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 267 articles )

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  1. Börger, Egon: The abstract state machines method for modular design and analysis of programming languages (2017)
  2. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  3. Yao, Zhigang; Eddy, William F.: A statistical approach to the inverse problem in magnetoencephalography (2014)
  4. Pizzi, Nick J.: A fuzzy classifier approach to estimating software quality (2013) ioport
  5. Shukla, K. K.; Tiwari, Arvind K.: Efficient algorithms for discrete wavelet transform. With applications to denoising and fuzzy inference systems (2013)
  6. Tu, Yijuan; Yeoh, Guan Heng; Liu, Chaoqun: Computational fluid dynamics. A practical approach. (2013)
  7. Díaz, M.J.Castro; Fernández-Nieto, E.: A class of computationally fast first order finite volume solvers: PVM methods (2012)
  8. Van Snyder, W.; Livesey, Nathaniel J.: Data analysis for the NASA EOS aura microwave limb sounder instrument (2011)
  9. Rauber, Thomas; Rünger, Gudula: Parallel programming for multicore and cluster systems (2010)
  10. Shih, Wen-Chung; Yang, Chao-Tung; Tseng, Shian-Shyong: Performance-based data distribution for data mining applications on grid computing environments (2010) ioport
  11. Yamamoto, Yoshikazu; Nakano, Junji; Fujiwara, Takeshi: Parallel computing in the statistical system Jasp (2010)
  12. Chen, Liang; Bairagi, Deepankar; Lin, Yuan: MCFX: a new parallel programming framework for multicore systems (2009) ioport
  13. Gerritsen, Margot G.; Löf, Henrik; Thiele, Marco R.: Parallel implementations of streamline simulators (2009)
  14. Kalivarapu, Vijay; Foo, Jung-Leng; Winer, Eliot: Synchronous parallelization of particle swarm optimization with digital pheromones (2009)
  15. Li, Kuan-Ching; Weng, Tien-Hsiung: Performance-based parallel application toolkit for high-performance clusters (2009) ioport
  16. Periaux, J.; Lee, D.S.; Gonzalez, L.F.; Srinivas, K.: Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual Nash strategies in a CFD design environment (2009)
  17. Shang, Yueqiang: A distributed memory parallel Gauss-seidel algorithm for linear algebraic systems (2009)
  18. Yang, Shouxi; Segre, Alberto Maria; Codenotti, Bruno: An optimal multiprocessor combinatorial auction solver (2009)
  19. Collette, Sébastien; Cucu, Liliana; Goossens, Joël: Integrating job parallelism in real-time scheduling theory (2008)
  20. Fox, Geoffrey C.; Aktas, Mehmet S.; Aydin, Galip; Gadgil, Harshawardhan; Pallickara, Shrideep; Pierce, Marlon E.; Sayar, Ahmet: Algorithms and the grid (2008) ioport

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