{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/21"}],"enrichment":{"capability":"OmniMalloc solves static memory allocation for neural networks by assigning buffer offsets to minimize peak memory usage, with an efficient C++ backend and multiple allocation algorithms including SuperMalloc.","skillfed_tags":["memory-optimization","ml-compiler","static-allocation"],"use_cases":["Optimize memory layout for ML model inference on embedded or accelerator devices with fixed memory budgets.","Plan static buffer allocation for neural network compilation pipelines to reduce peak memory footprint.","Benchmark and compare different allocation strategies on real workloads using the built-in harness.","Develop and prototype new allocation algorithms using the library's framework and visualization tools.","Pack multiple buffers with non-overlapping lifetimes into shared memory pools for resource-constrained environments."],"what_it_does":"OmniMalloc is a Python library that solves the static memory allocation problem: given a set of buffers with known sizes and lifetimes, it computes optimal offsets so that peak memory usage is minimized. This is a core step in ML compilers, embedded runtimes, and accelerator toolchains. The library wraps a C++ backend and exposes multiple allocation algorithms\u2014including SuperMalloc, a new allocator claimed to outperform open-source alternatives\u2014behind a single Python API.\n\nYou define allocations with size and lifetime intervals (half-open ranges where lifetimes that touch never conflict), then call allocate() with your choice of allocator strategy. The library returns a plan with assigned offsets for each buffer. It also ships benchmark harness and visualization tools to evaluate allocation strategies on real workloads.","worth_installing":"Yes, if you are building ML compilers, embedded runtimes, or accelerator toolchains that need to optimize static memory allocation. The library is actively maintained, has no known vulnerabilities, supports current Python versions (3.10\u20133.14), and offers both a practical allocator (SuperMalloc) and a framework for developing new strategies. Install friction is moderate due to compiled wheels, but pre-built binaries are available for common platforms. Not relevant for general Python development."},"id":"omnimalloc","links":{"html":"https://skillfed.io/packages/omnimalloc","md":"https://skillfed.io/packages/omnimalloc.md","pypi":"https://pypi.org/project/omnimalloc/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"omnimalloc","python_support":"supports_current","summary":"Your one-stop shop for static memory allocation."},"popularity":{"monthly_downloads":91315,"position":13524,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.0"}