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Memory system is critical to architecture design which can significantly impact application performance. Concurrent Average Memory Access Time (C-AMAT) is a model for analyzing and optimizing memory system performance using a recursive definition of the memory access latency along the memory hierarchy. The original C-AMAT model, however, does not provide the necessary granularity and flexibility for handling modern memory architectures with heterogeneous memory technologies and diverse system topology. We propose to augment C-AMAT to take into consideration the idiosyncrasies of individual cache/memory components as well as their topological arrangement in the memory architecture design. Through trace-based simulation, we validate the augmented model and examine the memory system performance with insight unavailable using the original C-AMAT model. 

Modern scientific workflows couple simulations with AI-powered analytics by frequently exchanging data to accelerate time-to-science to reduce the complexity of the simulation planes. However, this data exchange is limited in performance and portability due to a lack of support for scientific data formats in AI frameworks. We need a cohesive mechanism to effectively integrate at scale complex scientific data formats such as HDF5, PnetCDF, ADIOS2, GNCF, and Silo into popular AI frameworks such as TensorFlow, PyTorch, and Caffe. To this end, we designed Stimulus, a data management library for ingesting scientific data effectively into the popular AI frameworks. We utilize the StimOps functions along with StimPack abstraction to enable the integration of scientific data formats with any AI framework. The evaluations show that Stimulus outperforms several large-scale applications with different use-cases such as Cosmic Tagger (consuming HDF5 dataset in PyTorch), Distributed FFN (consuming HDF5 dataset in TensorFlow), and CosmoFlow (converting HDF5 into TFRecord and then consuming that in TensorFlow) by 5.3 x, 2.9 x, and 1.9 x respectively with ideal I/O scalability up to 768 GPUs on the Summit supercomputer. Through Stimulus, we can portably extend existing popular AI frameworks to cohesively support any complex scientific data format and efficiently scale the applications on large-scale supercomputers.