Efficient data-driven task allocation for future many-cluster on-chip systems

Kód výsledku v IS VaVaI

RIV/61989100:27740/17:10237489 - isvavai.cz

Výsledek na webu

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8035120

DOI - Digital Object Identifier

10.1109/HPCS.2017.81

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient data-driven task allocation for future many-cluster on-chip systems

Popis výsledku v původním jazyce

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. © 2017 IEEE.

Název v anglickém jazyce

Efficient data-driven task allocation for future many-cluster on-chip systems

Popis výsledku anglicky

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. © 2017 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

LQ1602: IT4Innovations excellence in science

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Conference on High Performance Computing and Simulation, HPCS 2017 : proceedings

ISBN

978-1-5386-3250-5

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

503-510

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Ženeva

Datum konání akce

17. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—