Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 4/2018

16.11.2017

A mathematical model to calculate real cost/performance in software distributed shared memory on computing environments

verfasst von: Ehsan Mousavi Khaneghah, Nosratollah Shadnoush, Amir Hossein Ghobakhlou

Erschienen in: The Journal of Supercomputing | Ausgabe 4/2018

Einloggen

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

search-config
loading …

Abstract

One of the important factors in high-performance computing (HPC) is the cost/performance ratio. Calculation of cost/performance ratio is the main criterion for the separation of hardware computing systems (supercomputers) from software computing systems (Cluster, Grid, Peer-to-Peer). There are various economic methods to calculate hardware cost. In addition, there are numerous methods in software engineering to calculate the cost of developing and programming the scientific and engineering software. The computing power in the aforementioned systems is basically calculated with programs like LINPACK and HPCL. The inter-process communication is considered as a variable in calculating the cost of executing the scientific programs, whose nature and amount depends on the program execution itself. As there is a high dependency of effective variables in cost calculation of inter-process communications during the program execution, it should be used for calculating the cost of any application. This paper complements the existing methods by presenting a more comprehensive and accurate method to calculate the real cost of distributed shared memory (DSM) mechanisms used by HPC Systems. Therefore, a systematic method has been used to achieve a whole equation for DSM costing, determine the effective factors of the cost, and propose a method based on costing economic methods. Effective parameters are classified into two groups, namely DSM-inhere dependent and application-specific dependent parameters. Each parameter is presented and discussed, and the correlation between them specifies the system’s weight on DSM real cost according to which the cost is modeled and validated analytically.
Vorheriger Artikel An efficient anonymous authentication protocol in multiple server communication networks (EAAM)
Nächster Artikel Parallel implementations of the 3D fast wavelet transform on a Raspberry Pi 2 cluster

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
Literatur
1.
Expósito, RR et al (2013) Running scientific codes on Amazon EC2: a performance analysis of five high-end instances. J Comput Sci Technol 13:153–159
2.
Al Geist, Reed DA (2017) A survey of high-performance computing scaling challenges. Int J High Perform Comput Appl 31(1):104–113CrossRef
3.
Thackston R, Fortenberry R (2015) High performance computing: considerations when deciding to rent or buy
4.
Zhang, Z, Cherkasova L, Loo BT (2014) Optimizing cost and performance trade-offs for MapReduce job processing in the cloud. In: 2014 IEEE Network Operations and Management Symposium (NOMS). IEEE
5.
Kurmann C, Rauch F, Stricker TM, (2003) Cost/performance tradeoffs in network interconnects for clusters of commodity PCs. Workshop on Communication Architecture for Clusters, Nice, France
6.
Rauber T, Rünger G (2013) Parallel programming: for multicore and cluster systems. Springer, BerlinCrossRefMATH
7.
Tootaghaj DZ et al (2015) Evaluating the combined impact of node architecture and cloud workload characteristics on network traffic and performance/cost. In: 2015 IEEE International Symposium on Workload Characterization (IISWC). IEEE
8.
Adams M (2014) HPGMG 1.0: a benchmark for ranking high performance computing systems
9.
Sukharev PV et al (2017) Benchmarking of high performance computing clusters with heterogeneous CPU/GPU architecture. In: IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE
10.
Dongarra J, Heroux MA, Luszczek P (2015) HPCG benchmark: a new metric for ranking high performance computing systems. Knoxville
11.
Al-Roomi M et al (2013) Cloud computing pricing models: a survey. Int J Grid Distrib Comput 6(5):93–106CrossRef
12.
Bhowmick A, Prasad CGVN (2017) Time and cost optimization by grid computing over existing traditional IT systems in business environment. Int J 5:93–98
13.
Han R et al (2014) Enabling cost-aware and adaptive elasticity of multi-tier cloud applications. Future Gener Comput Syst 32:82–98CrossRef
14.
Núñez A, Merayo MG (2014) A formal framework to analyze cost and performance in map-reduce based applications. J Comput Sci 5(2):106–118CrossRef
15.
Iosup A et al (2011) Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans Parallel Distrib Syst 22(6):931–945CrossRef
16.
Menascé D, Almeida V (1990) Cost-performance analysis of heterogeneity in supercomputer architectures. In: Proceedings of Supercomputing’90. IEEE
17.
Marathe A et al (2013) A comparative study of high-performance computing on the cloud. In: Proceedings of the 22nd International Symposium on High-Performance Parallel and Distributed Computing. ACM
18.
Garg SK, Versteeg S, Buyya R (2013) A framework for ranking of cloud computing services. Future Gener Comput Syst 29(4):1012–1023CrossRef
19.
De Alfonso C (2013) An economic and energy-aware analysis of the viability of outsourcing cluster computing to a cloud. Future Gener Comput Syst 29(3):704–712CrossRef
20.
Kaplan R, Anderson SR (2013) Time-driven activity-based costing: a simpler and more powerful path to higher profits. Harvard business press, Boston
21.
Tahir M et al (2016) Framework for Better Reusability in Component Based Software Engineering. J Appl Environ Biol Sci (JAEBS) 6:77–81
22.
Fenton N, Bieman J (2014) Software metrics: a rigorous and practical approach. CRC Press, Boca RatonCrossRefMATH
23.
Berriman GB et al (2010) The application of cloud computing to astronomy: a study of cost and performance. In: Sixth IEEE International Conference on e-Science Workshops. IEEE
24.
Deelman E et al (2015) Pegasus, a workflow management system for science automation. Future Gener Comput Syst 46:17–35CrossRef
25.
Yan Z et al (2011) Cloud versus in-house cluster: evaluating Amazon cluster compute instances for running MPI applications. In: State of the Practice Reports. ACM
26.
Woitaszek M, Tufo HM (2010) Developing a cloud computing charging model for high-performance computing resources. In: IEEE 10th International Conference on Computer and Information Technology (CIT). IEEE
27.
Aviram A et al (2012) Efficient system-enforced deterministic parallelism. Commun ACM 55(5):111–119CrossRef
28.
Otley D, Emmanuel KMC (2013) Readings in accounting for management control. Springer, Berlin
29.
Schöner G (2013) Dynamical systems thinking. In: Handbook of developmental systems theory and methodology, p 188
30.
Drury CM (2013) Management and cost accounting. Springer, Berlin
31.
Deegan C (2012) Australian financial accounting. McGraw-Hill Education Australia
32.
Lian X et al (2015) Cache coherence protocols in shared-memory multiprocessors
33.
Lenoski DE, Weber W-D (2014) Scalable shared-memory multiprocessing. Elsevier, Amsterdam
34.
Qura-Tul FASN, Khan AKDMS (2015) Development of cluster computing—a review. Development 5(1):1–9
35.
Satish N et al (2012) Can traditional programming bridge the ninja performance gap for parallel computing applications? ACM SIGARCH Computer Architecture News, vol 40, no 3. IEEE Computer Society
36.
Menezo LG, Puente V, Gregorio J-A (2015) Flask coherence: a morphable hybrid coherence protocol to balance energy, performance, and scalability. In: 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA). IEEE
37.
Serrano Gómez M (2013) Scheduling local and remote memory in cluster computers. Dissertation, Editorial Universitat Politècnica de València
38.
Behrends R et al (2016) HPC-GAP: engineering a 21st-century high-performance computer algebra system. Concurr Comput Pract Exp 28(13):3606–3636CrossRef
39.
Kasahara H et al (2012) Method for controlling heterogeneous multiprocessor and multigrain parallelizing compiler. US Patent 8,250,548, 21 Aug
40.
Marongiu A, Benini L (2012) An OpenMP compiler for efficient use of distributed scratchpad memory in MPSoCs. IEEE Trans Comput 61(2):222–236MathSciNetCrossRefMATH
41.
Engle C et al (2012) Shark: fast data analysis sing coarse-grained distributed memory. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. ACM
42.
Cruz EHM et al (2014) Dynamic thread mapping of shared memory applications by exploiting cache coherence protocols. J Parallel Distrib Comput 74(3):2215–2228CrossRef
43.
Habel R, Silber-Chaussumier F, Irigoin F (2013) Generating Efficient Parallel Programs for Distributed Memory Systems. Technical Report CRI/A-523, MINES ParisTech and Télécom SudParis
44.
Sim J et al (2012) A performance analysis framework for identifying potential benefits in GPGPU applications. ACM SIGPLAN Notices, vol 47, no 8. ACM
45.
Kaashoek MF (2015) Parallel computing and the OS. SOSP History Day 2015. ACM
46.
Bericht T, Darmstadt TH, Informatik F, Theel OE, Fleisch Br D (1996) A dynamic coherence protocol for distributed shared memory enforcing high data availability at low costs. IEEE Trans Parallel Distrib Syst 7(9):915–30CrossRef
47.
Yuan D et al (2014) Simple testing can prevent most critical failures: an analysis of production failures in distributed data-intensive systems. In: 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14)
48.
Medya S, Cherkasova L, Magalhaes G, Ozonat K, Padmanabha C, Sarma J, Sheikh I (2016) Towards performance and scalability analysis of distributed memory programs on large-scale clusters. In: Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering. ACM, pp 113–116
49.
He S et al (2013) A cost-aware region-level data placement scheme for hybrid parallel i/o systems. In: IEEE International Conference on Cluster Computing (CLUSTER). IEEE
50.
Susmit B (2014) The software architecture for efficient distributed interprocess communication in mobile distributed systems. J Grid Comput 12(4):615–635CrossRef
51.
Sharifi M, Mirtaheri SL, Khaneghah EM (2010) A dynamic framework for integrated management of all types of resources in P2P systems. J Supercomput 52(2):149–170CrossRef
52.
Khaneghah EM (2017) PMamut: runtime flexible resource management framework in scalable distributed system based on nature of request, demand and supply and federalism. US Patent 9,613,312, 4 Apr
53.
Musial P, Nicolaou N, Shvartsman AA (2014) Implementing distributed shared memory for dynamic networks. Commun ACM 57(6):88–98CrossRef
54.
Kim J, Vaidya NH (1997) A cost model for distributed shared memory using competitive update. In: Fourth International Conference on High-Performance Computing, Bangalore, India
55.
Gray J (1988) The cost of messages. In: Proceedings of the Seventh Annual ACM Symposium on Principles of Distributed Computing, Toronto, Ontario, Canada
56.
Kim J-H, Vaidya NH (1997) A cost model for distributed shared memory using competitive update. In: Proceedings of Fourth International Conference on High-Performance Computing. IEEE
57.
Li S et al (2015) An extensible framework for predictive analytics on cost and performance in the cloud. In: International Conference on Cloud Computing and Big Data (CCBD). IEEE
58.
Dave VS, Dutta K (2014) Neural network based models for software effort estimation: a review. Artif Intell Rev 42(2):295–307CrossRef
59.
Hassan HA, Mohamed SA, Sheta WM (2016) Scalability and communication performance of HPC on Azure Cloud. Egypt Inform J 17(2):175–182CrossRef
60.
Midgley G (ed) (2003) Systems thinking. Sage, London
61.
Thüm T et al (2014) A classification and survey of analysis strategies for software product lines. ACM Comput Surv (CSUR) 47(1):6CrossRef
62.
Metzger A, Pohl K (2014) Software product line engineering and variability management: achievements and challenges. In: Proceedings of the on Future of Software Engineering. ACM
63.
Sharifi M, Tirado-Ramos A, Khaneghah EM, Mirtaheri SL (2010) Formulating the real cost of dsm-inherent dependent parameters in HPC clusters. In: SMTP workshop in conjunction with the IEEE International Parallel & Distributed Processing Symposium (IPDPS 2010), 19 April
64.
Power R (2014) Abstractions for in-memory distributed computation. Dissertation, New York University
65.
Vasava, HD, Rathod JM (2015) Software based distributed shared memory (DSM) model using shared variables between multiprocessors. In: International Conference on Communications and Signal Processing (ICCSP). IEEE
66.
Maosen H, Wei H, Huang Y (2016) Enabling mobile device coordination over distributed shared memory. In: IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). IEEE
67.
Pelley S, Chen PM, Wenisch TF (2014) Memory persistency. In: 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA). IEEE
68.
Alglave J, Maranget L, Tautschnig M (2014) Herding cats: modelling, simulation, testing, and data mining for weak memory. ACM Trans Program Lang Syst (TOPLAS) 36(2):7CrossRef
69.
Ghosh S (2014) Distributed systems: an algorithmic approach. CRC Press, Boca Raton
70.
Kaxiras S et al (2015) Turning centralized coherence and distributed critical-section execution on their head: a new approach for scalable distributed shared memory. In: Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing. ACM
71.
Das D, Ray RS, Ray UK (2016) Implementation and consistency issues in distributed shared memory. Int J Comput Sci Eng 4(12):125
72.
Dulloor S R et al (2014) System software for persistent memory. In: Proceedings of the Ninth European Conference on Computer Systems. ACM
73.
74.
Javanbakht Z, Öchsner A (2017) Introduction to Marc/Mentat. In: Advanced finite element simulation with MSC Marc. Springer, Cham
75.
Shrivastava A et al (2016) Automatic management of software programmable memories in many-core architectures. IET Comput Digit Tech 10(6):288–298CrossRef
Metadaten
Titel
A mathematical model to calculate real cost/performance in software distributed shared memory on computing environments
verfasst von
Ehsan Mousavi Khaneghah
Nosratollah Shadnoush
Amir Hossein Ghobakhlou
Publikationsdatum
16.11.2017
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 4/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-017-2191-7

Weitere Artikel der Ausgabe 4/2018

The Journal of Supercomputing 4/2018 Zur Ausgabe

OriginalPaper

Data-type specific cache compression in GPGPUs

OriginalPaper

Pardis: a process calculus for parallel and distributed programming in Haskell

OriginalPaper

An efficient anonymous authentication protocol in multiple server communication networks (EAAM)

OriginalPaper

A hybrid MapReduce-based k-means clustering using genetic algorithm for distributed datasets

OriginalPaper

K-DT: a formal system for the evaluation of linear data dependence testing techniques

OriginalPaper

An energy-efficient 3D-stacked STT-RAM cache architecture for cloud processors: the effect on emerging scale-out workloads

Premium Partner

    Bildnachweise