability. Technologies demonstrated include Rust-based GPU code, multi-GPU orchestration, memory management, code refactoring, and test/documentation automation. Business value: higher reliability and throughput for GPU-accelerated TFHE operations, enabling customers to run larger OPRF workloads with lower risk and faster feedback cycles.
August 2025
1 Commits
Aug 1, 20252025-08 Monthly Summary for zama-ai/tfhe-rs: Focused on stabilizing GPU-related tests and reducing memory pressure to address OOMs on 4090 hardware. The primary deliverable was a targeted fix to the GPU test suite that reduces concurrency from 4 to 2 during integer GPU server key tests, eliminating OOM occurrences and improving CI stability. This work enhances test reliability, accelerates feedback cycles, and reinforces confidence in GPU-enabled builds moving forward. Technologies involved include Rust, GPU testing orchestration, memory footprint optimization, and CI pipeline practices.
1 Commits
Aug 1, 20252025-08 Monthly Summary for zama-ai/tfhe-rs: Focused on stabilizing GPU-related tests and reducing memory pressure to address OOMs on 4090 hardware. The primary deliverable was a targeted fix to the GPU test suite that reduces concurrency from 4 to 2 during integer GPU server key tests, eliminating OOM occurrences and improving CI stability. This work enhances test reliability, accelerates feedback cycles, and reinforces confidence in GPU-enabled builds moving forward. Technologies involved include Rust, GPU testing orchestration, memory footprint optimization, and CI pipeline practices.
August 2025
July 2025
16 Commits • 1 Features
Jul 1, 2025July 2025 highlights for zama-ai/tfhe-rs: Delivered targeted GPU and CI improvements to increase reliability, scalability, and measurable performance. Upgraded the CUDA backend for compatibility, enhanced benchmarking visibility, and tightened GPU resource management and data handling to reduce crashes and undefined behavior. Overall, the month focused on business value through stability, faster feedback loops, and clearer performance signals for GPU workloads.
July 2025
16 Commits • 1 Features
Jul 1, 2025July 2025 highlights for zama-ai/tfhe-rs: Delivered targeted GPU and CI improvements to increase reliability, scalability, and measurable performance. Upgraded the CUDA backend for compatibility, enhanced benchmarking visibility, and tightened GPU resource management and data handling to reduce crashes and undefined behavior. Overall, the month focused on business value through stability, faster feedback loops, and clearer performance signals for GPU workloads.
June 2025
19 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for zama-ai/tfhe-rs: Delivered substantial GPU-focused improvements across memory tracking, backend correctness, and CI, enabling more accurate resource planning, reliable GPU testing, and faster feedback loops for cryptographic workloads. Features include comprehensive GPU memory tracking and footprint estimation for cryptographic ops (div, neg, scalar div, eq/ne, booleans, RNG, compression/decompression, LUTs, and multi-GPU accounting) with tests and host/device sizing; backend correctness enhancements (degree propagation after bitwise ops, scalar bitwise handling, move_to_current_device for FheBool, and tuned noise squashing); and strengthened CI/test infrastructure with GPU fallback configurations and streamlined multi-GPU tests. Several critical bug fixes improved memory sizing accuracy, compression size calculation, and overall performance. Demonstrated proficiency in GPU resource management, Rust performance engineering, and CI automation.
19 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for zama-ai/tfhe-rs: Delivered substantial GPU-focused improvements across memory tracking, backend correctness, and CI, enabling more accurate resource planning, reliable GPU testing, and faster feedback loops for cryptographic workloads. Features include comprehensive GPU memory tracking and footprint estimation for cryptographic ops (div, neg, scalar div, eq/ne, booleans, RNG, compression/decompression, LUTs, and multi-GPU accounting) with tests and host/device sizing; backend correctness enhancements (degree propagation after bitwise ops, scalar bitwise handling, move_to_current_device for FheBool, and tuned noise squashing); and strengthened CI/test infrastructure with GPU fallback configurations and streamlined multi-GPU tests. Several critical bug fixes improved memory sizing accuracy, compression size calculation, and overall performance. Demonstrated proficiency in GPU resource management, Rust performance engineering, and CI automation.
June 2025
May 2025
18 Commits • 2 Features
May 1, 2025Month: 2025-05 — Performance-focused contributions in zama-ai/tfhe-rs centered on GPU memory footprint estimation, benchmarking enhancements, and stability improvements. Key features delivered: comprehensive GPU memory footprint tracking for arithmetic and logical operations (add/sub, bitwise, comparisons, shift/rotate, cmux, mul) to enable accurate memory usage estimation and allocation planning. Benchmarking enhancements include separate PBS metrics, parallelized benchmarks (dex bench and transfer bench), and expanded GPU hardware configurations for granular performance analysis. Major bugs fixed: GPU stability and CI/test reliability improvements, including OOM fixes in CI, overflow handling fixes, and correctness adjustments after mem-tracking changes, plus a multi-GPU selection capability. Overall impact: improved resource planning, more reliable GPU tests, and richer performance signals, enabling faster iteration cycles and better business decisions. Technologies/skills demonstrated: GPU memory instrumentation, parallel benchmarking, hardware configuration management for GPUs, and CI reliability improvements.
May 2025
18 Commits • 2 Features
May 1, 2025Month: 2025-05 — Performance-focused contributions in zama-ai/tfhe-rs centered on GPU memory footprint estimation, benchmarking enhancements, and stability improvements. Key features delivered: comprehensive GPU memory footprint tracking for arithmetic and logical operations (add/sub, bitwise, comparisons, shift/rotate, cmux, mul) to enable accurate memory usage estimation and allocation planning. Benchmarking enhancements include separate PBS metrics, parallelized benchmarks (dex bench and transfer bench), and expanded GPU hardware configurations for granular performance analysis. Major bugs fixed: GPU stability and CI/test reliability improvements, including OOM fixes in CI, overflow handling fixes, and correctness adjustments after mem-tracking changes, plus a multi-GPU selection capability. Overall impact: improved resource planning, more reliable GPU tests, and richer performance signals, enabling faster iteration cycles and better business decisions. Technologies/skills demonstrated: GPU memory instrumentation, parallel benchmarking, hardware configuration management for GPUs, and CI reliability improvements.
April 2025
16 Commits • 4 Features
Apr 1, 2025April 2025 performance summary for zama-ai/tfhe-rs: Delivered significant GPU benchmarking and CUDA backend improvements, stabilized GPU tests and CI workflows, and expanded benchmarking documentation and instrumentation. Key features include multi-arch CUDA build support, new scalar operations and leading-zero benchmarks, improved memory accounting for temporary buffers, and broader GPU operation coverage including DEX benchmarks. CI optimizations reduced resource usage and improved test stability across GPU tests, with bench workflows fixed and test thread counts tuned for smaller instances. Documentation updates clarify device selection, hardware references, and AWS CPU benchmarking details, while internal cleanup removed unused CUDA events and added explicit memory reporting for temporary buffers. These changes collectively improve benchmarking accuracy, reliability of GPU tests, and developer experience, driving clearer performance signals for business decisions and customer-facing benchmarks.
16 Commits • 4 Features
Apr 1, 2025April 2025 performance summary for zama-ai/tfhe-rs: Delivered significant GPU benchmarking and CUDA backend improvements, stabilized GPU tests and CI workflows, and expanded benchmarking documentation and instrumentation. Key features include multi-arch CUDA build support, new scalar operations and leading-zero benchmarks, improved memory accounting for temporary buffers, and broader GPU operation coverage including DEX benchmarks. CI optimizations reduced resource usage and improved test stability across GPU tests, with bench workflows fixed and test thread counts tuned for smaller instances. Documentation updates clarify device selection, hardware references, and AWS CPU benchmarking details, while internal cleanup removed unused CUDA events and added explicit memory reporting for temporary buffers. These changes collectively improve benchmarking accuracy, reliability of GPU tests, and developer experience, driving clearer performance signals for business decisions and customer-facing benchmarks.
April 2025
March 2025
32 Commits • 8 Features
Mar 1, 2025Monthly summary for 2025-03 focusing on delivering GPU-accelerated cryptographic tooling improvements, cost visibility, and reliability improvements in zama-ai/tfhe-rs. Key improvements include (1) refactoring GPU propagation to track noise and degree (including div tracking) for better noise budgeting and correctness; (2) adding C++ helpers to pop/push/insert in radix ciphertext to simplify and optimize data-paths; (3) implementing hourly cost tracking for sxm5 VMs in CI to improve cost visibility and budgeting; (4) advancing GPU performance and stability via memory transfer optimizations (passing host scalars, removing unnecessary memcpy_to_cpu, copy-slice adjustments) and introducing 128-bit GPU compression entry points, plus benchmark coverage and workflow fixes. These changes collectively improve performance, correctness, and cost transparency while reducing flakiness in GPU test runs.
March 2025
32 Commits • 8 Features
Mar 1, 2025Monthly summary for 2025-03 focusing on delivering GPU-accelerated cryptographic tooling improvements, cost visibility, and reliability improvements in zama-ai/tfhe-rs. Key improvements include (1) refactoring GPU propagation to track noise and degree (including div tracking) for better noise budgeting and correctness; (2) adding C++ helpers to pop/push/insert in radix ciphertext to simplify and optimize data-paths; (3) implementing hourly cost tracking for sxm5 VMs in CI to improve cost visibility and budgeting; (4) advancing GPU performance and stability via memory transfer optimizations (passing host scalars, removing unnecessary memcpy_to_cpu, copy-slice adjustments) and introducing 128-bit GPU compression entry points, plus benchmark coverage and workflow fixes. These changes collectively improve performance, correctness, and cost transparency while reducing flakiness in GPU test runs.
February 2025
29 Commits • 9 Features
Feb 1, 2025February 2025 (2025-02) — tfhe-rs GPU backend (zama-ai/tfhe-rs) delivered substantial feature work, critical bug fixes, and testing/stability improvements that enhance numerical accuracy, observability, and production readiness. Key features and changes improved correctness and maintainability across GPU paths; foundational support now exists for zero-initialized state handling of CUDA data structures and more observable noise/degree propagation through key arithmetic paths. CI/test automation was strengthened to support longer-running tests and more reliable validation cycles, reducing debugging time and accelerating release readiness.
29 Commits • 9 Features
Feb 1, 2025February 2025 (2025-02) — tfhe-rs GPU backend (zama-ai/tfhe-rs) delivered substantial feature work, critical bug fixes, and testing/stability improvements that enhance numerical accuracy, observability, and production readiness. Key features and changes improved correctness and maintainability across GPU paths; foundational support now exists for zero-initialized state handling of CUDA data structures and more observable noise/degree propagation through key arithmetic paths. CI/test automation was strengthened to support longer-running tests and more reliable validation cycles, reducing debugging time and accelerating release readiness.
February 2025
January 2025
10 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for zama-ai/tfhe-rs focused on performance, reliability, and developer productivity: CUDA backend Async API and enhanced data transfer unlocks asynchronous GPU operations; standardized multi-bit parameters across CPU/GPU for consistent performance and easier maintenance; CI/workflow improvements improve green builds and faster feedback; updated benchmark docs enable accurate performance-driven decisions across teams.
January 2025
10 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for zama-ai/tfhe-rs focused on performance, reliability, and developer productivity: CUDA backend Async API and enhanced data transfer unlocks asynchronous GPU operations; standardized multi-bit parameters across CPU/GPU for consistent performance and easier maintenance; CI/workflow improvements improve green builds and faster feedback; updated benchmark docs enable accurate performance-driven decisions across teams.
December 2024
19 Commits • 4 Features
Dec 1, 2024December 2024 — TFHE-RS (zama-ai/tfhe-rs): Delivered substantial GPU-focused improvements and backend reliability enhancements. Implemented GPU Benchmarking Enhancements with multi-GPU support and new VM target configurations, optimizing throughput and enabling broader hardware coverage. Consolidated and optimized the GPU backend core (memory management, comparisons, carries, LUT usage) to improve performance and correctness across CUDA/Rust backends. Released GPU documentation and practical usage examples, including 2D array usage with GpuFheUint32Array and ClearArray, to accelerate user adoption. Strengthened CI/validation with unique artifact naming and randomized long-run tests for CPU and GPU, increasing test coverage and reducing release risk.
19 Commits • 4 Features
Dec 1, 2024December 2024 — TFHE-RS (zama-ai/tfhe-rs): Delivered substantial GPU-focused improvements and backend reliability enhancements. Implemented GPU Benchmarking Enhancements with multi-GPU support and new VM target configurations, optimizing throughput and enabling broader hardware coverage. Consolidated and optimized the GPU backend core (memory management, comparisons, carries, LUT usage) to improve performance and correctness across CUDA/Rust backends. Released GPU documentation and practical usage examples, including 2D array usage with GpuFheUint32Array and ClearArray, to accelerate user adoption. Strengthened CI/validation with unique artifact naming and randomized long-run tests for CPU and GPU, increasing test coverage and reducing release risk.
December 2024
November 2024
13 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for zama-ai/tfhe-rs focused on scaling GPU-accelerated cryptography, improving CI/CD reliability for GPUBenchmarks, and expanding testing coverage. Key outcomes include robust multi-GPU CUDA backend, new GPU-centric APIs, enhanced testing and benchmarking workflows, and increased test coverage for PBS noise distribution. Business value: unlocked scalable GPU-accelerated cryptographic operations across multiple GPUs, delivering higher throughput and correctness; reduced build/test feedback time via optimized CI; broader validation coverage lowering production risk in crypto workflows. Notable context: work aligns with demands for high-throughput, GPU-centric cryptographic workloads and robust CI for GPU pipelines, enabling faster experiments and safer production releases.
November 2024
13 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for zama-ai/tfhe-rs focused on scaling GPU-accelerated cryptography, improving CI/CD reliability for GPUBenchmarks, and expanding testing coverage. Key outcomes include robust multi-GPU CUDA backend, new GPU-centric APIs, enhanced testing and benchmarking workflows, and increased test coverage for PBS noise distribution. Business value: unlocked scalable GPU-accelerated cryptographic operations across multiple GPUs, delivering higher throughput and correctness; reduced build/test feedback time via optimized CI; broader validation coverage lowering production risk in crypto workflows. Notable context: work aligns with demands for high-throughput, GPU-centric cryptographic workloads and robust CI for GPU pipelines, enabling faster experiments and safer production releases.
October 2024
6 Commits • 2 Features
Oct 1, 2024October 2024 (2024-10) — GPU-focused delivery for tfhe-rs: refined GPU CI/test infrastructure, a critical memory alignment fix for GPU PBS, and backend/build enhancements with broader hardware support. These changes reduce CI waste, improve stability of GPU-accelerated PBS operations, and simplify cross-language builds, enabling faster secure computation on GPUs.
6 Commits • 2 Features
Oct 1, 2024October 2024 (2024-10) — GPU-focused delivery for tfhe-rs: refined GPU CI/test infrastructure, a critical memory alignment fix for GPU PBS, and backend/build enhancements with broader hardware support. These changes reduce CI waste, improve stability of GPU-accelerated PBS operations, and simplify cross-language builds, enabling faster secure computation on GPUs.
October 2024
September 2024
1 Commits • 1 Features
Sep 1, 2024September 2024 monthly summary for zama-ai/tfhe-rs focusing on GPU readiness and code clarity enhancements. Implemented GPU TUniform parameter support and made TUniform parameters defaults, streamlined parameter handling by removing aliases, and updated test filtering to align with new parameters. These changes improve GPU-configurability, maintainability, and testing efficiency.
September 2024
1 Commits • 1 Features
Sep 1, 2024September 2024 monthly summary for zama-ai/tfhe-rs focusing on GPU readiness and code clarity enhancements. Implemented GPU TUniform parameter support and made TUniform parameters defaults, streamlined parameter handling by removing aliases, and updated test filtering to align with new parameters. These changes improve GPU-configurability, maintainability, and testing efficiency.
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Quality Metrics
Correctness90.4%
Maintainability88.4%
Architecture85.8%
Performance82.6%
AI Usage20.4%
Skills & Technologies
Programming Languages
BashCC++CMakeCUDACUDA C++JSONMakeMakefileMarkdown
Technical Skills
API DesignAPI DevelopmentAlgorithm designAsynchronous ProgrammingAutomationBackend DevelopmentBackend developmentBenchmarkingBug fixingBuild System ConfigurationBuild SystemsC++C++ DevelopmentC++ ProgrammingC++ development
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