R-technology of programming: Basic notions and implementation

Vel'bitsky, I. V.; Kovalev, A. L.; Kasatkina, Iryna; Wang, Lei

doi:10.1007/bf02943551

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Conventional programming technology [42] uses a compiler to convert a program, written in an algorithmic language (source code), into machine code with unresolved external references (object code), then a linker, processing object files and libraries, allocates object files and resolves external references to produce an executable program in machine language. Recent update is represented by graphical design with UML [19] that further develops principles of R-technology of programming [41] with respect to the object-oriented design.…”

Section: Compiler-linker Of Snsmentioning

confidence: 99%

Virtual Machine and Integrated Developer Environment for Sleptsov Net Computing

Zaitsev

Shmeleva

Zhang

et al. 2023

Parallel Process. Lett.

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Modern computing is a path of violations and transformations coming from an intrinsically concurrent application domain into a sequence of instructions and then back to concurrency with OpenMP, MPI and CUDA/OpenCL. Why we create so many difficulties? Sleptsov Net Computing (SNC) maps a task into an appropriate computing structure implemented as a re-configurable multidimensional sparse matrix of computing memory. It has entirely graphical mass parallel language for concurrent programming and a framework of techniques for concurrent program verification to develop reliable software. Estimated efficiency of SNC is higher than 50% compared to actual less that 1% efficiency of the most powerful supercomputers. It yields hyper-performance capable of efficient control of hyper-sonic objects, colliders, thermonuclear reaction. This paper presents an open source prototype VM and IDE for SNC with a view on upcoming hardware implementation of the corresponding computer.

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Section: Compiler-linker Of Snsmentioning

confidence: 99%

Virtual Machine and Integrated Developer Environment for Sleptsov Net Computing

Zaitsev

Shmeleva

Zhang

et al. 2023

Parallel Process. Lett.

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Notation for mass parallel algorithms: computing Petri net state space on GPU case study

Zaitsev,

Zhang,

Liu

et al. 2024

International Journal of Parallel, Emergent and Distributed Sys

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GPU Based Virtual Machine for Sleptsov Net Computing

Shmeleva,

Zaitsev,

Retschitzegger

2024

Parallel Process. Lett.

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Novel paradigm of Sleptsov Net Computing (SNC) mends imperfections of modern HPC architecture with computing memory implementation and provides a vivid graphical language, fine granulation of concurrent processes, and wide application of formal methods for reliable software design. IDE and VM for SNC have been developed and described in early papers. In the present paper, we considerably reduce GPU memory and thread consumption of the previous prototype implementation, introducing a matrix with condensed columns (MCC), and enhance performance, using the first fireable transition choice on the transition sequence reordered according to the lattice of priorities. MCC takes into consideration procedures of Sleptsov net (SN) arcs processing to enhance performance with rather little overhead compared to traditional sparse matrix formats. We represent a matrix of SN arcs by a pair of considerably smaller matrices, having the same number of columns, with the number of rows equal to the maximal number of nonzero elements over the source matrix columns. The first matrix contains the indexes within the source matrix, the second matrix contains the values within the source matrix. To run an SN, we use the corresponding rectangular matrix of GPU threads, with very few indirect memory accesses via MCC. As a result, we increase VM performance in 2–4 times, and reduce by hundred times GPU memory and thread consumption on programs from the SN software collection.

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R-technology of programming: Basic notions and implementation

Cited by 3 publications

References 4 publications

Virtual Machine and Integrated Developer Environment for Sleptsov Net Computing

Virtual Machine and Integrated Developer Environment for Sleptsov Net Computing

Notation for mass parallel algorithms: computing Petri net state space on GPU case study

GPU Based Virtual Machine for Sleptsov Net Computing

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