Low Power High-Performance Computing on the BeagleBoard Platform
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The document discusses the development and evaluation of a low-power, high-performance computing cluster based on the Beagleboard platform, highlighting its energy efficiency compared to traditional laptops. It describes the Beaglecluster's hardware and software setups, various experiments conducted (including benchmarks for high-performance Linpack and matrix multiplication), and the analysis of power consumption. The results indicate that the Beaglecluster is more energy-efficient than laptops when communication overhead is minimized, and the research suggests future enhancements for improved efficiency and functionalities.
![Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments
Introduction
High-performance computing clusters are employed in computation-
ally intensive tasks (e.g., weather prediction, astronomical mod-
elling).
Usually, they are evaluated only in terms of Floating Point Opera-
tions Per Second (FLOPS) (e.g., Top500 list).
The costs of energy and infrastructure exceed the costs of the
computational devices, and this gap is expected to grow by 2014
[Belady, 2007].
A new metric
FLOPS/Watt
3 / 25](https://image.slidesharecdn.com/slides-130618083836-phpapp02/85/Low-Power-High-Performance-Computing-on-the-BeagleBoard-Platform-3-320.jpg)
![Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments
Introduction
High-performance computing clusters are employed in computation-
ally intensive tasks (e.g., weather prediction, astronomical mod-
elling).
Usually, they are evaluated only in terms of Floating Point Opera-
tions Per Second (FLOPS) (e.g., Top500 list).
The costs of energy and infrastructure exceed the costs of the
computational devices, and this gap is expected to grow by 2014
[Belady, 2007].
A new metric
FLOPS/Watt
3 / 25](https://image.slidesharecdn.com/slides-130618083836-phpapp02/85/Low-Power-High-Performance-Computing-on-the-BeagleBoard-Platform-4-320.jpg)
![Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments
Introduction
High-performance computing clusters are employed in computation-
ally intensive tasks (e.g., weather prediction, astronomical mod-
elling).
Usually, they are evaluated only in terms of Floating Point Opera-
tions Per Second (FLOPS) (e.g., Top500 list).
The costs of energy and infrastructure exceed the costs of the
computational devices, and this gap is expected to grow by 2014
[Belady, 2007].
A new metric
FLOPS/Watt
3 / 25](https://image.slidesharecdn.com/slides-130618083836-phpapp02/85/Low-Power-High-Performance-Computing-on-the-BeagleBoard-Platform-5-320.jpg)
![Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments
Introduction
High-performance computing clusters are employed in computation-
ally intensive tasks (e.g., weather prediction, astronomical mod-
elling).
Usually, they are evaluated only in terms of Floating Point Opera-
tions Per Second (FLOPS) (e.g., Top500 list).
The costs of energy and infrastructure exceed the costs of the
computational devices, and this gap is expected to grow by 2014
[Belady, 2007].
A new metric
FLOPS/Watt
3 / 25](https://image.slidesharecdn.com/slides-130618083836-phpapp02/85/Low-Power-High-Performance-Computing-on-the-BeagleBoard-Platform-6-320.jpg)
![Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments
Speaker Diarization
Speaker Diarization
• A speaker diarization algorithm detects “who speaks now”.
• The algorithm here addressed is based on the real-time
implementation described in [Colagiacomo, et al. 2010].
• The calculation of the cross-correlations between the channel
i signal xi(t) and the channel j signal xj(t) is the most
computational demanding part:
Cij(t) = max
τ
{IFFT[FFT(xi(t)xj(t − τ)) • FFT(w(t))]} .
Here, t is the time index, τ is the correlation lag, w(t) is the
Hamming window and • denotes the element-wise product.
13 / 25](https://image.slidesharecdn.com/slides-130618083836-phpapp02/85/Low-Power-High-Performance-Computing-on-the-BeagleBoard-Platform-22-320.jpg)
![H. W. Meuer, “The TOP500 Project: Looking Back Over 15 Years of
Supercomputing Experience,” Informatik-Spektrum, vol. 31, no. 3, pp. 203–222,
2008. [Online]. Available: http://www.top500.org
C. L. Belady, “In the Data Center, Power and Cooling Cost More Than the IT
Equipment It Supports,” Electronics Cooling Magazine, vol. 13, no. 1, May 2007.
W.-c. Feng and K. Cameron, “The Green500 List: Encouraging Sustainable
Supercomputing,” IEEE Computer, vol. 40, no. 12, pp. 50–55, Dec. 2007.
[Online]. Available: http://www.green500.org
I. Ahmad and S. Ranka, Eds., Handbook of Energy-Aware and Green Computing,
1st ed., ser. Information Science. Boca Raton, US: CRC Press, Jan. 2012.
S. Andrade, J. Dourado, and C. Maciel, “Low-power cluster using OMAP3530,”
in Proc. of EDERC, Nice, France, Dec. 2010, pp. 220–224.
K. F¨urlinger, C. Klausecker, and D. Kranzlm¨uller, “Towards energy efficient
parallel computing on consumer electronic devices,” in Proc. of ICT-GLOW.
Berlin, Heidelberg: Springer-Verlag, 2011, pp. 1–9.
M. Brim, R. Flanery, A. Geist, B. Luethke, and S. L. Scott, “Cluster Command
and Control (C3) Tool Suite,” Parallel and Distributed Computing Practices,
vol. 4, no. 4, Dec. 2001.
25 / 25](https://image.slidesharecdn.com/slides-130618083836-phpapp02/85/Low-Power-High-Performance-Computing-on-the-BeagleBoard-Platform-35-320.jpg)
Low Power High-Performance Computing on the BeagleBoard Platform
- 1. Low Power High-Performance Computing on the BeagleBoard Platform E. Principi, V. Colagiacomo, S. Squartini, and F. Piazza A3Lab, Department of Information Engineering Universit`a Politecnica delle Marche 5th European DSP Education and Research Conference 13th and 14th September, 2012, Amsterdam, Netherlands
- 2. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Outline 1 Introduction 2 Purpose of this work 3 The BeagleCluster Hardware Platform Software Platform 4 Experiments High-Performance Linpack Matrix Multiplication Speaker Diarization Analysis of power consumption 5 Conclusions and Future Developments 2 / 25
- 3. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Introduction High-performance computing clusters are employed in computation- ally intensive tasks (e.g., weather prediction, astronomical mod- elling). Usually, they are evaluated only in terms of Floating Point Opera- tions Per Second (FLOPS) (e.g., Top500 list). The costs of energy and infrastructure exceed the costs of the computational devices, and this gap is expected to grow by 2014 [Belady, 2007]. A new metric FLOPS/Watt 3 / 25
- 4. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Introduction High-performance computing clusters are employed in computation- ally intensive tasks (e.g., weather prediction, astronomical mod- elling). Usually, they are evaluated only in terms of Floating Point Opera- tions Per Second (FLOPS) (e.g., Top500 list). The costs of energy and infrastructure exceed the costs of the computational devices, and this gap is expected to grow by 2014 [Belady, 2007]. A new metric FLOPS/Watt 3 / 25
- 5. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Introduction High-performance computing clusters are employed in computation- ally intensive tasks (e.g., weather prediction, astronomical mod- elling). Usually, they are evaluated only in terms of Floating Point Opera- tions Per Second (FLOPS) (e.g., Top500 list). The costs of energy and infrastructure exceed the costs of the computational devices, and this gap is expected to grow by 2014 [Belady, 2007]. A new metric FLOPS/Watt 3 / 25
- 6. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Introduction High-performance computing clusters are employed in computation- ally intensive tasks (e.g., weather prediction, astronomical mod- elling). Usually, they are evaluated only in terms of Floating Point Opera- tions Per Second (FLOPS) (e.g., Top500 list). The costs of energy and infrastructure exceed the costs of the computational devices, and this gap is expected to grow by 2014 [Belady, 2007]. A new metric FLOPS/Watt 3 / 25
- 7. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Tendency in the industry • Use of processors traditionally employed in the mobile world. • Canonical built a 42-core ARM cluster for compiling the Ubuntu distribution. • Calxeda developed the EnergyCore ECX-1000 series of server-on-a-chip based on ARM Cortex-A9. 4 / 25
- 8. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Tendency in the industry • Use of processors traditionally employed in the mobile world. • Canonical built a 42-core ARM cluster for compiling the Ubuntu distribution. • Calxeda developed the EnergyCore ECX-1000 series of server-on-a-chip based on ARM Cortex-A9. 4 / 25
- 9. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Tendency in the industry • Use of processors traditionally employed in the mobile world. • Canonical built a 42-core ARM cluster for compiling the Ubuntu distribution. • Calxeda developed the EnergyCore ECX-1000 series of server-on-a-chip based on ARM Cortex-A9. • Hewlett-Packard Redstone servers • Four rack chassis = 2800 conventional servers • Energy saving: 90% • Space saving: 94% • Currently employed in TryStack free cloud service (http://trystack.org) 4 / 25
- 10. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Purpose of this work Develop Develop an energy efficient cluster computer composed of off-the- shelf inexpensive hardware and open software and propose it to the scientific community. Evaluate Evaluate the cluster both through conventional benchmarks and a real-time constrained speech processing application. Measure Measure the power consumption of the cluster, assess the energy efficiency, and compare it with a laptop PC. 5 / 25
- 11. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Purpose of this work Develop Develop an energy efficient cluster computer composed of off-the- shelf inexpensive hardware and open software and propose it to the scientific community. Evaluate Evaluate the cluster both through conventional benchmarks and a real-time constrained speech processing application. Measure Measure the power consumption of the cluster, assess the energy efficiency, and compare it with a laptop PC. 5 / 25
- 12. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Purpose of this work Develop Develop an energy efficient cluster computer composed of off-the- shelf inexpensive hardware and open software and propose it to the scientific community. Evaluate Evaluate the cluster both through conventional benchmarks and a real-time constrained speech processing application. Measure Measure the power consumption of the cluster, assess the energy efficiency, and compare it with a laptop PC. 5 / 25
- 13. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Hardware Platform Cluster description The BeagleCluster is composed of five BeagleBoard-xM. Beagleboard-xM Processor TI DM3730 ARM subsystem Cortex-A8 @ 1 GHz DSP subsystem C64x+ @ 800 MHz Graphics accelerator PowerVR SGX @ 200 MHz RAM 512 MB DDR @ 200 MHz Network interface Ethernet 10/100 6 / 25
- 14. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Hardware Platform Cluster description (cont.) • Asymmetric topology: one head node, four worker nodes. • Nodes are connected to a Hewlett-Packard ProCurve 1410-8G switch through the BeagleBoard-xM 100 Mbit interface. • Nodes are powered by a Lambda AC-DC power supply. 7 / 25
- 15. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Software Platform Software Platform • Operating system: ˚Angstr¨om GNU/Linux distribution (worker nodes do not have a GUI). • Tool-chain: CodeSourcery. • Network File System: data and code are shared throughout the cluster using Network File System. • Cluster Command Control: a suite of tools for managing the cluster (e.g., terminating processes, rebooting worker nodes, pushing drive images). • Message Passing Interface (Argonne National Laboratory MPICH2): application programming interface that allows the exchange of messages and data among processes running on the nodes of a cluster. 8 / 25
- 16. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Software Platform Software Platform (cont.) • Ganglia: offers a web interface used to monitor the cluster activity and to detect abnormal functioning. 9 / 25
- 17. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments High-Performance Linpack High-Performance Linpack (HPL) • HPL is the de-facto standard benchmark for floating point performance measurement. • It is employed in the Top500 and Green500 lists. • HPL solves a dense system of linear equations using double precision arithmetic. • Parallelism is obtained by means of MPI. • Computation is based on BLAS (Vesperix ATLAS-ARM). 10 / 25
- 18. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments High-Performance Linpack High-Performance Linpack (HPL) (cont.) MFLOPS 258.6 MFLOPS/W 13.26 Green500 500th position (June 2012) Cray XT5 SixCore, Opteron Six Core 6C 2.6 GHz, XT4 Internal Interconnect: 32.05 MFLOPS/W Note Arithmetic operations are performed in double precision in the Vector Floating Point unit: NEON unit cannot be employed. 11 / 25
- 19. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments High-Performance Linpack High-Performance Linpack (HPL) (cont.) MFLOPS 258.6 MFLOPS/W 13.26 Green500 500th position (June 2012) Cray XT5 SixCore, Opteron Six Core 6C 2.6 GHz, XT4 Internal Interconnect: 32.05 MFLOPS/W Note Arithmetic operations are performed in double precision in the Vector Floating Point unit: NEON unit cannot be employed. 11 / 25
- 20. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Matrix Multiplication Matrix Multiplication • This benchmark shows the performance improvement that can be obtained using NEON optimized code. • The benchmark multiplies an m × n matrix A with an n × p matrix B. • It operates dividing the rows of matrix A in groups, and processing each group in a different node. • Communication among nodes is based on MPI. Platform Execution time BeagleCluster 42.13 s BeagleCluster w/ NEON 5.18 s NEON optimized code significantly reduces the execution time ⇒ HPL performance can be improved by properly exploiting NEON 12 / 25
- 21. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Matrix Multiplication Matrix Multiplication • This benchmark shows the performance improvement that can be obtained using NEON optimized code. • The benchmark multiplies an m × n matrix A with an n × p matrix B. • It operates dividing the rows of matrix A in groups, and processing each group in a different node. • Communication among nodes is based on MPI. Platform Execution time BeagleCluster 42.13 s BeagleCluster w/ NEON 5.18 s NEON optimized code significantly reduces the execution time ⇒ HPL performance can be improved by properly exploiting NEON 12 / 25
- 22. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Speaker Diarization Speaker Diarization • A speaker diarization algorithm detects “who speaks now”. • The algorithm here addressed is based on the real-time implementation described in [Colagiacomo, et al. 2010]. • The calculation of the cross-correlations between the channel i signal xi(t) and the channel j signal xj(t) is the most computational demanding part: Cij(t) = max τ {IFFT[FFT(xi(t)xj(t − τ)) • FFT(w(t))]} . Here, t is the time index, τ is the correlation lag, w(t) is the Hamming window and • denotes the element-wise product. 13 / 25
- 23. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Speaker Diarization Speaker Diarization (cont.) • Cluster-wide parallelism has been obtained assigning the feature extraction stage of each channel to one of the worker nodes. • The server process in the head node dispatches audio frames to the worker nodes through the MPI Bcast instruction and performs the final classification. • Performance have been evaluated in terms of Real-Time Factor (RTF): RTF = Total execution time Speech segment duration 14 / 25
- 24. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Speaker Diarization Speaker Diarization (cont.) • Audio data: four lapel microphone signals of meeting ES2009b contained in the AMI corpus. • Comparison with an Asus F9SG laptop (Intel Core2 Duo T8300 CPU running at 2.4 GHz and with 2 GB of RAM) • Power consumption is measured switching the LCD monitor off. 15 / 25
- 25. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Speaker Diarization Speaker Diarization (cont.) Single-board implementation results • Real-time execution is achieved through the NEON instruction set and reducing the number of cross-correlations: the maximum of Cij(t) is searched incrementing τ by ∆τ > 1. ∆τ Laptop BeagleBoard-xM 1 2.47 12.73 16 0.25 1.02 32 0.18 0.63 64 0.14 0.44 128 0.12 0.36 The choice of ∆τ is critical both for the laptop and the BeagleBoard-xM. 16 / 25
- 26. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Speaker Diarization Speaker Diarization (cont.) Cluster-wide implementation results ∆τ Single-board Five nodes 1 12.73 4.71 16 1.02 1.69 32 0.63 1.63 64 0.44 1.56 128 0.36 1.55 • The MPI version is almost 3 times as fast as the single-board one when ∆τ = 1. • As ∆τ increases, the MPI implementation performance decreases: the communication overhead becomes the new bottleneck. 17 / 25
- 27. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Speaker Diarization Speaker Diarization (cont.) Cluster-wide implementation • This has been verified in a four nodes cluster. • Nodes read audio data directly from the local file system. • One of the worker nodes performs both the feature extraction and the classification tasks. ∆τ Five nodes Four nodes (w/ local data) 1 4.71 3.35 16 1.69 0.33 32 1.63 0.23 64 1.56 0.18 128 1.55 0.16 Reducing the communication overhead real-time execution can be achieved with ∆τ = 16. 18 / 25
- 28. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Speaker Diarization Analysis of power consumption BeagleCluster 20.32 W Laptop 32.36 W Energy ratio Er = RTFcluster · Pcluster RTFlaptop · Plaptop ∼= 1.2 The communication overhead limits the energy efficiency of the Bea- gleCluster. Energy ratio of the four nodes cluster Er ∼= 0.69 Reducing the communication overhead the BeagleCluster is more efficient than the laptop PC. 19 / 25
- 29. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Conclusions • A cluster computer based on the BeagleBoard-xM platform has been described. • The cluster is based on open software for executing parallel tasks, management, and monitoring the nodes status. • High Performance Linpack has been used to obtain the number of floating point operations per second. • The performance improvement that can be achieved using NEON optimized code has been shown by means of a matrix multiplication benchmark. • Processing time and power consumption have been measured by means of a cluster-wide speaker diarization algorithm to evaluate the real-time capabilities and the energy efficiency of the cluster. 20 / 25
- 30. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Conclusions (cont.) • Results showed that using the 100 Mbit Ethernet interface, the BeagleCluster consumes 1.2 times the energy spent by the laptop PC. • Removing the communication bottleneck, the BeagleCluster achieves a superior energy efficiency. • The cost of the 5 nodes cluster is 655 e. Compared to the laptop PC, whose cost is 1100 e, the BeagleCluster is about 500 e cheaper. 21 / 25
- 31. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Future developments • The software platform will be expanded with a resource manager and a scheduler to enable the execution of batch jobs. • The energy efficiency will be assessed in a High-Availability scenario, for example using the cluster for hosting websites. • The use of more efficient hardware platforms (e.g., PandaBoards) and of the DM3730 DSP will be considered. 22 / 25
- 32. Introduction Purpose of this work The BeagleCluster Experiments Conclusions and Future Developments Thank you for your attention! Emanuele Principi Vito Colagiacomo [email protected] [email protected] Stefano Squartini Francesco Piazza [email protected] [email protected] 23 / 25
- 33. Manufacturer AMPROBE Model LH41A Measuring Range 0-40A, DC or AC peak Resolution 1 mA in 4 A range 10 mA in 40 A range Accuracy ±1.3% + 5 digits Frequency Range DC in DC 40 Hz to 400 Hz in AC 24 / 25
- 34. High-Performance Linpack: details Rmax 258.6 MFLOPS Problem size 15000 Block size 16 Grid ratio 2 × 2 25 / 25
- 35. H. W. Meuer, “The TOP500 Project: Looking Back Over 15 Years of Supercomputing Experience,” Informatik-Spektrum, vol. 31, no. 3, pp. 203–222, 2008. [Online]. Available: http://www.top500.org C. L. Belady, “In the Data Center, Power and Cooling Cost More Than the IT Equipment It Supports,” Electronics Cooling Magazine, vol. 13, no. 1, May 2007. W.-c. Feng and K. Cameron, “The Green500 List: Encouraging Sustainable Supercomputing,” IEEE Computer, vol. 40, no. 12, pp. 50–55, Dec. 2007. [Online]. Available: http://www.green500.org I. Ahmad and S. Ranka, Eds., Handbook of Energy-Aware and Green Computing, 1st ed., ser. Information Science. Boca Raton, US: CRC Press, Jan. 2012. S. Andrade, J. Dourado, and C. Maciel, “Low-power cluster using OMAP3530,” in Proc. of EDERC, Nice, France, Dec. 2010, pp. 220–224. K. F¨urlinger, C. Klausecker, and D. Kranzlm¨uller, “Towards energy efficient parallel computing on consumer electronic devices,” in Proc. of ICT-GLOW. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 1–9. M. Brim, R. Flanery, A. Geist, B. Luethke, and S. L. Scott, “Cluster Command and Control (C3) Tool Suite,” Parallel and Distributed Computing Practices, vol. 4, no. 4, Dec. 2001. 25 / 25
- 36. Argonne National Laboratory, “MPICH2,” http://www.mcs.anl.gov/research/projects/mpich2/. M. L. Massie, B. N. Chun, and D. E. Culler, “The Ganglia distributed monitoring system: design, implementation, and experience,” Parallel Computing, vol. 30, no. 7, pp. 817–840, 2004. M. Moattar and M. Homayounpour, “A review on speaker diarization systems and approaches,” Speech Communication, vol. 54, no. 10, pp. 1065–1103, 2012. E. Principi, R. Rotili, M. W¨ollmer, F. Eyben, S. Squartini, and B. Schuller, “Real-Time Activity Detection in a Multi-Talker Reverberated Environment,” Cognitive Computation, pp. 1–12, 2012. V. Colagiacomo, E. Principi, S. Cifani, and S. Squartini, “Real-Time Speaker Diarization on TI OMAP3530,” in Proc. of EDERC, Nice, France, Dec. 1st-2nd 2010. InfiniBand Trade Association, “InfiniBand Architecture Specification Release 1.2.1,” Jan. 2008. N. J. Boden, D. Cohen, R. E. Felderman, A. Kulawik, C. Seitz, J. N. Seizovic, and W. Su, “Myrinet: A Gigabit-per-second Local Area Network,” IEEE Micro, vol. 15, no. 1, pp. 29–36, Feb. 1995. 25 / 25