Efficient data-driven task allocation for future many-cluster on-chip systems
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F17%3A10237489" target="_blank" >RIV/61989100:27740/17:10237489 - isvavai.cz</a>
Výsledek na webu
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8035120" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8035120</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/HPCS.2017.81" target="_blank" >10.1109/HPCS.2017.81</a>
Alternativní jazyky
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.
Klasifikace
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)
Návaznosti výsledku
Projekt
<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
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
—