# Platform Independent Software Analysis for Near Memory Computing

## TL;DR

This paper introduces PISA-NMC, a hardware-agnostic profiling tool that identifies applications suitable for near-memory computing by analyzing memory and parallelism metrics.

## Contribution

It extends existing profiling tools with new metrics relevant for NMC and demonstrates their effectiveness in selecting suitable applications.

## Key findings

- Metrics like memory entropy and spatial locality correlate with NMC performance
- Profiling can effectively identify applications benefiting from NMC
- The tool aids in guiding application development for NMC architectures

## Abstract

Near-memory Computing (NMC) promises improved performance for the applications that can exploit the features of emerging memory technologies such as 3D-stacked memory. However, it is not trivial to find such applications and specialized tools are needed to identify them. In this paper, we present PISA-NMC, which extends a state-of-the-art hardware agnostic profiling tool with metrics concerning memory and parallelism, which are relevant for NMC. The metrics include memory entropy, spatial locality, data-level, and basic-block-level parallelism. By profiling a set of representative applications and correlating the metrics with the application's performance on a simulated NMC system, we verify the importance of those metrics. Finally, we demonstrate which metrics are useful in identifying applications suitable for NMC architectures.