English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Like time complexity models that have significantly contributed to theanalysis and development of fast algorithms, energy complexity models forparallel algorithms are desired as crucial means to develop energy efficientalgorithms for ubiquitous multicore platforms. Ideal energy complexity modelsshould be validated on real multicore platforms and applicable to a wide rangeof parallel algorithms. However, existing energy complexity models for parallelalgorithms are either theoretical without model validation oralgorithm-specific without ability to analyze energy complexity for awide-range of parallel algorithms.  This paper presents a new general validated energy complexity model forparallel (multithreaded) algorithms. The new model abstracts away possiblemulticore platforms by their static and dynamic energy of computationaloperations and data access, and derives the energy complexity of a givenalgorithm from its work, span and I/O complexity. The new model is validated bydifferent sparse matrix vector multiplication (SpMV) algorithms and densematrix multiplication (matmul) algorithms running on high performance computing(HPC) platforms (e.g., Intel Xeon and Xeon Phi). The new energy complexitymodel is able to characterize and compare the energy consumption of SpMV andmatmul kernels according to three aspects: different algorithms, differentinput matrix types and different platforms. The prediction of the new modelregarding which algorithm consumes more energy with different inputs ondifferent platforms, is confirmed by the experimental results. In order toimprove the usability and accuracy of the new model for a wide range ofplatforms, the platform parameters of ICE model are provided for elevenplatforms including HPC, accelerator and embedded platforms.

ICE: A General and Validated Energy Complexity Model for Multithreaded Algorithms