Energy-optimal configurations for High-Performance Computing applications: automated low-impact characterization and performance optimization of shared-memory applications

Energy consumption is key to enabling exascale High-performance Com-
puting (HPC). However, energy-optimized hardware and software combi-
nations could still be inefficient if the software operates poorly.
This work proposes a set of tools, models, and algorithms for energy
optimization aimed at high-performance computing based on knowledge
of the application and the specific hardware architecture. The main
contributions of this work are.
A framework called Parallel Scalability Suite (PaScal Suite) automati-
cally measures and compares multiple executions of a parallel application
according to various scenarios characterized by input size, number of
threads, cores, and frequencies. As a result, PascalSuite can automate
designing application models with an overhead of less than 1%.
An entire system energy model based on the CPU frequency and
the number of cores. The model aims to understand and optimize the
energy behavior of parallel applications in HPC systems according to
application parameters, such as the degree of parallelism, input load, and
CPU parameters related to dynamic and static power.
A methodology that combines measurement data with a heuristic
algorithm to provide insights into choosing the best phase divisions. Our
heuristic can reduce the scan space from 107000 to 102 with an average error
of 10% and up to 38% reduction in energy consumption using optimal
distribution compared to standard Linux DVFS.
A novel normalized time representation of the application characterizes
the application parameters and model, named application fingerprint.