sleptsov net computing - persons.iis.nsk.su
TRANSCRIPT
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Sleptsov Net Computing
Dmitry Zaitsev http://member.acm.org/~daze
International Humanitarian University
http://mgu.edu.ua
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Write Programs
Draw Programs?
or
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Flow charts
• Process Charts, Frank and Lillian Gilbreth, 1921
• ASME Standard: Operation and Flow Process Charts, 1947
• Planning and coding of problems for an electronic computing instrument, Part II, Volume 1, 1947, Herman Goldstine and John von Neumann
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Frank and Lillian Gilbreth example
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Program schemata
Martiniuk, 1961
Yanov, 1958
Lavrov, 1961
Functional equivalence and optimization, Lyapunov A.A., Ershov, 1950-1970
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R-technology of programming
Ukraine, V.M. Glushkov, I.V. Velbitsky, 1970-1990
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Modern Visual Programming
• DRAKON - the Buran space project
• Microsoft Visual Programming Language, MVPL
• Scratch for Android
• Node-RED
• Ardublock
• DGLux5
• AT&T Flow Designer
• ReactiveBlocks
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DRAKON
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UML
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Programming on Petri nets
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Path to a uniform concept
• Textual programming
• Graphs loaded by textual language
• Pure graphical programming - nothing save graphs
• Inhibitor Sleptsov net – fast universal language of concurrent programming
• Massively parallel computations
• Fine granulation
• Computing memory implementation
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Transition firing strategy
• Petri
– a single transition at a step
• Salwicki
– the maximal firing strategy
• Sleptsov
– the multiple firing strategy
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Sleptsov net vs Petri net
2t1
t1 t1
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Sleptsov Net – Multiple Firing
Petri net Sleptsov net
Reachability graphs
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Sleptsov Nets Run Fast
COMPARING TIME COMPLEXITIES OF OPERATIONS (LINEAR SCALE - NUMBER OF STEPS)
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I. Peculiarities of programming in SNs
A. Reversed control flow (c) B. Using inhibitor arcs to control a transition firing
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Basic operators
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Basic subnets (subroutines)
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Fast multiplication in Sleptsov nets
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Work with variables
f s
Output variables . . .
Input variables . . .
Operator (procedure)
Operator
Input variables
s
Output variables
COPY x
CLEAN f
MOV
E f
f/s
y
s
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Expansion of dashed/dotted arcs
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Subnets (routines) calls
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Sleptsov Net Paradigm of Computing
Input data
(SN marking)
Processor
of
inhibitor
Sleptsov
nets
Output
data
Loaded
SN
Compiler
of loaded
SNs into
plain SN
Plain
inhibitor
SN
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Universal Sleptsov Net Concept
Given Sleptsov net
Initial marking
Final marking
Transitions firing sequence
Universal
Sleptsov net
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Explicitly Constructed Universal Petri/Sleptsov Nets
Year Technique Size (nodes)
Time complexity
2010 Direct simulation of inhibitor PN by inhibitor PN 1000 polynomial
2010 Simulation of a given Turing machine by deterministic inhibitor PN (DIPN)
1000 exponential
2011 Simulation of a given Markov normal algorithm by DIPN 1000 exponential
2013 Simulation of small universal Turing machine by DIPN 56 exponential
2013 Simulation of weak small universal Turing machine by DIPN
43 exponential
2015 Simulation of cellular automaton Rule 110 by infinite PN 21 ∙ 𝑛 polynomial
2015 Simulation of Turing machine that simulates Rule 110 by infinite PN
14 ∙ 𝑛 polynomial
2017 Simulation of weak small universal Turing machine by Sleptsov net
39 polynomial
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II. Direct simulation of an inhibitor PN by an inhibitor PN
Encoding by 10 scalar variables
=
Places of UIPN
State equation
=
Formal description
of IPN dynamics
Inhibitor Petri Net
(IPN) =
2 integers: m,n; 2 matrices: B,D;
1 vector: Q0
Algorithm AUIPN
of IPN dynamics with respect
to the encoding
in C-like pseudo
language
UIPN
=
Algorithm AUIPN
of IPN dynamics
in IPN
Transitions firing sequence Z, its length k, marking Q
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Formal Representation of Inhibitor Petri Net behavior
State equation
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Encodings
𝒔 = 𝝋 𝑸 = 𝒓𝒋 ∙ 𝒒𝒋
𝒎−𝟏
𝒋=𝟎
, 𝒓 = 𝐦𝐚𝐱𝒋𝒒𝒋 + 𝟏
𝒔 = 𝝋 𝑨 = 𝒓(𝒎⋅𝒊+𝒋) ⋅ 𝒂𝒊,𝒋
𝒎−𝟏
𝒋=𝟎
,
𝒏−𝟏
𝒊=𝟎
𝒓 = 𝐦𝐚𝐱𝒊,𝒋𝒂𝒊,𝒋 + 𝟏
𝒔𝒗 = 𝒔𝒗−𝟏 ⋅ 𝒓 + 𝒂𝒊,𝒋, 𝒔𝟎 = 𝒂𝒏−𝟏,𝒎−𝟏
𝒂𝒊,𝒋 = 𝒔𝒏∙𝒎−𝟏−𝒗 𝒎𝒐𝒅 𝒓, 𝒔𝒏∙𝒎−𝟏−(𝒗+𝟏) = 𝒔𝒏∙𝒎−𝟏−𝒗 𝒅𝒊𝒗 𝒓, 𝒔𝒏∙𝒎−𝟏 = 𝒔
Encoding of a vector (marking)
Encoding of a matrix (incidence)
Recursive encoding
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Composition of data and control flow
n
n
rB
rB
sD
sD
rD
rD
m
m
sB
sB
sQ
sQ
rQ
rQ
sZ
sZ
k
k
start
Variables
Control flow finish
Code of a given Petri net
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UIPN
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CheckFire
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PickFire
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Fire
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Examples of nets encoding
Net m n sB rB sD rD
ADD 6 4 21180169496 3 282946 2
MAX 8 8 254813592433189871074065241412 3 293862152152879368 2
MUL 10 9 646549072061101455668889034663481743952654 3 19352259085292454555975681 2
Net Marking 𝑸 sQ rQ
ADD ADDQ0 (2,3,1,0,0,0) 2880 4
ADD ADDQ (0,0,0,5,1,0) 186 6
MAX MAXQ0 (2,3,1,0,0,0,0,0) 46080 4
MAX MAXQ (0,0,0,3,1,0,0,0) 832 4
MUL MULQ0 (2,3,1,0,0,0,0,0,0,0) 737280 4
MUL MULQ (0,0,0,6,1,0,0,0,0,0) 722701 7
Petri net graph
Marking
Transitions firing sequence
Net Q0 Q Z sZ k
ADD ADDQ0 ADDQ t1,t3,t2,t2,t3,t3,t4 2411 7
MAX MAXQ0 MAXQ t1,t2,t2,t6,t7,t8 4983 6
MUL MULQ0 MULQ t1,t2,t4,t4,t5,t6,t6,t7,t2,
t4,t4,t5,t6,t6,t7,t2,t4,t4,
t5,t6,t6,t7,t3,t9,t9,t8
109815712212339723705298 26
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II. UPN(14,29)
Neary and Woods’s
weakly universal
Turing machine
with
2 states and
4 symbols WUTM(2,4)
Universal
(deterministic
inhibitor)
Petri net
with
14 places and
29 transitions UPN(14,29)
Directly
simulates
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Chains of translations
UPN(14,29) TM CTS CA DIPN BTS
WUTM(2,4) TM CTS CA
Simulates
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Encoding of states, symbols, and transition function
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Tape encoding
1
0
021 )()...(l
i
iill rxsxxxs
x R0
R1
…
Rk-1
L0
L1
…
Ll-1
)...( 021 LLLsL ll )...( 021 RRRsR ll )(xsX
L L X X R R
Encoding function:
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Encoding of blank words
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General arrangement of UPN(14,29)
t1
t2
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Simulating TM instruction
an instruction with the left move an instruction with the right move
(2,0,4,left,1) (4,1,2,right,1)
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Subnet FS simulating WUTM(2,4) transition function
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Subnet MA5LR
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Subnet MD5LR
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UPN(14,29) in graphical form
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Trace of UPN(14,29) running
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III. Simulating Linear Cellular Automaton 110
i i+1 i+2 i+3 i+4 i+5 i+6 i+7 i-1 i-2 i-3 i-4 i-5 i-6 i-7
(1)
i i+1 i+2 i+3 i+4 i+5 i+6 i+7 i-1 i-2 i-3 i-4 i-5 i-6 i-7
Rules 110:
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Minimization of CA110 function
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Synchronous PN with Inhibitor and Read Arcs
Parametric expression (PE):
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Expanding Traversals of the Cell Array for CA110 Simulation by Asynchronous Nets
0
m=3 n=2
k=0
k=1
k=2
k=3
k=4
…
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DSi – Calculate the State Difference
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CSi – Change the Cell State
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Parametric Specification of DSi and CSi
DSi
CSi
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Boomerang and Barriers of UPN(9,12,Inf)
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Boomerang and Barriers Net Picture
An example for m = 1, n = 2 The fragment represents 8 cells
∞
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Boomerang and Barriers Net
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Visual Simulation of Ether
The left word “0001”, central word “01110”, and right word “111110”; m = 4 and n = 0 interchanges with n = 6
The left word “1001” and the right word “1011111000”; m = 4 and n = 10
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Simulating TMs which Simulate CA110
Neary & Woods’ weakly universal TM with 2 states and 4 symbols WUTM(2,4)
The left blank word:
The right blank word:
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Model of WUTM(2,4) Cell
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Model of WUTM(2,4) – UPN(6,8,inf)
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IV. Universal Sleptsov Net USN(13,26)
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Transition function subnet TF
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Head move subnet HM
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USN(13,26) Final
Assembly
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V. Examples of SN programs: RSA encoding/decoding
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Examples of SN programs:
Solving Laplace equation
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Examples of SN programs: Computing Fuzzy Logic Function
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Examples of SN programs: Discrete-Time Linear Control in Two Tacts
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Conclusions
• Petri nets run exponentially slower with regard to Turing machines while Sleptsov nets run fast
• Universal Sleptsov net is a prototype of a processor in the Sleptsov net paradigm of computing; the smallest contain 39 nodes and run in polynomial time
• Sleptsov net computing offers: graphical concurrent language, formal verification of concurrent programs, fine granulation of parallel processes, massively parallel computations
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Basic References • Zaitsev D.A. Universal Sleptsov Net, International Journal of Computer
Mathematics, 2017 • Zaitsev D.A. Simulating Cellular Automata by Infinite Petri Nets, Journal
of Cellular Automata, 2017 • Zaitsev D.A., Jürjens J. Programming in the Sleptsov Net Language for
Systems Control, Advances in Mechanical Engineering, 2016, Vol. 8(4), 1–11.
• Zaitsev D.A. Sleptsov Nets Run Fast, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, Vol. 46(5), 682–693.
• Zaitsev D.A. Paradigm of Computations on the Petri Nets, Automation and Remote Control, 2014, Vol. 75(8), 1369–1383.
• Zaitsev D.A. Toward the Minimal Universal Petri Net, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014, Vol. 44(1), 47–58.
• Zaitsev D.A. Inhibitor Petri Net Executing an Arbitrary Given Markov Normal Algorithm, Automatic Control and Computer Sciences, 2012, Vol. 46(7), 345–355.
• Zaitsev D.A. Universal Petri net, Cybernetics and Systems Analysis, 2012, Vol. 48(4), 498–511.
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#SleptsovNets
#СетиСлепцова