Models for energy consumption of data structures and algorithms

EXCESS deliverable D2.1. More information at http://www.excess-project.eu/This deliverable reports our early energy models for data structures and algorithms based on both micro-benchmarks and concurrent algorithms. It reports the early results of Task 2.1 on investigating and modeling the trade-off between energy and performance in concurrent data structures and algorithms, which forms the basis for the whole work package 2 (WP2). The work has been conducted on the two main EXCESS platforms: (1) Intel platform with recent Intel multi-core CPUs and (2) Movidius embedded platform

White-box methodologies, programming abstractions and libraries

EXCESS deliverable D2.2. More information at http://www.excess-project.eu/This deliverable reports the results of white-box methodologies and early results ofthe first prototype of libraries and programming abstractions as available by projectmonth 18 by Work Package 2 (WP2). It reports i) the latest results of Task 2.2on white-box methodologies, programming abstractions and libraries for developingenergy-efficient data structures and algorithms and ii) the improved results of Task2.1 on investigating and modeling the trade-off between energy and performance ofconcurrent data structures and algorithms. The work has been conducted on two mainEXCESS platforms: Intel platforms with recent Intel multicore CPUs and MovidiusMyriad1 platform

Power models, energy models and libraries for energy-efficient concurrent data structures and algorithms

EXCESS deliverable D2.3. More information at http://www.excess-project.eu/This deliverable reports the results of the power models, energy models and librariesfor energy-efficient concurrent data structures and algorithms as available by projectmonth 30 of Work Package 2 (WP2). It reports i) the latest results of Task 2.2-2.4 onproviding programming abstractions and libraries for developing energy-efficient datastructures and algorithms and ii) the improved results of Task 2.1 on investigating andmodeling the trade-off between energy and performance of concurrent data structuresand algorithms. The work has been conducted on two main EXCESS platforms: Intelplatforms with recent Intel multicore CPUs and Movidius Myriad platforms

Efficient Self-tuning Spin-locks Using Competitive Analysis

Reactive spin-lock algorithms that can automatically adapt to contention variation on the lock have received great attention in the eld of multiprocessor synchronization since they can help applications achieve good performance in all possible contention conditions. However, in existing reactive spin-locks the reaction relies on (i) some fixed experimentally tuned thresholds, which may get frequently inappropriate in dynamic environments like multiprogramming/multiprocessor systems, or (ii) known probability distributions of inputs. This paper presents a new reactive spin-lock algorithm that is completely selftuning, which means no experimentally tuned parameter nor probability distribution of inputs are needed. The new spin-lock is based on both synchronization structures of applications and a competitive online algorithm. Our experiments, which use the Spark98 kernels and the SPLASH-2 applications as application benchmarks, on a multiprocessor machine SGI Origin2000 and an Intel Xeon workstation have showed that the new self-tuning spin-lock performs as well as the best of hand-tuning spin-lock representatives in a wide range of contention levels