Sleptsov Net Computing Resolves Problems of Modern Supercomputing Revealed in Turing Award Talk

By IGI Global on Jul 19, 2023
Prof. Dmitry A. Zaitsev's groundbreaking research has found practical applications across numerous industries. One of his most notable contributions is the pioneering work on "Sleptsov Net Computing," which was brought to light during Seminal Turing Award Talk in 2021. As a distinguished laureate of the prestigious Turing Award, Prof. Zaitsev's expertise in this field is evident, and his innovative approach has been recognized as a significant breakthrough in the world of high-performance computing.

Dmitry A. ZaitsevSeminal Turing Award Talk of 2021 laureate of Turing Award Jack Dongarra to the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22) sheds new light on the problems of modern supercomputers development. Jack Dongarra’s deep analysis of the supercomputers efficiency implies a conclusion that the architecture of the most powerful computers in the world is not completely adjusted for solving practical tasks in challenging application domains. In this situation, Slepsov Net Computing looks as a promising approach capable to provide a breakthrough in the issues of modern supercomputers efficiency.
For more than two decades, efficiency of supercomputers has been measured solving big dense random linear systems using LINPACK library developed by Jack Dongarra team. In his Turing Award talk, Jack Dongarra calls LINPACK a yardstick of high performance computing. Using this yardstick, a series of supercompures in USA and other countries has been developed, including the most powerful computer in the world at the present time – Frontier of Oak Ridge Laboratory, USA. Remind that, performance of computers is measured in FLOPS – floating point operations per second, and Frontier breaks exaflops barrier that amounts to ten to the eighteenth power flops. In the final part of his talk, Jack Dongarra reveals recently obtained information on efficiency of the best computers, mentioned in Top500 list, during solving real-life tasks in manifold application domains basically represented by simulators which lead to solving sparse linear systems. Frontier and many other top computers show efficiency about 0.8% that means Frontier has real performance about ten petaflops instead of an exaflops. Only Japanese computer Fugaku of Fujitsu, occupying recently the second place, reaches 30% efficiency.
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Indeed, supercomputing is not so simple as cutting up plots of land to measure them with a single yardstick but requires a complex balanced approach of using a mixture of simulators for the top significant application domains. Dense systems provide regular load on processors and communication subsystem filling the cash well to smooth out the processor-memory bottleneck. Real life tasks, represented by simulators and sparse systems, require irregular communication pattern that is implemented rather well by the Fugaku’s six dimensional torus Tofu D Interconnect. Thus, design of communication subsystems in the form of a multidimensional torus represents a prospective direction of research.
"During my life I was lucky to find the best teachers, more precisely, I was lucky they, Anatoly Sleptsov in 1988, and Jack Dongarra in 2017, chose me as an apprentice. I learned much on concurrent processes and place-transition nets from Anatoly Sleptsov, then he directed me to practical implementation of our theoretical results at Topaz plant, and Opera-Topaz was born in 1990 before such nets were called a 'workflow' and the speed-up principle was called an 'exhaustive use of rule'. Jack Dongarra introduced me to the miraculous world of supercomputers and directed me through the process of my clan theory implementation on modern parallel and distributed architecture, joint software issued, join papers published. I devoted my ode to Jack Dongarra and I like to believe that our joint work switched compass of his research a bit more towards sparse computations. " —Dmitry A. Zaitsev, Odessa State Enviromental University, Ukraine
Sleptsov Net Computing goes further; it uses computing memory, represented as two and higher dimension structures, when spatial peculiarities of modeled systems are taken into consideration. Doing computations in memory allows us to get rid of the traditional processor-memory bottleneck problem. Instead of sophisticated conventional architecture to utilize multicore CPUs and GPUs of distributed nodes, for instance with OpenMP-CUDA-MPI set tools, Sleptsov Net Computing offers homogenous concept of a unified graphical language for concurrent programming with very fine granularity of parallel computations. As grown from place-transition net theory, Sleptsov net inherits developed methods to analyses and prove correctness of parallel programs in the process of their model-driven development. Implementation of Sleptsov net processors in the form a matrix of computing memory leads to the nanosecond tack of time of massively parallel computations that means good ability to control hypersonic objects in real-time applications.
Software reliability is a separate issue. Let us imagine that reliability of a bulk of software operating in the world is based on the belief that it does not contain an error or rather contains an error with low probability. The belief is supported by a certain number of successful tests on which a program in question works correctly. Such software is embedded in cars’ and planes’ onboard computers, to make us believe in low probability of an accident, while we prove correctness of concurrent Sleptsov net programs in a formal way.
Recently, two master students Qing Zhang and Hongfei Zhao from XIDIAN University, Xi’an, China implemented software prototypes of a Sleptsov net processor and a compiler-linker of Sleptsov net programs and presented them at the International Conference “Problems of Infocommunications. Science and Technology” (PIC S&T′2022), 10 -12 October, Borys Grinchenko Kyiv University, Ukraine. We invite investors to start-up the project of enterprise level implementation of Sleptsov net computing paradigm, including hardware implementation of Sleptsov net processor and computer. To integrate it initially into the conventional infrastructure, we attach it, as an extension device, to a traditional computer, how it was mentioned in Jack Dongarra talk as one of prospective directions for further developing modern architecture of HPC.
Besides, at the present time, an SNC full-scale project background has been well prepared by manifold keynote talks and dedicated lectures as well as with about a dozen of publications, including Special issue Petri/Sleptsov net based technology of programming for parallel, emergent and distributed systems of International Journal of Parallel, Emergent and Distributed Systems.
a)  a)  Cybersecurity: Open key RSA encryption/decryption
a) Cybersecurity: Open key RSA encryption/decryption
b)  Fuzzy control: Computing fuzzy logic function
b) Fuzzy control: Computing fuzzy logic function
Chapter 122 of Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics (2 Volumes), "Sleptsov Net Computing" presentes motivation for new models of hyper-computations. Sleptsov net was introduced compared to Petri and Salwicki nets. A concept of universal Sleptsov net, as a prototype of a processor in Sleptsov net computing, was discussed.
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About the Chapter Author
Dmitry A. Zaitsev received the Eng. degree in applied mathematics from Donetsk Polytechnic Institute, Donetsk, Ukraine, in 1986, the Ph.D. degree in automated control from the Kiev Institute of Cybernetics, Kiev, Ukraine, in 1991, and the Dr.Sc. degree in telecommunications from the Odessa National Academy of Telecommunications,Odessa, Ukraine, in 2006. He published more than a hundred papers, 2 books, and 2 book chapters. He developed the universal Petri nets, the analysis of infinite Petri nets with regular structure, the decomposition of Petri nets in clans, and the method of synthesis of fuzzy logic function given by tables. He is the ACM Senior Member.
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