$ show nv-legate/cupynumeric
cuPyNumeric is a GPU-accelerated, distributed NumPy-compatible array library built on the NVIDIA Legate framework. It provides a drop-in replacement for NumPy that scales across multiple GPUs and nodes, implementing a broad subset of NumPy's API including array creation, manipulation, linear algebra (Cholesky, QR, SVD, solve, eig), FFT, random number generation, sorting, statistics, and advanced indexing. The architecture uses a Python frontend with a C++ backend where each operation is a Legate task with CPU, OpenMP, and GPU variants. The library is explicitly end-of-life (final release v26.06.01) and recommends alternatives like cuPyNumeric's successor or direct use of Legate. Despite its deprecated status, the codebase demonstrates strong engineering with a clean task-based architecture, extensive integration tests, and reusable patterns such as a benchmarking harness, strategy-based indexing dispatch, and a settings framework. However, it suffers from many known NumPy incompatibilities (marked as xfail tests), incomplete features, and tight coupling to the Legate runtime.
The LLM identified 5 files worth highlighting for reuse:
A reusable benchmarking framework with configurable runs, warmup, size resolution, timer protocols, and output summarization. Includes MicrobenchmarkConfig for CLI argument parsing, SizeRequest for memory-target sizing, and Summarize for result reporting. Could be extracted and applied to any performance measurement project with minimal adaptation.
Implements a clean Strategy pattern for advanced indexing dispatch, with classes Basic, BoolMask, IntArraySingleAxis, and General. Each strategy encapsulates the execution logic for a specific indexing pattern, making it easy to add new strategies or optimize existing ones. The pattern is self-contained and could be reused in other array libraries or data processing frameworks.
Provides a robust settings system using PrioritizedSetting and EnvOnlySetting classes that support environment variable fallback, type conversion, and validation. Used to configure runtime options like bounds checking, doctor warnings, and NCCL flags. The pattern is generic and could be extracted for any Python project needing hierarchical configuration with environment variable overrides.
Implements FastDivmod and FastDivmodU64 templates for GPU-optimized integer division using multiplicative inverses. This is a low-level but highly reusable pattern for any CUDA code that requires efficient division operations. The implementation is self-contained and demonstrates non-obvious GPU optimization skill.
Provides a profiling_wrapper decorator that adds provenance tracking and profiling labels to array operations. The decorator pattern is cleanly separated from the core logic and could be reused in any Python library that needs to instrument function calls for performance analysis or debugging.