MANGO: Exploring Manycore Architectures for Next-GeneratiOn HPC Systems

Flich, José; Agosta, Giovanni; Ampletzer, Philipp; Alonso, David Atienza; Brandolese, Carlo; Cappe, Etienne; Cilardo, Alessandro; Dragić, Leon; Dray, Alexandre; Duspara, Alen; Fornaciari, William; Guillaume, Gerald; Hoornenborg, Ynse; Iranfar, Arman; Kovač, Mario; Libutti, Simone; Maitre, Bruno; Martínez, J.M.; Massari, Giuseppe; Mlinarić, Hrvoje; Papastefanakis, Ermis; Picornell, Tomas; Piljić, Igor; Pupykina, Anna; Reghenzani, Federico; Staub, Isabelle; Tornero, Rafael; Zapater, Marina; Zoni, Davide

doi:10.1109/dsd.2017.51

Cited by 13 publications

(10 citation statements)

References 16 publications

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“…The Antarex project [25], [26], focuses on energy efficient systems and in particular on producing a tool chain that tunes code to run efficiently on heterogeneous infrastructures. The Manago project [27] is equally similar to work presented in this paper but with a particular focus on time-predictability with trade-offs with power and energy efficiency. Dutot et al [28] advance upon power-capping and consider energy budgets with a principle focus on scheduling.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

Kavanagh

Djemame

Ejarque

et al. 2020

IEEE Trans. Sustain. Comput.

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Hardware in High Performance Computing environments in recent years have increasingly become more heterogeneous in order to improve computational performance. An additional aspect of such systems is the management of power and energy consumption. The increase in heterogeneity requires middleware and programming model abstractions to eliminate additional complexities that it brings, while also offering opportunities such as improved power management. In this paper we explore application level self-adaptation including aspects such as automated configuration and deployment of applications to different heterogeneous infrastructure and for their redeployment. This therefore not only mitigates complexities associated with heterogeneous devices but aims to take advantage of the heterogeneity. The overall result of this paper is a self-adaptive framework that manages application Quality of Service (QoS) at runtime, which includes the automatic migration of applications between different accelerated infrastructures. Discussion covers when this migration is appropriate and quantifies the likely benefits.

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Section: Related Workmentioning

confidence: 99%

Energy-Aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

Kavanagh

Djemame

Ejarque

et al. 2020

IEEE Trans. Sustain. Comput.

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“…MANGO platform [15,16] is a heterogeneous highperformance computing system consisting of generalpurpose compute nodes (GN) that are intertwined with heterogeneous acceleration nodes (HN), linked by an across-boundary homogeneous interconnect. GNs are based on CPUs (i.e.…”

Section: Platform -Mango Platformmentioning

confidence: 99%

Performance-efficient integration and programming approach of DCT accelerator for HEVC in MANGO platform

2019

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Video encoding based on novel HEVC standard is an extremely computationally expensive process and achieving efficient encoding requires intelligent utilization of all available resources, from both software and hardware perspective. Profiling and analysis of the encoding process identified Discrete cosine transform (DCT) as one of the key kernels that consume most of the time in the application's runtime. Therefore, high-throughput, fully-pipelined hardware accelerator was designed in FPGA and integrated into MANGO platform. MANGO platform is heterogeneous HPC system that consists of different types of nodes, from general purpose nodes (GN) to heterogeneous nodes (HN). While executing specific kernels on GN nodes is a straightforward process, executing kernels on accelerator-based HNs is a more complex procedure and requires specific integration to successfully exploit heterogeneous architecture. This paper presents performance-efficient integration of DCT hardware accelerator in MANGO platform, focusing on the performance of the encoder while maintaining coding efficiency and video quality of the encoded bitstream. Several approaches were considered, tested and compared; from the standalone integration where series of single tasks were offloaded to the DCT accelerator, to more complex solutions based on smart buffer utilization.

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“…The Antarex project [17], [18], focuses on energy efficient systems and in particular on producing a tool chain that tunes code to run efficiently on heterogeneous infrastructures. The Manago project [19] is equally similar to work presented in this paper but with a particular focus on time-predictability with trade-offs with power and energy efficiency. Dutot et al [20] advance upon power-capping and consider energy budgets with a principle focus on scheduling.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware Self-Adaptive Middleware for Heterogeneous Parallel Architectures

Kavanagh

Djemame

2018

2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT)

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Hardware in HPC environments in recent years has become ever more heterogeneous in order to improve computational performance and as an aspect of managing power and energy constraints. This increase in heterogeneity requires middleware abstractions to eliminate additional complexities that it brings. In this paper we present a self-adaptation framework which includes aspects such as automated configuration, deployment and redeployment of applications to different heterogeneous infrastructure. This therefore not only mitigates complexity but aims to take advantage of the existing heterogeneity. The overall result of this paper is a generic event driven self-adaptive system that manages application QoS at runtime, which includes the automatic migration of applications between different accelerated infrastructures. Discussion covers when this migration is appropriate and quantifies the likely benefits.

show abstract

MANGO: Exploring Manycore Architectures for Next-GeneratiOn HPC Systems

Cited by 13 publications

References 16 publications

Energy-Aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

Energy-Aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

Performance-efficient integration and programming approach of DCT accelerator for HEVC in MANGO platform

Energy-aware Self-Adaptive Middleware for Heterogeneous Parallel Architectures

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