nach oben

Erschienen in:

07.02.2019

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

verfasst von: María Angélica Dávila Guzmán, Raúl Nozal, Rubén Gran Tejero, María Villarroya-Gaudó, Darío Suárez Gracia, Jose Luis Bosque

Erschienen in: The Journal of Supercomputing | Ausgabe 3/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Heterogeneous systems are the core architecture of most of the high-performance computing nodes, due to their excellent performance and energy efficiency. However, a key challenge that remains is programmability, specifically, releasing the programmer from the burden of managing data and devices with different architectures. To this end, we extend EngineCL to support FPGA devices. Based on OpenCL, EngineCL is a high-level framework providing load balancing among devices. Our proposal fully integrates FPGAs into the framework, enabling effective cooperation between CPU, GPU, and FPGA. With command overlapping and judicious data management, our work improves performance by up to 96% compared with single-device execution and delivers energy-delay gains of up to 37%. In addition, adopting FPGAs does not require programmers to make big changes in their applications because the extensions do not modify the user-facing interface of EngineCL.

Vorheriger Artikel Variable intra-task threading for power-constrained performance and energy optimization in DAG scheduling

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

This value corresponds to the cooperative execution and is lower than GPU-only DR, see Table 3, because there is less continuous work on the device.

Altera SDK for OpenCL Programming Guide. http://www.altera.co.uk/literature/hb/opencl-sdk/aocl_programming_guide.pdf

Alawieh M et al (2015) A high performance FPGA–GPU–CPU platform for a real-time locating system. In: EUSIPCO, pp 1576–1580

Belviranli ME et al (2013) A dynamic self-scheduling scheme for heterogeneous multiprocessor architectures. ACM Trans Archit Code Optim 9(4):57:1–57:20CrossRef

Binotto AlPD et al (2010) Towards dynamic reconfigurable load-balancing for hybrid desktop platforms. In: IPDPSW

Chen X et al (2017) CMSA: a heterogeneous CPU/GPU computing system for multiple similar RNA/DNA sequence alignment. In: BMC Bioinformatics

Chung ES et al (2010) Single-chip heterogeneous computing: Does the future include custom logic, FPGAs, and GPGPUs? In: Proceedings of the 43rd Annual International Symposium on Microarchitecture, MICRO ’43. IEEE Computer Society, Washington, pp 225–236

Gaster B, Howes L, Kaeli DR, Mistry P, Schaa D (2011) Heterogeneous computing with OpenCL, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco

Horowitz M (2014) 1.1 computing’s energy problem (and what we can do about it). In: ISSCC, pp 10–14

Igual FD, Jara LM, Pérez JIG, Piñuel L, Prieto-Matías M (2015) A power measurement environment for PCIe accelerators. Comput Sci R&D 30(2):115–124

10.

Kaleem R et al (2014) Adaptive heterogeneous scheduling for integrated GPUs. In: PACT. ACM, New York, pp 151–162

11.

Katranovet A et al (2016) Intel threading building block (TBB) flow graph as a software infrastructure layer for OpenCL-based computations. In: ACM IWOCL, pp 9:1–9:3

12.

Koch D et al (eds) (2016) FPGAs for software programmers. Springer, Cham

13.

Lee J et al (2016) Orchestrating multiple data-parallel kernels on multiple devices. In: International Conference on Parallel Architectures and Compilation Techniques, pp 355–366

14.

Luk C-K et al (2009) Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. IEEE/ACM Micro-42 p 45

15.

Mittal Sa (2015) A survey of CPU–GPU heterogeneous computing techniques. ACM Comput Surv 47(4):1–35CrossRef

16.

Momeni A et al (2016) Hardware thread reordering to boost OpenCL throughput on FPGAs. In: ICCD, pp 257–264

17.

Muslim FB et al (2017) Efficient FPGA implementation of Opencl high-performance computing applications via high-level synthesis. IEEE Access 5:2747–2762CrossRef

18.

Nane R et al (2016) A survey and evaluation of FPGA high-level synthesis tools. IEEE Trans Comput Aided Des Integr Circuits Syst 35(10):1591–1604CrossRef

19.

Nozal R et al (2018) EngineCL: usability and performance in heterogeneous computing. arXiv: abs/1805.02755

20.

Nozal R et al (2018) Load balancing in a heterogeneous world: Cpu-Xeon Phi co-execution of data-parallel kernels. J Supercomput 73(1):330–342

21.

Nunez-Yanez J (2018) Simultaneous multiprocessing in a software-defined heterogeneous FPGA. J Supercomput

22.

Pandit P et al (2014) Fluidic kernels: cooperative execution of OpenCL programs on multiple heterogeneous devices. In: Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization

23.

Pérez B (2017) Energy efficiency of load balancing for data-parallel applications in heterogeneous systems. J Supercomput 73(1):330–342CrossRef

24.

Pérez B et al (2016) Simplifying programming and load balancing of data parallel applications on heterogeneous systems. In: GPGPU. ACM, New York, pp. 42–51

25.

Qualcomm Snapdragon Heterogeneous Compute SDK (2018). https://developer.qualcomm.com/software/heterogeneous-compute-sdk

26.

Rethinagiri SK et al (2015) Trigeneous platforms for energy efficient computing of HPC applications. In: International Conference on High Performance Computing Trigeneous. IEEE

27.

SDSoC Environment User Guide. www.xilinx.com/support/documentation/sw_manuals/xilinx2017_1/ug1027-sdsoc-user-guide.pdf

28.

Tsoi KH et al (2010) Axel: a heterogeneous cluster with FPGAs and GPUs. In: ACM/SIGDA FPGA. ACM, New York, pp 115–124

29.

Vilches A et al (2015) Adaptive partitioning for irregular applications on heterogeneous CPU–GPU chips. Procedia Comput Sci ICCS 51:140–149CrossRef

30.

Wang Z et al (2016) A performance analysis framework for optimizing OpenCL applications on FPGAs. In: Proceedings of HPCA, pp 114–125

31.

Zhou S et al (2017) Accelerating graph analytics on CPU-FPGA heterogeneous platform. In: SBAC-PAD, pp 137–144

32.

Zohouri HR et al (2016) Evaluating and optimizing OpenCL kernels for high performance computing with FPGAs. In: SC. IEEE Press, Piscataway, pp 35:1–35:12

Titel: Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL
verfasst von: María Angélica Dávila Guzmán
Raúl Nozal
Rubén Gran Tejero
María Villarroya-Gaudó
Darío Suárez Gracia
Jose Luis Bosque
Publikationsdatum: 07.02.2019
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 3/2019
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-019-02768-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 3/2019

Caffe CNN-based classification of hyperspectral images on GPU

Acceleration of the integer motion estimation in JEM through pre-analysis techniques

A heuristic technique to improve energy efficiency with dynamic load balancing

A highly scalable parallel encoder version of the emergent JEM video encoder

An efficient GPU version of the preconditioned GMRES method

A self-optimized software tool for quantifying the degree of left ventricle hyper-trabeculation

Premium Partner