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We present ALEA, a tool to measure power and energy consumption at the granularity of basic blocks, using a probabilistic approach. ALEA provides fine-grained energy profiling via sta- tistical sampling, which overcomes the limitations of power sens- ing instruments. Compared to state-of-the-art energy measurement tools, ALEA provides finer granularity without sacrificing accuracy. ALEA achieves low overhead energy measurements with mean error rates between 1.4% and 3.5% in 14 sequential and paral- lel benchmarks tested on both Intel and ARM platforms. The sampling method caps execution time overhead at approximately 1%. ALEA is thus suitable for online energy monitoring and optimization. Finally, ALEA is a user-space tool with a portable, machine-independent sampling method. We demonstrate two use cases of ALEA, where we reduce the energy consumption of a k-means computational kernel by 37% and an ocean modelling code by 33%, compared to high-performance execution baselines, by varying the power optimization strategy between basic blocks.
|Title of host publication||Proceedings of the 24th International Conference on Parallel Architectures and Compilation Techniques (PACT)|
|Number of pages||20|
|Publication status||Published - Oct 2015|
|Event||The 24th International Conference on Parallel Architectures and Compilation Techniques - San Francisco, United States|
Duration: 18 Oct 2015 → 21 Oct 2015
|Conference||The 24th International Conference on Parallel Architectures and Compilation Techniques|
|Period||18/10/2015 → 21/10/2015|
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01/08/2013 → …
Nikolopoulos, D. & Karakonstantis, G.
01/08/2012 → 31/05/2016