Dynamic voltage and frequency scaling (DVFS) is
a well-known technique to reduce the power and/or energy
consumption of various applications. While most processors
provide chip-level DVFS, where the frequency and voltage of
the cores in a chip can only be changed all together; core-level
DVFS, where each core can be controlled independently, requires
core-level voltage regulators in hardware and only is supported
in production in Haswell generation among Intel processors. The
finer grained control that per-core DVFS provides can lead to
higher energy efficiency compared to chip-level DVFS especially
for the unsynchronized, unstructured parallel applications when
carefully applied.
Ability to do per-core DVFS opens up new doors for different
optimizations within runtime systems. We implement an intelligent
energy efficient runtime module which uses a fine-grained
function level per-core DVFS approach. Our module finds the
energy-optimal frequency for each phase/function/kernel of the
application over the first few iterations and applies the optimal
frequency for each function. We test our implementation on
Haswell processors and show that our algorithm enables 4%
to 35% energy reduction over chip-level DVFS with as much as
performance.
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This content will become publicly available on June 20, 2025
VESTA: Power Modeling with Language Runtime Events
Power modeling is an essential building block for computer systems in support of energy optimization, energy profiling, and energy-aware application development. We introduce VESTA, a novel approach to modeling the power consumption of applications with one key insight: language runtime events are often correlated with a sustained level of power consumption. When compared with the established approach of power modeling based on hardware performance counters (HPCs), VESTA has the benefit of solely requiring application-scoped information and enabling a higher level of explainability, while achieving comparable or even higher precision. Through experiments performed on 37 real-world applications on the Java Virtual Machine (JVM), we find the power model built by VESTA is capable of predicting energy consumption with a mean absolute percentage error of 1.56%, while the monitoring of language runtime events incurs small performance and energy overhead.
more » « less- PAR ID:
- 10532442
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 8
- Issue:
- PLDI
- ISSN:
- 2475-1421
- Page Range / eLocation ID:
- 621 to 646
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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