PVM (Parallel Virtual Machine) is a software package that permits a heterogeneous collection of Unix and/or Windows computers hooked together by a network to be used as a single large parallel computer. Thus large computational problems can be solved more cost effectively by using the aggregate power and memory of many computers. The software is very portable. The source, which is available free thru netlib, has been compiled on everything from laptops to CRAYs. PVM enables users to exploit their existing computer hardware to solve much larger problems at minimal additional cost. Hundreds of sites around the world are using PVM to solve important scientific, industrial, and medical problems in addition to PVM’s use as an educational tool to teach parallel programming. With tens of thousands of users, PVM has become the de facto standard for distributed computing world-wide.

References in zbMATH (referenced in 308 articles )

Showing results 1 to 20 of 308.
Sorted by year (citations)

1 2 3 ... 14 15 16 next

  1. Gimeno, Joan; Jorba, Àngel; Nicolás, Begoña; Olmedo, Estrella: Numerical computation of high-order expansions of invariant manifolds of high-dimensional tori (2022)
  2. Agullo, Emmanuel; Cools, Siegfried; Yetkin, Emrullah Fatih; Giraud, Luc; Schenkels, Nick; Vanroose, Wim: On soft errors in the conjugate gradient method: sensitivity and robust numerical detection (2020)
  3. Hope, Gaute; Schmidt, Henrik: A parallelization of the wavenumber integration acoustic modelling package OASES (2019)
  4. Börger, Egon: The abstract state machines method for modular design and analysis of programming languages (2017)
  5. Gentle, James E.: Matrix algebra. Theory, computations and applications in statistics (2017)
  6. Tran, An C.; Dietrich, Jens; Guesgen, Hans W.; Marsland, Stephen: Parallel symmetric class expression learning (2017)
  7. Erciyes, K.: Distributed and sequential algorithms for bioinformatics (2015)
  8. Li, Qin; Zhao, Yongxin; Zhu, Huibiao; He, Jifeng: A UTP semantic model for Orc language with execution status and fault handling (2014)
  9. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  10. Yao, Zhigang; Eddy, William F.: A statistical approach to the inverse problem in magnetoencephalography (2014)
  11. Pizzi, Nick J.: A fuzzy classifier approach to estimating software quality (2013) ioport
  12. Shukla, K. K.; Tiwari, Arvind K.: Efficient algorithms for discrete wavelet transform. With applications to denoising and fuzzy inference systems (2013)
  13. Tu, Yijuan; Yeoh, Guan Heng; Liu, Chaoqun: Computational fluid dynamics. A practical approach. (2013)
  14. Castro Díaz, M. J.; Fernández-Nieto, E.: A class of computationally fast first order finite volume solvers: PVM methods (2012)
  15. Kulich, Tomáš: The diameter of a random subgraph of the hypercube (2012)
  16. Van Snyder, W.; Livesey, Nathaniel J.: Data analysis for the NASA EOS aura microwave limb sounder instrument (2011)
  17. Rauber, Thomas; Rünger, Gudula: Parallel programming for multicore and cluster systems (2010)
  18. Shih, Wen-Chung; Yang, Chao-Tung; Tseng, Shian-Shyong: Performance-based data distribution for data mining applications on grid computing environments (2010) ioport
  19. Yamamoto, Yoshikazu; Nakano, Junji; Fujiwara, Takeshi: Parallel computing in the statistical system Jasp (2010)
  20. Chen, Liang; Bairagi, Deepankar; Lin, Yuan: MCFX: a new parallel programming framework for multicore systems (2009) ioport

1 2 3 ... 14 15 16 next