Energy efficiency will be important in future accelerator-based HPC systems for sustainability and to improve overall performance. This study proposes a deep neural network (DNN)-based learning model for execution time and power consumption of workloads ...

With the continuous improvement of on-chip integrated voltage regulators (IVRs) and fast, adaptive frequency control, dynamic voltage-frequency scaling (DVFS) transition times have shrunk from the microsecond to the nanosecond regime, providing immense ...

With the breakdown of Dennard’s scaling over the past decade, performance growth of modern microprocessor design has largely relied on scaling core count in chip multiprocessors (CMPs). The challenge of chip power density, however, remains and demands ...

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the task ...

We consider an N\times N Input-Queued (IQ) switch with a crossbar-based switching fabric implemented on a single chip. The power consumption produced by the crossbar chip, due to the data transfer, grows as N R^3, where R is the maximum bit rate. Thus, ...

Over the life of a modern supercomputer, the energy cost of running the system can exceed the cost of the original hardware purchase. This has driven the community to attempt to understand and minimize energy costs wherever possible. Towards these ends, ...

Predicting performance under Dynamic Voltage Frequency Scaling (DVFS) remains an open problem. Current best practice explores available performance counters to serve as input to linear regression models that predict performance. However, the ...

In this paper, we propose a novel online-learning algorithm for system-level power management. We formulate both dynamic power management (DPM) and dynamic voltage-frequency scaling problems as one of workload characterization and selection and solve ...

A generalized dynamic energy performance scaling (DEPS) framework is proposed for exploring application-specific energy-saving potential in hard real-time embedded systems. This software-centric framework focuses on system-wide energy reduction and ...

A feature exhibited by many modern computing systems is their ability to improve the quality of output they generate for a given input by spending more computing resources on processing it. Often this improvement comes at the price of degraded ...

This paper presents an extremely lightweight dynamic voltage and frequency scaling technique targeted towards modern multi-tasking systems. The technique utilizes processors runtime statistics and an online learning algorithm to estimate the best suited ...