{"repo":"google/minimalloc","free":true,"listed":false,"github":"https://github.com/google/minimalloc","clone":"git clone https://github.com/google/minimalloc.git","description":"A lightweight memory allocator for hardware-accelerated machine learning","language":"C++","stars":192,"topics":["combinatorial-optimization","compiler","rectangle-packing","static-analysis","hardware-acceleration","machine-learning","memory-allocation","asplos","asplos23"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"Source code for our ASPLOS 2023 paper, \" MiniMalloc: A Lightweight Memory Allocator for Hardware-Accelerated Machine Learning .\" News MiniMalloc to be featured in the MIT PL Review 2025 on April 25th, 2025. Overview An increasing number of deep learning workloads are being supported by hardware acceleration . In order to unlock the maximum performance of a hardware accelerator, a machine learning model must first be carefully mapped onto its various internal components by way of a compiler . One especially important problem faced by a production-class compiler is that of memory allocation , whereby a set of buffers with predefined lifespans are mapped onto offsets in global memory. Since this allocation is performed statically, the compiler has the freedom to place buffers strategically, but must nevertheless wrestle with a combinatorial explosion in the number of assignment possibilities. MiniMalloc is a state-of-the-art algorithm designed specifically for static memory allocation that uses several novel search techniques in order to solve such problems efficiently and effectively. How it works A key insight motivating our methodology is the discovery of a specific category of solutions -- which we call canonical solutions -- that correspond to the members of an algebraic lattice: By limiting our exploration to the subset of canonical solutions, we can dramatically reduce the size of the search space while simultaneously ensuring that our algorithm remains sound and complete","default_branch":null,"files":null,"tree":[],"storefront":"/r/google","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/google/minimalloc/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}