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.
@article{arxiv.1906.10037,
title = {Platform Independent Software Analysis for Near Memory Computing},
author = {Stefano Corda and Gagandeep Singh and Ahsan Javed Awan and Roel Jordans and Henk Corporaal},
journal= {arXiv preprint arXiv:1906.10037},
year = {2019}
}