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.
@article{arxiv.1904.08762,
title = {Memory and Parallelism Analysis Using a Platform-Independent Approach},
author = {Stefano Corda and Gagandeep Singh and Ahsan Javed Awan and Roel Jordans and Henk Corporaal},
journal= {arXiv preprint arXiv:1904.08762},
year = {2019}
}
Comments
22nd ACM International Workshop on Software and Compilers for Embedded Systems (SCOPES '19), May 2019