# Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis

arXiv: https://arxiv.org/abs/2202.11518 · HTML: https://mlsanity.com/paper/2202.11518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Frequent Directions : Simple and Deterministic Matrix Sketching](https://mlsanity.com/paper/1501.01711/) (2015)
- [Memory Limited, Streaming PCA](https://mlsanity.com/paper/1307.0032/) (2013)
- [Invariances and Data Augmentation for Supervised Music Transcription](https://mlsanity.com/paper/1711.04845/) (2017)
- [Basic Performance Measurements of the Intel Optane DC Persistent Memory Module](https://mlsanity.com/paper/1903.05714/) (2019)
- [An Architecture for Memory Centric Active Storage (MCAS)](https://mlsanity.com/paper/2103.00007/) (2021)
- [Memory-efficient training with streaming dimensionality reduction](https://mlsanity.com/paper/2004.12041/) (2020)

## Abstract (arXiv)

Dimensionality reduction algorithms are standard tools in a researcher's toolbox. Dimensionality reduction algorithms are frequently used to augment downstream tasks such as machine learning, data science, and also are exploratory methods for understanding complex phenomena. For instance, dimensionality reduction is commonly used in Biology as well as Neuroscience to understand data collected from biological subjects. However, dimensionality reduction techniques are limited by the von-Neumann architectures that they execute on. Specifically, data intensive algorithms such as dimensionality reduction techniques often require fast, high capacity, persistent memory which historically hardware has been unable to provide at the same time. In this paper, we present a re-implementation of an existing dimensionality reduction technique called Geometric Multi-Scale Resolution Analysis (GMRA) which has been accelerated via novel persistent memory technology called Memory Centric Active Storage (MCAS). Our implementation uses a specialized version of MCAS called PyMM that provides native support for Python datatypes including NumPy arrays and PyTorch tensors. We compare our PyMM implementation against a DRAM implementation, and show that when data fits in DRAM, PyMM offers competitive runtimes. When data does not fit in DRAM, our PyMM implementation is still able to process the data.