Efficient Data-driven Task Allocation for Future Many-cluster On-chip Systems

Alberto Scionti, Somnath Mazumdar, Antoni Portero

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Continuous demand for higher performance is adding more pressure on hardware designers to provide faster machines with low energy consumption. Recent technological advancements allow placing a group of silicon dies on top of a conventional interposer (silicon layer), which provides space to integrate logic and interconnection resources to manage active processing cores. However, such large resource availability requires an adequate Program eXecution Model (PXM) as well as an efficient mechanism to allocate resources in the system. From this perspective, fine-grain data-driven PXMs represent an attractive solution to reduce the cost of synchronising concurrent activities. The contribution of this work is twofold. First, a hardware architecture called TALHES - a Task ALlocator for HEterogeneous System is proposed to support scheduling of multi-threaded applications (adhering to an explicit data-driven PXM). TALHES introduces a Network-on-Chip (NoC) extension: i) while on-chip 2D-mesh NoCs are used to support locality of computations in the execution of a single task; ii) a global task scheduler integrated into the silicon interposer orchestrates application tasks among different clusters of cores (eventually with different computing capabilities). The second contribution of the paper is a simulation framework that is tailored to support the analysis of such fine-grain data-driven applications. In this work, Linux Containers are used to abstract and efficiently simulate clusters of cores (i.e., a single die), as well as the behaviour of the global scheduling unit.
Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017
EditorsWaleed W. Smari
Number of pages8
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date12 Sep 2017
Pages503-510
Article number8035120
ISBN (Electronic)9781538632505
DOIs
Publication statusPublished - 12 Sep 2017
Externally publishedYes
Event15th International Conference on High Performance Computing and Simulation, HPCS 2017 - Genoa, Italy
Duration: 17 Jul 201721 Jul 2017
Conference number: 15

Conference

Conference15th International Conference on High Performance Computing and Simulation, HPCS 2017
Number15
Country/TerritoryItaly
CityGenoa
Period17/07/201721/07/2017
SeriesProceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017

Keywords

  • Clusters
  • Dataflow
  • Manycore processors
  • Network-on-Chip
  • Simulation
  • Task-scheduling

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