Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis

TL;DR

This paper re-implements the GMRA dimensionality reduction technique using persistent memory technology (MCAS/PyMM), enabling efficient processing of large datasets beyond DRAM capacity, with competitive performance when data fits in memory.

Contribution

The paper introduces a novel implementation of GMRA accelerated by MCAS/PyMM, demonstrating its ability to handle larger datasets efficiently compared to traditional DRAM-based methods.

Findings

PyMM achieves competitive runtimes with DRAM when data fits in memory.

PyMM can process datasets larger than DRAM capacity.

Persistent memory enables scalable dimensionality reduction.

Abstract

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.