# DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

arXiv: https://arxiv.org/abs/2105.03725 · HTML: https://mlsanity.com/paper/2105.03725/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2021.

## Built on by (most cited first, 10 of 10)

- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud](https://mlsanity.com/paper/2209.08938/) (2022)
- [PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System](https://mlsanity.com/paper/2404.07164/) (2024)
- [Machine Learning Training on a Real Processing-in-Memory System](https://mlsanity.com/paper/2206.06022/) (2022)
- [An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System](https://mlsanity.com/paper/2207.07886/) (2022)
- [Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases](https://mlsanity.com/paper/2205.14664/) (2022)
- [TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems](https://mlsanity.com/paper/2304.01951/) (2023)
- [DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures](https://mlsanity.com/paper/2511.15503/) (2025)
- [DARTH-PUM: A Hybrid Processing-Using-Memory Architecture](https://mlsanity.com/paper/2602.16075/) (2026)
- [RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory](https://mlsanity.com/paper/2607.21731/) (2026)

## Abstract (arXiv)

Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data movement, spanning from traditional mechanisms (e.g., deep multi-level cache hierarchies, aggressive hardware prefetchers) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Our goal is to methodically identify potential sources of data movement over a broad set of applications and to comprehensively compare traditional compute-centric data movement mitigation techniques to more memory-centric techniques, thereby developing a rigorous understanding of the best techniques to mitigate each source of data movement. With this goal in mind, we perform the first large-scale characterization of a wide variety of applications, across a wide range of application domains, to identify fundamental program properties that lead to data movement to/from main memory. We develop the first systematic methodology to classify applications based on the sources contributing to data movement bottlenecks. From our large-scale characterization of 77K functions across 345 applications, we select 144 functions to form the first open-source benchmark suite (DAMOV) for main memory data movement studies. We select a diverse range of functions that (1) represent different types of data movement bottlenecks, and (2) come from a wide range of application domains. Using NDP as a case study, we identify new insights about the different data movement bottlenecks and use these insights to determine the most suitable data movement mitigation mechanism for a particular application. We open-source DAMOV and the complete source code for our new characterization methodology at https://github.com/CMU-SAFARI/DAMOV.