# Memory and Parallelism Analysis Using a Platform-Independent Approach

## TL;DR

This paper presents a platform-independent analysis tool that incorporates new metrics to identify applications suitable for near-memory computing architectures, aiding in optimizing performance.

## Contribution

It extends existing analysis tools with NMC-specific metrics like memory entropy and parallelism, enabling better detection of applications for NMC architectures.

## Key findings

- Enhanced metrics for memory and parallelism analysis
- Improved identification of NMC-compatible applications
- Framework supports platform-independent application assessment

## Abstract

Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task. In this ongoing work, we extend the state-of-the-art platform-independent software analysis tool with NMC related metrics such as memory entropy, spatial locality, data-level, and basic-block-level parallelism. These metrics help to identify the applications more suitable for NMC architectures.