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Position Paper: A Quantitative Approach for Guiding Data Management on Complex Memory Architectures
DescriptionRecent trends have led to an increased reliance on more diverse and heterogeneous device technologies to continue performance scaling. As a result, many supercomputers now include memory systems with multiple types of memory storage, each with different power, performance, and capacity characteristics. The community urgently needs new strategies to adapt mission critical applications to such complex memory architectures. In this talk, we will describe a quantitative approach that leverages lightweight application monitoring to derive and enforce effective runtime management for complex memory platforms, without requiring any developer effort or even recompilation of target programs. Additionally, we will present an evaluation that shows our approach can enable substantial performance benefits for a variety of memory intensive applications on real and complex memory hardware.
Extreme Scale Computing
HPC Community Collaboration
Machine Learning and Artificial Intelligence
Resource Management and Scheduling