Dissertação de Mestrado em Processamento Paralelo e Distribuido

Contato

Publicado em: 25/10/2013

UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL

INSTITUTO DE INFORMÁTICA

PROGRAMA DE PÓS-GRADUAÇÃO EM COMPUTAÇÃO

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DEFESA DE DISSERTAÇÃO DE MESTRADO

Aluno: Daniel Alfonso Gonçalves de Oliveira

Orientador: Prof. Dr. Philippe Olivier Alexandre Navaux

Título: Energy Consumption and Performance of HPC architectures for Exascale

Linha de Pesquisa: Processamento Paralelo e Distribuído

Data: 29/10/2013

Hora: 14h

Local: Sala 114. Prédio 43425 – Instituto de Informática

Banca Examinadora:

Prof. Dr. Avelino Francisco Zorzo (PUCRS)

Prof. Dr. Lucas Mello Schnorr (UFRGS)

Prof. Dr. Paolo Rech (UFRGS)

Presidente da Banca: Prof. Dr. Philippe Olivier Alexandre Navaux

Resumo:

One of the main concerns to build the new generation of High Performance Computing (HPC) systems is energy consumption. To break the exascale barrier, the scientific community needs to investigate alternatives that cope with energy consumption.

Current HPC systems are power hungry and are already consuming Megawatts of energy. Future exascale systems will be strongly constrained by their energy consumption requirements. Therefore, general purpose high power processors could be replaced by new architectures in HPC design.

Two architectures emerge in the HPC context. The first architecture uses Graphic Processing Units (GPU). GPUs have many processing cores, supporting simultaneous execution of thousands of threads, adapting well to massively parallel applications. Today, top ranked HPC systems feature many GPUs, which present high processing speed at low energy consumption budget with various parallel applications. The second architecture uses Low Power Processors, such as ARM processors. They are improving the performance, while still aiming to keep the power consumption as low as possible. As an example of this performance gain, projects like Mont-Blanc bet on ARM to build energy efficient HPC systems.

This work aims to verify the potential of these emerging architectures. We evaluate the these architectures and compare them to the current most common HPC architecture, high power processors such as Intel. The main goal is to analyze the energy consumption and performance of these architectures in the HPC context. Therefore, heterogeneous HPC benchmarks were executed in the architectures. The results show that the GPU architecture is the fastest and the best in terms of energy efficiency. GPUs were at least 5 times faster while consuming 18 times less energy for all benchmarks tested. We also observed that high power processors are faster than low power processors and consume less energy for heavy-weight workloads. However, for light-weight workloads, low power processors presented a better energy efficiency.

We conclude that heterogeneous systems combining GPUs and low power processors can be an interesting solution to achieve greater energy efficiency, although low power processors presented a worse energy efficiency for HPC workloads. Their extremely low power consumption during the processing of an application is less than the idle power of the other architectures. Therefore, combining low power processors with GPUs could result in an overall energy efficiency greater than high power processors combined with GPUs.

Palavras-chave: HPC, Exascale, ARM processors, GPU Accelerators, Energy Consumption, Performance

UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL
INSTITUTO DE INFORMÁTICA
PROGRAMA DE PÓS-GRADUAÇÃO EM COMPUTAÇÃO
———————————————————
DEFESA DE DISSERTAÇÃO DE MESTRADO

Aluno: Daniel Alfonso Gonçalves de Oliveira
Orientador: Prof. Dr. Philippe Olivier Alexandre Navaux
Título: Energy Consumption and Performance of HPC architectures for Exascale
Linha de Pesquisa: Processamento Paralelo e Distribuído

Data: 29/10/2013
Hora: 14h
Local: Sala 114. Prédio 43425 – Instituto de Informática

Banca Examinadora:
Prof. Dr. Avelino Francisco Zorzo (PUCRS)
Prof. Dr. Lucas Mello Schnorr (UFRGS)
Prof. Dr. Paolo Rech (UFRGS)

Presidente da Banca: Prof. Dr. Philippe Olivier Alexandre Navaux

Resumo:One of the main concerns to build the new generation of High Performance Computing (HPC) systems is energy consumption. To break the exascale barrier, the scientific community needs to investigate alternatives that cope with energy consumption.
Current HPC systems are power hungry and are already consuming Megawatts of energy. Future exascale systems will be strongly constrained by their energy consumption requirements. Therefore, general purpose high power processors could be replaced by new architectures in HPC design.
Two architectures emerge in the HPC context. The first architecture uses Graphic Processing Units (GPU). GPUs have many processing cores, supporting simultaneous execution of thousands of threads, adapting well to massively parallel applications. Today, top ranked HPC systems feature many GPUs, which present high processing speed at low energy consumption budget with various parallel applications. The second architecture uses Low Power Processors, such as ARM processors. They are improving the performance, while still aiming to keep the power consumption as low as possible. As an example of this performance gain, projects like Mont-Blanc bet on ARM to build energy efficient HPC systems.
This work aims to verify the potential of these emerging architectures. We evaluate the these architectures and compare them to the current most common HPC architecture, high power processors such as Intel. The main goal is to analyze the energy consumption and performance of these architectures in the HPC context. Therefore, heterogeneous HPC benchmarks were executed in the architectures. The results show that the GPU architecture is the fastest and the best in terms of energy efficiency. GPUs were at least 5 times faster while consuming 18 times less energy for all benchmarks tested. We also observed that high power processors are faster than low power processors and consume less energy for heavy-weight workloads. However, for light-weight workloads, low power processors presented a better energy efficiency.
We conclude that heterogeneous systems combining GPUs and low power processors can be an interesting solution to achieve greater energy efficiency, although low power processors presented a worse energy efficiency for HPC workloads. Their extremely low power consumption during the processing of an application is less than the idle power of the other architectures. Therefore, combining low power processors with GPUs could result in an overall energy efficiency greater than high power processors combined with GPUs.

Palavras-chave: HPC, Exascale, ARM processors, GPU Accelerators, Energy Consumption, Performance