Fast, Multicore-Scalable, Low-Fragmentation Memory Allocation through Large Virtual Memory and Global Data Structures

TL;DR

The paper introduces scalloc, a scalable, low-fragmentation memory allocator that efficiently manages memory in multicore environments using virtual spans and global data structures.

Contribution

It presents a novel concurrent allocator, scalloc, which outperforms existing allocators in scalability, memory usage, and performance through innovative design strategies.

Findings

scalloc outperforms other allocators in scalability and performance

scalloc uses less memory than comparable allocators

scalloc maintains competitive performance across various workloads

Abstract

We demonstrate that general-purpose memory allocation involving many threads on many cores can be done with high performance, multicore scalability, and low memory consumption. For this purpose, we have designed and implemented scalloc, a concurrent allocator that generally performs and scales in our experiments better than other allocators while using less memory, and is still competitive otherwise. The main ideas behind the design of scalloc are: uniform treatment of small and big objects through so-called virtual spans, efficiently and effectively reclaiming free memory through fast and scalable global data structures, and constant-time (modulo synchronization) allocation and deallocation operations that trade off memory reuse and spatial locality without being subject to false sharing.