Study of Raspberry Pi 2 quad-core Cortex-A7 CPU cluster as a mini supercomputer

  title={Study of Raspberry Pi 2 quad-core Cortex-A7 CPU cluster as a mini supercomputer},
  author={Abdurrachman Mappuji and Nazrul Effendy and Muhamad Mustaghfirin and Fandy Sondok and Rara Priska Yuniar and Sheptiani Putri Pangesti},
  journal={2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE)},
High performance computing (HPC) devices is no longer exclusive for academic, R&D, or military purposes. The use of HPC device such as supercomputer now growing rapidly as some new area arise such as big data, and computer simulation. It makes the use of supercomputer more inclusive. Today's supercomputer has a huge computing power, but requires an enormous amount of energy to operate. In contrast a single board computer (SBC), i.e., Raspberry Pi has minimum computing power, but requires a… 

Figures and Tables from this paper

HPC as a Service: A naïve model
An effort is made of bringing concepts from cloud computing to HPC in order to get benefits of cloud and the main target is to create a system which can develop a capability of providing computing power as a service.
Performance analysis of single board computer clusters
The survey on ARM processors for HPC
This work sees the opportunity to contribute to this subject by analyzing the state of the art to identify essential papers, highlighting important developments of ARM architecture in support to HPC, and discussing both positive and negative trends observed regarding the use of ARM for exascale computing.
Predictive Power Consumption Model for Compute Intensive Applications in Clustered ARM A53 Embedded Systems
A predictive power consumption model for compute intensive applications with a 5% correlation error from real power measurements is presented and projections exemplify how in the future, ARM based supercomputers will be a good alternative for reaching better power-performance capabilities.
Learning Cluster Computing by Creating a Raspberry Pi Cluster
This short paper describes a student's learning experience in cluster computing as part of the Texas Woman's University's Quality Enhancement project, where the student received funding to create a low-cost cluster computer using 5 Raspberry Pis.
Single Board Computers for Deep Machine Learning
The benefits of using multiple Raspberry Pi 3’s in a cluster to perform a Hyper Parameter Sweep to assist with design layouts, as an essential part of the design of a Deep Machine Learning System are shown.
На сьогодні суперкомп’ютери забезпечують високу швидкодію за допомогою паралельних обчислень великих об’ємів даних. Проте вони дуже дорогі, складні в розробці, і потребують дуже багато електроенергії


Design and Analysis of a 32-bit Embedded High-Performance Cluster Optimized for Energy and Performance
While the Raspberry Pi cluster lags recent x86 machines in performance, the power, visualization, and thermal features make it an excellent low-cost platform for education and experimentation.
Operating System Concepts
This best-selling book provides a solid theoretical foundation for understanding operating systems while giving the teacher and students the flexibility to choose the implementation system.
Message Passing Interface (MPI)
This paper describes the specification of the MPI and does an investigation about it.
Raspberry Pi 2 arrives with quad-core CPU, 1GB RAM, same $35 price
  • Ars Technica, 2015. [Online]. Available: [Accessed: 08- Apr-2016].
  • 2015
HPL Benchmark
  • Innovative Computing Laboratory University of Tennessee, 2016. [Online]. Available: [Accessed: 03-Jun-2016].
  • 2016
10 Best Raspberry Pi and Pi 2 Alternatives
  • Beebom, 2015. [Online]. Available: [Accessed: 08-Apr-2016].
  • 2015
Performance benchmarking a Raspberry Pi cluster [presentation]
Supercomputer ‘Titans’ Face Huge Energy Costs
  • 2012. [Online]. Available: [Accessed: 16-Oct- 2015].
  • 2012
Data Mining Improved Company’s Revenue By 187
  • Spotfire Blogging Team, 2011. [Online]. Available: [Accessed: 16-Oct-2015].
  • 2011