# On the Impact of Memory Allocation on High-Performance Query Processing

arXiv: https://arxiv.org/abs/1905.01135 · HTML: https://mlsanity.com/paper/1905.01135/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2019.

## Built on by (most cited first, 3 of 3)

- [MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training](https://mlsanity.com/paper/2407.12117/) (2024)
- [Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs](https://mlsanity.com/paper/2509.18886/) (2025)
- [Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud](https://mlsanity.com/paper/2410.05930/) (2024)

## Abstract (arXiv)

Somewhat surprisingly, the behavior of analytical query engines is crucially affected by the dynamic memory allocator used. Memory allocators highly influence performance, scalability, memory efficiency and memory fairness to other processes. In this work, we provide the first comprehensive experimental analysis on the impact of memory allocation for high-performance query engines. We test five state-of-the-art dynamic memory allocators and discuss their strengths and weaknesses within our DBMS. The right allocator can increase the performance of TPC-DS (SF 100) by 2.7x on a 4-socket Intel Xeon server.