The Journal of Supercomputing

, Volume 75, Issue 10, pp 7003–7036 | Cite as

The survey on ARM processors for HPC

  • Daniel Yokoyama
  • Bruno Schulze
  • Fábio BorgesEmail author
  • Giacomo Mc Evoy


The ongoing effort to reach the exascale computing barrier has led to a myriad of research and publications in the topic of alternative energy-efficient architectures, such as ARM, for HPC systems. The staggering pace at which ARM architectures have evolved has increased the volume of publications on this topic even more. A complex subject as the race to exascale touches on several aspects such as floating-point performance, scalability issues in coupled workloads, net energy consumption and ratio of energy to performance. In this context, we see the opportunity to contribute to this subject by: (1) analyzing the state of the art to identify essential papers; (2) highlighting important developments of ARM architecture in support to HPC; (3) discussing both positive and negative trends observed regarding the use of ARM for HPC; and (4) listing key topics concerning the use of ARM for exascale computing, along with distinguished references for each one.


Exascale ARM High-performance computing Energy efficiency Heterogeneous computing 



The authors would like to acknowledge the National Laboratory for Scientific Computing Postgraduate program, CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) Ph.D. fellowship, the Atos Company through the cooperation project of Research, Development and Training of Human Resources in Computational Modeling and High-Performance Computing (conditioned to the receipt of resources by the Fundação de Apoio ao Desenvolvimento da Computação Científica-FACC) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) Grant Number 309873/2013-4.


  1. 1.
  2. 2.
  3. 3.
    Viso geral sobre intel advanced vector extensions 512. Accessed 22 Sept 2017
  4. 4.
    Linaro. Accessed 5 May 2019
  5. 5.
    Openhpc releases (2019). Accessed 5 May 2019
  6. 6.
    Abdurachmanov D, Bockelman B, Elmer P, Eulisse G, Knight R, Muzaffar S (2015) Heterogeneous high throughput scientific computing with apm x-gene and intel xeon phi. J Phys Conf Ser 608(1):012033Google Scholar
  7. 7.
    Abdurachmanov D, Elmer P, Eulisse G, Knight R, Niemi T, Nurminen JK, Nyback F, Pestana G, Ou Z, Khan K (2015) Techniques and tools for measuring energy efficiency of scientific software applications. J Phys Conf Ser 608:012032Google Scholar
  8. 8.
    Abdurachmanov D, Elmer P, Eulisse G, Muzaffar S (2014) Initial explorations of arm processors for scientific computing. J Phys Conf Ser 523(1):012009Google Scholar
  9. 9.
    Addiego N (2017) Evaluation of the efficiency of an ARM-based beowulf cluster versus traditional desktop computing for high performance computing. Master’s thesis, University of San DiegoGoogle Scholar
  10. 10.
    Adhianto L, Banerjee S, Fagan M, Krentel M, Marin G, Mellor-Crummey J, Tallent NR (2010) Hpctoolkit: tools for performance analysis of optimized parallel programs. Concurr Comput Pract Exp 22(6):685–701Google Scholar
  11. 11.
    Allalen M, Brayford D, Tafani D, Weinberg V, Mohr B, Brömmel D, Halver R, Meinke J, Mohanty S (2015) The mont-blanc project: First phase successfully finished. arXiv preprint arXiv:1508.05075
  12. 12.
    ARM: ARM Architecture Reference Manual—ARMv7-A and ARMv7-R edition. (2014). Accessed 24 Apr 2019
  13. 13.
    Armejach A, Caminal H, Cebrian JM, Langarita R, González-Alberquilla R, Adeniyi-Jones C, Valero M, Casas M, Moretó M (2019) Using arms scalable vector extension on stencil codes. J Supercomput.
  14. 14.
    Azimi R, Fox T, Gonzalez W, Reda S (2018) Scale-out vs scale-up: a study of arm-based socs on server-class workloads. ACM Trans Model Perform Eval Comput Syst (TOMPECS) 3(4):18Google Scholar
  15. 15.
    Azimi R, Zhan X, Reda S (2015) How good are low-power 64-bit socs for server-class workloads? In: 2015 IEEE International Symposium on Workload Characterization (IISWC), IEEE, pp 116–117Google Scholar
  16. 16.
    Banchelli F, Garcia M, Josep M, Mantovani F, Morillo J, Peiro K, Ramirez G, Teruel X, Mc Evoy G, Wanza J, Gracia J, Lumi A, Ganellari D, Schiffmann P (2019) Mb3 d6.9—performance analysis of applications and mini-applications and benchmarking on the project test platforms. Tech. rep. Accessed 24 Apr 2019
  17. 17.
    Barr J (2018) EC2 Instances (A1) Powered by Arm-Based AWS Graviton Processors. Accessed 24 Apr 2019
  18. 18.
    Barrett BW, Brightwell R, Grant R, Hammond SD, Hemmert KS (2014) An evaluation of mpi message rate on hybrid-core processors. Int J High Perform Comput Appl 28(4):415–424. Google Scholar
  19. 19.
    Beserra D, Pinheiro MK, Souveyet C, Steffenel LA, Moreno ED (2017) Performance evaluation of os-level virtualization solutions for hpc purposes on soc-based systems. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), IEEE, pp 363–370Google Scholar
  20. 20.
    Bez JL, Bernart EE, Santos FF, Schnorr LM, Navaux POA (2016) Performance and energy efficiency analysis of HPC physics simulation applications in a cluster of arm processors. Pract Exp Concurr Comput 29:e4014Google Scholar
  21. 21.
    Blem E, Menon J, Sankaralingam K (2013) Power struggles: revisiting the risc vs. cisc debate on contemporary arm and x86 architectures. In: 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA2013), IEEE, pp 1–12Google Scholar
  22. 22.
    Boggs D, Brown G, Tuck N, Venkatraman K (2015) Denver: Nvidia’s first 64-bit arm processor. IEEE Micro 35(2):46–55Google Scholar
  23. 23.
    Brash D (2010) Extensions to the armv7-a architecture. In: 2010 IEEE Hot Chips 22 Symposium (HCS), IEEE, pp 1–21Google Scholar
  24. 24.
    Bratt I (2018) Arm’s First Generation Machine Learning Processor. Accessed 24 Apr 2019
  25. 25.
    Bull D, Das S, Shivashankar K, Dasika GS, Flautner K, Blaauw D (2011) A power-efficient 32 bit arm processor using timing-error detection and correction for transient-error tolerance and adaptation to pvt variation. IEEE J Solid-State Circ 46(1):18–31Google Scholar
  26. 26.
    Calore E, Mantovani F, Ruiz D (2018) Advanced performance analysis of hpc workloads on cavium thunderx. In: 2018 International Conference on High Performance Computing & Simulation (HPCS), IEEE, pp 375–382Google Scholar
  27. 27.
    Canuto M, Bosch R, Macias M, Guitart J (2016) A methodology for full-system power modeling in heterogeneous data centers. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, ACM, pp 20–29Google Scholar
  28. 28.
    Castelló A, Duato J, Mayo R, Peña AJ, Quintana-Ortí ES, Roca V, Silla F (2014) On the use of remote gpus and low-power processors for the acceleration of scientific applications. In: The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (ENERGY), pp 57–62Google Scholar
  29. 29.
    Choi J, Dukhan M, Liu X, Vuduc R (2014) Algorithmic time, energy, and power on candidate hpc compute building blocks. In: 2014 IEEE 28th International Parallel and Distributed Processing Symposium, IEEE, pp 447–457Google Scholar
  30. 30.
    Cloutier MF, Paradis C, Weaver VM (2014) Design and analysis of a 32-bit embedded high-performance cluster optimized for energy and performance. In: Proceedings of the 1st International Workshop on Hardware-Software Co-Design for High Performance Computing, IEEE Press, pp 1–8Google Scholar
  31. 31.
    Cong J, Huang M, Wu D, Yu CH (2016) Heterogeneous datacenters: options and opportunities. In: Proceedings of the 53rd Annual Design Automation Conference, ACM, p 16Google Scholar
  32. 32.
    Corni E, Morganti L, Morigi MP, Brancaccio R, Bettuzzi M, Levi G, Peccenini E, Cesini D, Ferraro A (2016) X-ray computed tomography applied to objects of cultural heritage: Porting and testing the filtered back-projection reconstruction algorithm on low power systems-on-chip. In: 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), IEEE, pp 369–372Google Scholar
  33. 33.
    D’Agostino D, Quarati A, Clematis A, Morganti L, Corni E, Giansanti V, Cesini D, Merelli I (2019) Soc-based computing infrastructures for scientific applications and commercial services: performance and economic evaluations. Future Gener Comput Syst 96:11–22Google Scholar
  34. 34.
    De Gelas J (2017) AppliedMicro’s X-Gene 3 SoC Begins Sampling: A Step in ARM’s 2017 Server Ambitions. Accessed 24 Apr 2019
  35. 35.
    Duran A, Ayguadé E, Badia RM, Labarta J, Martinell L, Martorell X, Planas J (2011) Ompss: a proposal for programming heterogeneous multi-core architectures. Parallel Process Lett 21(02):173–193MathSciNetGoogle Scholar
  36. 36.
    Durand Y, Carpenter PM, Adami S, Bilas A, Dutoit D, Farcy A, Gaydadjiev G, Goodacre J, Katevenis M, Marazakis M et al (2014) Euroserver: Energy efficient node for european micro-servers. In: 2014 17th Euromicro Conference on Digital System Design (DSD), IEEE, pp 206–213Google Scholar
  37. 37.
    Elangovan VK, Badia RM, Parra EA (2012) Ompss-opencl programming model for heterogeneous systems. In: International Workshop on Languages and Compilers for Parallel Computing, Springer, pp 96–111Google Scholar
  38. 38.
    Feng W, Scogland T. Green500. Accessed 5 May 2019
  39. 39.
    Ferreron A, Jagtap R, Rusitoru R (2016) Identifying representative regions of parallel hpc applications: a cross-architectural evaluation. In: 2016 IEEE International Symposium on Workload Characterization (IISWC), IEEE, pp 1–2Google Scholar
  40. 40.
    Fialho L, Gracia J, Nigay A, Evoy M (2019) Mb3 d7.13—final report on enhancements to message passing. Tech. rep. Accessed 24 Apr 2019
  41. 41.
    Filiposka S, Mishev A, Juiz C (2016) Current prospects towards energy-efficient top hpc systems. Comput Sci Inf Syst 13(1):151–171Google Scholar
  42. 42.
    Fox T (2017) Revisiting the case of arm socs in high-performance computing clusters. Ph.D. thesis, School of Engineering, Brown UniversityGoogle Scholar
  43. 43.
    Frid N, Ivošević D, Sruk V (2015) Heterogeneity impact on mpsoc platforms performance. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), IEEE, pp 1071–1076Google Scholar
  44. 44.
    Geveler M, Ribbrock D, Donner D, Ruelmann H, Höppke C, Schneider D, Tomaschewski D, Turek S (2016) The icarus white paper: a scalable, energy-efficient, solar-powered hpc center based on low power gpus. In: European Conference on Parallel Processing, Springer, pp 737–749Google Scholar
  45. 45.
    Gómez C, Martínez F, Armejach A, Moretó M, Mantovani F, Casas M (2019) Design space exploration of next-generation HPC machines. Barcelona Supercomputing Center.
  46. 46.
    Grant RE, Levenhagen M, Olivier SL, DeBonis D, Pedretti KT, Laros JH III (2016) Standardizing power monitoring and control at exascale. Computer 49(10):38–46Google Scholar
  47. 47.
    Grass T, Allande C, Armejach A, Rico A, Ayguadé E, Labarta J, Valero M, Casas M, Moreto M (2016) Musa: a multi-level simulation approach for next-generation hpc machines. In: SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE, pp 526–537Google Scholar
  48. 48.
    Grasso I, Radojkovic P, Rajovic N, Gelado I, Ramirez A (2014) Energy efficient hpc on embedded socs: Optimization techniques for mali gpu. In: 2014 IEEE 28th International on Parallel and Distributed Processing Symposium, IEEE, pp 123–132Google Scholar
  49. 49.
    Griessl R, Peykanu M, Hagemeyer J, Porrmann M, Krupop S, vor dem Berge M, Kiesel T, Christmann W (2014) A scalable server architecture for next-generation heterogeneous compute clusters. In: 2014 12th IEEE International Conference on Embedded and Ubiquitous Computing (EUC), IEEE, pp 146–153Google Scholar
  50. 50.
    Gu B, Kwak Y (2016) Map task allocation strategy in an arm-based hadoop cluster by using local storage as split cache. Int J Adv Media Commun 6(1):65–72Google Scholar
  51. 51.
    Gwennap L (2017) Performance Arms X-Gene 3 for Cloud. Accessed 24 Apr 2019
  52. 52.
    Halpern M, Zhu Y, Reddi VJ (2016) Mobile cpu’s rise to power: Quantifying the impact of generational mobile cpu design trends on performance, energy, and user satisfaction. In: 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA), IEEE, pp 64–76Google Scholar
  53. 53.
    Huawei (2019)Huawei Unveils Industry’s Highest-Performance ARM-based CPU. Accessed 24 Apr 2019
  54. 54.
    Iliescu DA, Petrogalli F. Arm scalable vector extension and application to machine learning. Tech. rep., ARM. Whitepaper Accessed 24 Apr 2019
  55. 55.
    Jackson A, Turner A, Weiland M, Johnson N, Perks O, Parsons M (2019) Evaluating the arm ecosystem for high performance computing. arXiv preprint arXiv:1904.04250
  56. 56.
    Jacquet D, Hasbani F, Flatresse P, Wilson R, Arnaud F, Cesana G, Di Gilio T, Lecocq C, Roy T, Chhabra A et al (2014) A 3 ghz dual core processor arm cortex tm-a9 in 28 nm utbb fd-soi cmos with ultra-wide voltage range and energy efficiency optimization. IEEE J Solid-State Circ 49(4):812–826Google Scholar
  57. 57.
    Jarus M, Varrette S, Oleksiak A, Bouvry P (2013) Performance evaluation and energy efficiency of high-density hpc platforms based on intel, amd and arm processors. In: European Conference on Energy Efficiency in Large Scale Distributed Systems, Springer, pp 182–200Google Scholar
  58. 58.
    Jin C, de Supinski BR, Abramson D, Poxon H, DeRose L, Dinh MN, Endrei M, Jessup ER (2016) A survey on software methods to improve the energy efficiency of parallel computing. Int J High Perform Comput Appl 31:1094342016665471Google Scholar
  59. 59.
    Jundt A, Cauble-Chantrenne A, Tiwari A, Peraza J, Laurenzano MA, Carrington L (2015) Compute bottlenecks on the new 64-bit arm. In: Proceedings of the 3rd International Workshop on Energy Efficient Supercomputing, ACM, p 6Google Scholar
  60. 60.
    Jung YW, Sok SW, Santoso GZ, Shin JS, Kim HY (2015) Prototype of light-weight hypervisor for arm server virtualization. In: Proceedings of the International Conference on Embedded Systems and Applications (ESA). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p 36Google Scholar
  61. 61.
    Kaewkasi C, Srisuruk W (2014) Optimizing performance and power consumption for an arm-based big data cluster. In: TENCON 2014-2014 IEEE Region 10 Conference, IEEE, pp 1–6Google Scholar
  62. 62.
    Kaewkasi C, Srisuruk W (2014) A study of big data processing constraints on a low-power hadoop cluster. In: 2014 International Computer Science and Engineering Conference (ICSEC), IEEE, pp 267–272Google Scholar
  63. 63.
    Kalyanasundaram J, Simmhan Y (2017) Arm wrestling with big data: A study of arm64 and x64 servers for data intensive workloads. arXiv preprint arXiv:1701.05996
  64. 64.
    Kecskemeti G, Hajji W, Tso FP (2017) Modelling low power compute clusters for cloud simulation. In: 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), IEEE, pp 39–45Google Scholar
  65. 65.
    Kennedy P. Intel Atom C2550 Benchmarks - 4 core Avoton tested. Accessed on 24 Apr 2019
  66. 66.
    Kodama Y, Odajima T, Asato A, Sato M (2019) Evaluation of the riken post-k processor simulator. arXiv preprint arXiv:1904.06451
  67. 67.
    Kumar D, Memon S, Thebo LA (2018) Design, implementation & performance analysis of low cost high performance computing (hpc) clusters. In: 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, pp 1–6Google Scholar
  68. 68.
    Laurenzano MA, Tiwari A, Cauble-Chantrenne A, Jundt A, Ward WA, Campbell R, Carrington L (2016) Characterization and bottleneck analysis of a 64-bit armv8 platform. In: 2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp 36–45.
  69. 69.
    Lee Y, Kim S (2015) Empirical characterization of power efficiency for large scale data processing. In: 2015 17th International Conference on Advanced Communication Technology (ICACT), IEEE, pp 787–790Google Scholar
  70. 70.
    Loghin D, Tudor BM, Zhang H, Ooi BC, Teo YM (2015) A performance study of big data on small nodes. Proc VLDB Endow 8(7):762–773Google Scholar
  71. 71.
    Lorenzon AF, Cera MC, Beck ACS (2015) On the influence of static power consumption in multicore embedded systems. In: 2015 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp 1374–1377Google Scholar
  72. 72.
    Lorenzon AF, Sartor AL, Cera MC, Beck ACS (2015) Optimized use of parallel programming interfaces in multithreaded embedded architectures. In: 2015 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), IEEE, pp 410–415Google Scholar
  73. 73.
    Luecke GR, Groth BM, Weeks NT, Kraeva M (2017) Comparing allinea’s and intel’s performance tools for hpc. In: Proceedings of the 25th High Performance Computing Symposium, HPC ’17, pp. 3:1–3:12. Society for Computer Simulation International, San Diego, CA, USA.
  74. 74.
    Mantovani F, Calore E (2018) Performance and power analysis of hpc workloads on heterogeneous multi-node clusters. J Low Power Electron Appl 8(2):13Google Scholar
  75. 75.
    Mantovani F, Ruiz D, Vilarrubi O, Martorell X, Nieto D, Auweter A, Tafani D, Adeniyi-Jones C, Gloaguen H, Utrera G (2015) D5.11—Final report on porting and tuning of system software to ARM architecture. Tech. rep. Accessed 24 Apr 2019
  76. 76.
    Mappuji A, Effendy N, Mustaghfirin M, Sondok F, Yuniar RP, Pangesti SP (2016) Study of raspberry pi 2 quad-core cortex-a7 cpu cluster as a mini supercomputer. In: 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), IEEE, pp 1–4Google Scholar
  77. 77.
    Maqbool J, Oh S, Fox GC (2015) Evaluating arm hpc clusters for scientific workloads. Concurr Comput Pract Exp 27(17):5390–5410Google Scholar
  78. 78.
    Maynard C, Selwood P (2016) Exascale computing research priorities for the met office forecasting research technical report no: 611Google Scholar
  79. 79.
    McCalpin J. STREAM Benchmark results. Accessed 24 Apr 2019
  80. 80.
    McCalpin JD (2016) Memory bandwidth and system balance in HPC systems. SC16 Invited Talk. Accessed 5 May 2019
  81. 81.
    McCalpin JD (1995) Memory bandwidth and machine balance in current high performance computers. IEEE Comput Soc Tech Comm Comput Arch (TCCA) Newslett 2:19–25Google Scholar
  82. 82.
    McIntoshSmith S, Price J, Deakin T, Poenaru A (2019) A performance analysis of the first generation of hpc-optimized arm processors. Pract Exp Concurr Comput. Google Scholar
  83. 83.
    Mellanox: InfiniBand Cards - Overview. (2014). Accessed 24 Apr 2019
  84. 84.
    Milluzzi A, George A, Lam H (2016) Computational and memory analysis of tegra socs. In: 2016 IEEE High Performance Extreme Computing Conference (HPEC), pp 1–7.
  85. 85.
    Moorthy P, Kapre N (2015) Zedwulf: Power-performance tradeoffs of a 32-node zynq soc cluster. In: 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), IEEE, pp 68–75Google Scholar
  86. 86.
    Morales F, Bismarck JL (2016) Evaluating gem5 and qemu virtual platforms for arm multicore architectures. Master’s thesis, KTH, School of Information and Communication Technology (ICT)Google Scholar
  87. 87.
    Morganti L, Cesini D, Ferraro A (2016) Evaluating systems on chip through hpc bioinformatic and astrophysic applications. In: 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), IEEE, pp 541–544Google Scholar
  88. 88.
    Nikolskiy V, Stegailov V (2016) Floating-point performance of arm cores and their efficiency in classical molecular dynamics. J Phys Conf Ser 681(1):012049Google Scholar
  89. 89.
    NVIDIA: Tegra K1 Technical Reference Manual. (2014). Accessed 24 Apr 2019
  90. 90.
    Oyarzun G, Borrell R, Gorobets A, Mantovani F, Oliva A (2018) Efficient cfd code implementation for the arm-based mont-blanc architecture. Future Gener Comput Syst 79:786–796Google Scholar
  91. 91.
    Plugaru V, Varrette S, Bouvry P (2014) Performance analysis of cloud environments on top of energy-efficient platforms featuring low power processors. In: 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom), IEEE, pp 416–425Google Scholar
  92. 92.
    Pruitt DD, Freudenthal EA (2016) Preliminary investigation of mobile system features potentially relevant to hpc. In: Proceedings of the 4th International Workshop on Energy Efficient Supercomputing, IEEE Press, pp 54–60Google Scholar
  93. 93.
    Puzović M, Manne S, GalOn S, Ono M (2016) Quantifying energy use in dense shared memory hpc node. In: Proceedings of the 4th International Workshop on Energy Efficient Supercomputing, E2SC ’16, pp 16–23. IEEE Press, Piscataway, NJ, USA.
  94. 94.
    Raho M, Spyridakis A, Paolino M, Raho D (2015) Kvm, xen and docker: A performance analysis for arm based nfv and cloud computing. In: 2015 IEEE 3rd Workshop on Advances in IEEE Information, Electronic and Electrical Engineering (AIEEE), pp 1–8Google Scholar
  95. 95.
    Rajovic N, Carpenter PM, Gelado I, Puzovic N, Ramirez A, Valero M (2013) Supercomputing with commodity cpus: Are mobile socs ready for hpc? In: 2013 SC-International Conference for High Performance Computing, Networking, Storage and Analysis (SC), IEEE, pp 1–12Google Scholar
  96. 96.
    Rajovic N, Rico A, Mantovani F, Ruiz D, Vilarrubi JO, Gomez C, Backes L, Nieto D, Servat H, Martorell X, Labarta J, Ayguade E, Adeniyi-Jones C, Derradji S, Gloaguen H, Lanucara P, Sanna N, Mehaut JF, Pouget K, Videau B, Boyer E, Allalen M, Auweter A, Brayford D, Tafani D, Weinberg V, Brömmel D, Halver R, Meinke JH, Beivide R, Benito M, Vallejo E, Valero M, Ramirez A (2016) The mont-blanc prototype: An alternative approach for hpc systems. In: SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, pp 444–455.
  97. 97.
    Rajovic N, Rico A, Puzovic N, Adeniyi-Jones C, Ramirez A (2014) Tibidabo: making the case for an arm-based hpc system. Future Gener Comput Syst 36:322–334Google Scholar
  98. 98.
    Reeda R, Cox MA, Wrigley T, Mellado B (2015) A cpu benchmarking characterization of arm based processors. Computer 7(3):581–586Google Scholar
  99. 99.
    Rethinagiri SK, Palomar O, Moreno JA, Unsal O, Cristal A (2015) Trigeneous platforms for energy efficient computing of hpc applications. In: 2015 IEEE 22nd International Conference on High Performance Computing (HiPC), IEEE, pp 264–274Google Scholar
  100. 100.
    Ross JA, Richie DA, Park SJ, Shires DR, Pollock LL (2014) A case study of opencl on an android mobile gpu. In: 2014 IEEE High Performance Extreme Computing Conference (HPEC), pp 1–6Google Scholar
  101. 101.
    Rostirolla G, da Rosa Righi R, Rodrigues VF, Velho P, Padoin EL (2015) Greenhpc: a novel framework to measure energy consumption on hpc applications. In: Sustainable Internet and ICT for Sustainability (SustainIT), 2015, IEEE, pp 1–8Google Scholar
  102. 102.
    Ruiz D, Mantovani F, Casas M, Labarta J, Spiga F (2018) The HPCG benchmark: analysis, shared memory preliminary improvements and evaluation on an arm-based platform. Polytechnic University of Catalonia. Accessed 5 May 2019
  103. 103.
    Rupp K. Knights Landing vs. Knights Corner, Haswell, Ivy Bridge, and Sandy Bridge: STREAM benchmark results. Accessed 24 Apr 2019
  104. 104.
    Schulz KW, Baird CR, Brayford D, Georgiou Y, Kurtzer GM, Simmel D, Sterling T, Sundararajan N, Van Hensbergen E (2016) Cluster computing with openhpc. In: HPCSYSPROS16: Inaugural HPC systems professionals workshop.
  105. 105.
    Schürmans S, Onnebrink G, Leupers R, Ascheid G, Chen X (2016) Frequency-aware esl power estimation for arm cortex-a9 using a black box processor model. ACM Trans Embed Comput Syst (TECS) 16(1):26Google Scholar
  106. 106.
    Selinger A, Rupp K, Selberherr S (2016) Evaluation of mobile arm-based socs for high performance computing. In: Society for Computer Simulation International Proceedings of the 24th High Performance Computing Symposium, p 21Google Scholar
  107. 107.
    Sheen SK (2016) Astro-a low-cost, low-power cluster for cpu-gpu hybrid computing using the jetson TK1. Master’s thesis, California Polytechnic State University.
  108. 108.
    Shore C. Porting to 64-bit arm. Tech. rep., ARM. Whitepaper Accessed 24 Apr 2019
  109. 109.
    Silvano C, Agosta G, Bartolini A, Beccari AR, Benini L, Bispo J, Cmar R, Cardoso JM, Cavazzoni C, Martinovič J et al (2016) Autotuning and adaptivity approach for energy efficient exascale hpc systems: the antarex approach. In: Design, Automation & Test in Europe Conference & Exhibition (DATE), pp 708–713. IEEE (2016)Google Scholar
  110. 110.
    Sirin U, Appuswamy R, Ailamaki A (2016) Oltp on a server-grade arm: power, throughput and latency comparison. In: Proceedings of the 12th International Workshop on Data Management on New Hardware, ACM, p 10Google Scholar
  111. 111.
    Stegailov V, Vecher V (2018) Efficiency analysis of intel, AMD and Nvidia 64-Bit hardware for memory-bound problems: a case study of Ab initio calculations with VASP, pp 81–90.
  112. 112.
    Stephens N, Biles S, Boettcher M, Eapen J, Eyole M, Gabrielli G, Horsnell M, Magklis G, Martinez A, Premillieu N et al (2017) The arm scalable vector extension. IEEE Micro 37(2):26–39Google Scholar
  113. 113.
    Stokke KR, Stensland HK, Griwodz C, Halvorsen P (2016) A high-precision, hybrid gpu, cpu and ram power model for generic multimedia workloads. In: Proceedings of the 7th International Conference on Multimedia Systems, MMSys ’16, pp 14:1–14:12. ACM, New York, NY, USA.
  114. 114.
    Stokke KR, Stensland HK, Halvorsen P, Griwodz C (2016) High-precision power modelling of the tegra k1 variable smp processor architecture. In: 2016 IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSOC), pp 193–200.
  115. 115.
    Strohmaier E, Dongarra J, Horst S, Meuer M, Meuer H (2019) Top 500 The List. Accessed 5 May 2019
  116. 116.
    Sundriyal V, Fought E, Sosonkina M, Windus TL (2016) Power profiling and evaluating the effect of frequency scaling on nwchem. In: Society for Computer Simulation International Proceedings of the 24th High Performance Computing Symposium, p 19Google Scholar
  117. 117.
    Thompson SE, Parthasarathy S (2006) Moore’s law: the future of si microelectronics. Mater today 9(6):20–25Google Scholar
  118. 118.
    Tiwari A, Keipert K, Jundt A, Peraza J, Leang SS, Laurenzano M, Gordon MS, Carrington L (2015) Performance and energy efficiency analysis of 64-bit arm using gamess. In: Proceedings of the 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing, ACM, p 8Google Scholar
  119. 119.
    Trader T (2014) The exascale revolution. Accessed 23 July 2016
  120. 120.
    Villebonnet V, Da Costa G, Lefevre L, Pierson JM, Stolf P (2014) Towards generalizing”” big little”” for energy proportional hpc and cloud infrastructures. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing (BdCloud), IEEE, pp 703–710Google Scholar
  121. 121.
    Weloli JW, Bilavarn S, Derradji S, Belleudy C, Lesmanne S (2016) Efficiency modeling and analysis of 64-bit arm clusters for hpc. In: 2016 Euromicro Conference on Digital System Design (DSD), pp 342–347.
  122. 122.
    Whaley RC, Petitet A, Dongarra JJ (2001) Automated empirical optimizations of software and the atlas project. Parallel Comput 27(1–2):3–35zbMATHGoogle Scholar
  123. 123.
    Whatmough PN, Das S, Hadjilambrou Z, Bull DM (2017) Power integrity analysis of a 28 nm dual-core arm cortex-a57 cluster using an all-digital power delivery monitor. IEEE J Solid-State Circ 52(6):1643–1654Google Scholar
  124. 124.
    Wrigleya G, Reed R, Mellado B (2015) Memory benchmarking characterisation of arm-based socs. Computer 7(3):607–617Google Scholar
  125. 125.
    Xie X (2016) Low-power technologies in high-performance computer: trends and perspectives. Natl Sci Rev 3(1):23–25Google Scholar
  126. 126.
    Yoshida T (2018) Fujitsu high performance cpu for the post-k computer. In: Hot Chips 30 Symposium (HCS), Series Hot Chips, vol 18Google Scholar
  127. 127.
    Zhang J, You S, Gruenwald L (2015) Tiny gpu cluster for big spatial data: A preliminary performance evaluation. In: 2015 IEEE 35th International Conference on Distributed Computing Systems Workshops (ICDCSW), IEEE, pp 142–147Google Scholar
  128. 128.
    Zhirnov VV, Cavin RK, Hutchby JA, Bourianoff GI (2003) Limits to binary logic switch scaling-a gedanken model. Proc IEEE 91(11):1934–1939Google Scholar
  129. 129.
    Zhu Y, Mattina M, Whatmough P (2018) Mobile machine learning hardware at arm: a systems-on-chip (soc) perspective. arXiv preprint arXiv:1801.06274

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratório Nacional de Computação Científica (LNCC)PetrópolisBrazil
  2. 2.R&D Atos/Bull (ARM Software Lab)PetrópolisBrazil

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