{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/5"}],"enrichment":{"capability":"UMF is a library for constructing and managing memory allocators and pools, allowing isolation of different allocation types across multiple memory pools with distinct attributes and hardware resources.","skillfed_tags":["memory-management","allocator","heterogeneous-compute"],"use_cases":["Isolate GPU memory allocations (via Level Zero or CUDA providers) from CPU allocations in heterogeneous compute applications","Build custom memory pools with different performance characteristics (e.g., jemalloc-backed vs. OS-backed) for different workload phases","Manage pre-allocated fixed buffers for real-time or embedded systems where allocation patterns must be deterministic","Benchmark and profile memory allocation behavior using UMF's built-in micro-benchmarks and statistics APIs","Implement IPC (inter-process communication) with shared memory on Linux using the OS provider's shared memory mapping mode"],"what_it_does":"UMF is a memory management framework that lets you build custom allocators and manage multiple memory pools with different characteristics. It abstracts memory providers (which handle coarse-grained allocation from the OS or fixed buffers) and pool allocators (which handle fine-grained allocation and pooling), allowing you to isolate different allocation types and direct them to different hardware resources as needed.\n\nThe library ships as a compiled wheel and depends on tcmlib at runtime. It supports both Linux and Windows platforms and includes predefined providers (OS memory, fixed buffer) and allocators, as well as interfaces for writing custom ones. While the description emphasizes C/C++ usage with examples in those languages, the Python package metadata and wheel distribution suggest some Python-facing capability, though the extent and maturity of Python bindings are not clearly documented in the fact sheet.","worth_installing":"Yes, with conditions. UMF is actively maintained, permissively licensed, and has no known vulnerabilities. Install it if you need fine-grained control over memory allocation strategies across multiple pools or hardware resources. However, verify that Python bindings meet your needs first\u2014the package appears to be primarily a C/C++ library, and the extent of Python integration is unclear from the metadata. Medium install friction due to compiled dependencies is acceptable for the use case."},"id":"umf","links":{"html":"https://skillfed.io/packages/umf","md":"https://skillfed.io/packages/umf.md","pypi":"https://pypi.org/project/umf/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-24","license_spdx":null,"license_treatment":"permissive","name":"umf","python_support":"unspecified","summary":"Unified Memory Framework"},"popularity":{"monthly_downloads":586598,"position":5880,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}