workload proceeding
TRANSCRIPT
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Discrete event simulation based on standard workload involving[ typedef map< char , unknown x > CompressMap ]
Author:
Said Mchaaliadraft copy septum 19th 2012
1. Abstract:
The study and design of computer systems require well database discovering gathering information regarding
the standard workload to which these system designs are subjected to be whose aim objects, because the
workload has a decisive effect on the observed performance [1,2,3,4,8,19].
During digital system design, parallel job scheduling and timing simulation are questions for intentional
integrated large scale cluster and supercomputer[19]. Hence, in such thread entities, the centric metric
measurable unit is the number of scheduling job per unit core proceeding processing [1,19].
Although, this standard workload measurement unit could as defined in Said Mchaalia works [8,10] be
converted to keymotor flows based on frequency valuelevel variations. Indeed, standard workload design
systems start from foundation and production of electrical brain powerfulness fountain flows.
Keywords:
Electricity proceeding processing, keymotor flows, valuelevel variation, control data flow graph manufacturing
methodology techniques.
2. Introduction:
Electricity is basic to power any discrete event simulation, whereby the dimension of electrons could be the
object of the best philosophy branch filed in advanced computer sciences and physics electricity environmentdynamism.Figure 001, presents the keymotor flow of any discrete event simulation. Therefore, discrete event
simulation basics need event occurrence inputs and event occurrence outputs to be identified during searching
studying sign systems of signals inside any core processing units. Thus, as shown in figure 001, the first discrete
event simulation signal inputs inside any modeling system for simulation requirement is the just data coming in
input buffers. Indeed, leaflike water motor flow, the source of water production is not yet defined. Thus, based
on hydro language electricity productions, the first fundamental following flow is the differential values in watervariation levels. Hence, the interruption of the mechanical system to get motion and by this way the first step in
just language of frequencies. Where this just language of frequencies is defined to be the number of tours per just
language of one second time event value. Although, the intellectual integrated intentional input is the language
(seefigure 001 for more details), which is defined as couple of just language of two operands, such that thetimeevent is a secret system sign, Therefore, in some synchronization significations of signalizing discrete event
simulation models, the timeevent secret sign system has to be defined as frequency synchronization. Event thoughthe valueevent secret sign system has to be defined as instantaneous event occurrence values. Hence, in the
example of hydro language electricity productions, the just data coming in, is water depth performance.
Furthermore, timeevent secret sign system is frequency synchronization of water depth amount quantity. Moreover
valueevent secret sign system is the instantaneous sinusoidal following description function of the turbine.
On the other hand, the background offigure 01 is to define and clarify civilization based on dictionary language
as Lemepel and Ziv did it, whereby whose works did achieve sequential digital data encoding and waveform
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compression. Therefore, a civilization is a system in advanced progress and achievement of any enlivenment
development involution inside dynamics environment mechanism. Hence, Archimedes laws, were background
database information of many scientific true right postulates, such that any wood flat surface could be swim on
any water volume design. Therefore, apply Newton's law to Archimedes law, intentional secret sign is that the
Earth's center is a kernel surface and could never be a just language of one kernel point.Figure 002 illustrates
the main original principles of discrete event simulation based on Archimedes law. Because, just language of
hydro electrical production productivity is original main sufficient suitable sophistical clean clear energy-to-light
proceeding processing.
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Figure 001: principles of discrete event simulation compile-compute-conclude processing analysis
Figure 001presents discrete event transaction core processor design, where the incoming in data are signal
transactions [3].
Figure 002: discrete event simulation based on Archimedes laws.
Figure 002 shows the discrete event simulation principles based on Archimedes laws, whereby any wood flat
surface-form could be used to achieve this discrete event simulation modeling and design,
In fact a selfish set of flat surfaceform woods could be defined for any instantaneous instant time event value.Though, a global simulation time is required. Thus, a global clock or timer, which could be bringing to a flat
surface on the Earth's Moon, because it is just language of one Earth's Moon for all alive active human persons
inside Earth.
3. Light from transistor basic logic influence system:
In fact, chemical substances are natural keymotor flow for anything involving water, which is root creationism of
electrical hydro language. Hence, transistor chemical substances are basic logic influence system of semi-
conductor, whereby electrical current flows are not clear defined. The most around semi-conductors used within
Integrated circuits.
Even though, doping silicon or geranium to create transistor is intentional signalizing for studying core
proceeding and processing, whereby just language of logic and arithmetic operations are implemented.
Therefore, this just language of logic and arithmetic operations are measurable units in binary bit registers and
control data flow graphs, whereby parallel scheduling jobs [1,4,5,7, 19] appear to be optimized for any core
make neutralism language.
Hence, for most around transistor applications, current digit displays are light emit diodes.
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Figure 003: three pin semi-conductor switcher laws. Figure 004: original main hertz counter.
Figure 005: two pin semi-conductor switcher laws.Figure 006: overview viewpoint of electricity production.
In fact, in hertz definition [11], the counting is the basic logic influence system ofHzmeasurement unit. Indeed,
Hz measurable amount quantity (echos waveform during modeling-simulation) is a number of tours per just
language of one second time. Therefore, converting the number of tours for any movable mechanical system,
require mathematical background. In Greece reading and Archimedes laws, this number of tours per second time
could be easy converted to number of sines or cosines or sinusoidal similar periodic mathematical following
description functions. Even though, transistor switching number could be then used to depict this sinusoidalsimilar periodic mathematical following description functions. Furthermore, as leaflike locking transistor selfish
set, which could be easy to count the number of any sinusoidal periodic mathematical following description
functions, the 555 trigger basics circuitdescribed infigure 007.
Figure 007: basic transistor 555 trigger circuit.
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Figure 007illustrates the involution of transistor inside triggering opcodes, whereby the next step in this kind of
simulation is to define the clock or the exactly time of discrete event occurrences.
Although, in figure 006, the turbine has periodically sinusoidal movement waveform, which could be easy
converted to {(onevent, offevent)}index throughout a digit sensor of rotation motion. Therefore the brain powerfulness
of digital-analog design in this kind of discrete event simulation is to invent and evolved a powerful sensor for
exactly delivering gathering number of tours of the production turbine per just language of one second time
value unit.
Indeed, from background work in frequency modeling cycle basics synchronization timer[6], requires
fundamental mathematical functional description flow of envisaged signification synchronization timer, which
could be then evolved inside the co-core processor to synchronize itself the cycle core basics proceeding
processing.
Figure 008: co-core processor design to capture turbine rotation velocity in order to transfer it to triggering signal
Figure 008: co-core processor design to capture turbine rotation velocity in order to transfer it to triggering signal,
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which could then used as 555 ICincoming input to output designed square waveforms, whereby this square
waveform could be then used inside discrete event simulation core signification synchronization.
4. Verification methodologies for deep-sub-micron circuits:
During last decade micro-electronics devices have extended for functionality of many consumer and industry
production dramatically. This tend is partly due to the continuously increasing complexity digital design circuits.However, this complexity also adds a significant burden to the design verification phase of digital circuits.
In particular timing and functional verification are important parts in the validation of digital systems, especially
digital designs. Due to the above mentioned complexity of those designs the validation phase frequently is the
most time consuming task. This statement is especially true for future generation of digital design chips, which
will integrate more than 500 million transistor on a single die (deep submicron designs). Because of thiscomplexity, exhaustive functional verification is the only way to test the functionality of the design. Further, post
layout timing verification must ensure that all elements on the chip are meeting the projected timing conditions.
Unfortunately, the huge amount of active and passive elements within deep submicron designs will push current
verification methodologies and tools to and beyond their limits. Presently, verification efforts can be divided into
three categories; which are dynamic analysis, static analysis and formal verification.
Thus, dynamic analysis is also known as simulation. This techniques is very common and there are a lot ofcommercial tools available. However, usual uni-processor simulation platforms often reach their performance
limits dealing with large design. While special methods are capable of reaching high simulation throughout this
performance gain achieved by neglecting important timing information. Furthermore, static tools are usually
used to check timing of digital design. They are computational less expensive but require manual support from
designers. Unfortunately, this may be significant drawback when chip complexity increases.
Finally, formal verification tools verify design aim objects based on formal description of digital circuits.
Although, due to involution complexity of implemented algorithms only designs of small to moderate size can
be handled by these tools efficiently at this time. Alternatively, parallel timing simulation [1,4,5,6,7] on
computer (whose abstract review shown infigure 009) has been suggested to speed up functional and timing
simulation. However, parallel simulation is difficult thread task due to sequential digital data encoding and
compression [2], whereby dictionary techniques dictate orders in computational have to be exceededrepresentation. Consequently, it is often not possible to reach intentional speedup gain. Hence,figure 010 depict
the proposal famous fundamental fatal fast following cycle basics for parallel timing simulation [10]. Figure
010 depicts an electrical circuit of the phenomena of light emission. Light could only emitted and transmitted
using Light Emit Diodes and light bulbs or similarly. Therefore, the measurement amount quantities of lights and
whose velocity is depending on those involving tools, such electrical energy-to-light converter toolboxes.
Indeed, these energy-to-light converter toolboxes allow light motion anywhere. The most around application is
the disco emit light gaming-operations, optical-fiber data transmission, electrical arcs, the color of Earth's Sky at
night and so on, whereby the movable distance of the produced light could be may be attained 400000 meters or
more in the next high-tech light bulb production processing. In fact, the motion of light is hard thread-task to be
achieved. Nevertheless, the true right light motion velocity is original main sufficient suitable organization flow
for researchers in electrical branch and field's disciplines, whereby the true right definitions of velocity should
have been integrated within any light motion phenomena study. The insight of logic processing analysis of lightmotion velocity with source energy batteries of cars and trucks, is the distance variations belong to this light
velocity motion mechanism from 1 meter (car rear-light) to 300 meter bright light (headlight of car). Hereby, to
calculate light motion velocity, the time event value would be needed. For example, for10 seconds time, the
above detailed distances could so easy reached (1 meter to 300 meters). By this way, the light motion velocity,
which is defined as: velocity=distance(t)
t, proves that the light motion velocity could never be constant and
till now not yet reached the C, C=2.99x108m/s value identified by Einstein and co. The most around
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intense light motion velocity is the electrical arc motions' production within the insight of high power voltages.
Figure 009: basic principal primordial structure of core proceeding processing unit (computer)
Figure 009 shows the basic principal primordial structure of core proceeding processing unit (computer),
whereby fast functional parallel timing simulation and parallel scheduling job simulations are aim object of any
inside research environment dynamism. Therefore, to speed up cycle based timing simulation and then the
parallel scheduling jobs, a possible proposal high frequency timer, which is shown infigure 010 could be then
implemented inside circuit to generated clock triggering waveform from just language of usage inductor-
capacitor filter, laser light emit diode, laser light receive transistor and 555 IC.
Thus, based on advancedNASA works, the visible like light as bright laser light is a gigs hertz rotationmotion
into fundamental following frequency flow. Therefore,figure 011 illustrates natural visible light transmission-reception-absorption phenomena. Although, primordial problem is to synchronize principally frequent reading
bytes and writing into byte-matrix with core processor frequency. Hence, the original main sufficient suitable
definition of frequency is the number of sinusoidal periodically following description function of core processor,
whereby parallel timing simulation [1] and scheduling jobs [10] take places. Furthermore, speeding upfunctional timing simulation inside core processor is aim object of discrete event simulation. By this way, Said
Mchaalia[10] did investigate the complexity of synchronizing signalizing searching studying core processor
opcodes (see page 9 in reference [10]) to significant sophistical global timer frequency, which is issued from
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laser light diode. Indeed,figure 010 depicts an idea of using this laser like light to proceed timing simulation and
parallel scheduling job (pipelines, which are following event occurrences: ready to be executed, in queue,
fetched (picked up from queue), ran(scheduled), stored back into byte-matrix) inside core processors.
Lighttransmission and magnetic reception into LRD Lightabsorption and magnetic amplification
Figure 010: basic principal primordial structure of light signalizing frequency signification synchronization
As reference application for light motion velocity, the primordial thread-task production is to take the beam
bicycle lighting production, which is based on the physical power that could be involved within such an
organization flow. When this power is less ore mere, the produced beam bicycle light is not clear bright, and
when this power becomes stronger, the produced beam bicycle light becomes intense bright. The mathematicalformat of the energy inside such a thread-task processing, is the integration of produced power within
instantaneous time values; E=ClearVolume ( power( t)dt)dV , whereby the clearness of envisagedsurface-form is depending on the power within the light production mechanisms. This power could be variedbetween 2.5 micro Watts LED to unknown x tera Watts light bulbs. Therefore, fundamental following flow for
light production is just language of frequency to make nucleus bright light leaflike laser light.
Indeed, the most around signalizing significant applicable light implementation manipulation is the compile-
compute-conclude brain usage in many industrial branch field disciplines. Figure 011 shows a compact model of
the primordial principally industrial brain power of compile-compute-conclude proceeding processing, whereby
the main original component is the byte matrix ( typedef map CompressMap ) component such
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that, memory, registers inside core processing unit, and hard disk or portable memory sticks (see figure 011).
xAX xBX xCX xDX xSP xSI xDP XDI
purpose bitregister purpose bitregister purpose counter store back stack pointer stack index data pointer data index
Figure 011: basic principal primordial structure of natural light signalizing frequency signification synchronization
Although, Von Neuman [2], did bring out the depicted viewpoint of core proceeding processing uniformity,
which is shown infigure 011.
Figure 011 depicts the intentional inspiration item inside sequential digital data proceeding processing basics.Therefore four basic component kernel keymotor flows, which are the memory component whose measurable
data edge values are number of instantaneous couple bits, connection communication digit sensors whose
measurable data edge values are voltage variation level of instantaneous current data edges inside anyconnection component, wires whose measurable data edge values are ratios of instantaneous basic logic
influence systems such as temperatures, and the core processing unit whose measurable data edge values are
number of bit registers used during instantaneous arithmetic and logic couple opcode organization flows.
Indeed, the first step in intentional inspiration item inside sequential digital data proceeding processing basics
was the transistor foundation, whose measurable data edge values are number of instantaneous couple (on, off)
switching operations. The main sufficient suitable picture flow of transistor measurable unit could be shown in
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figure 018. Thus,following fundamental fatal functional flows define within any transistor hardware
description, shown inFigure 018, illustrates basic transistor transistor programmable push-pull involvement
development dynamics environment mechanism. Therefore the movable measurable centric metric bitwise
opcodes are measurements of voltage level variations inside load nil (see color brown shown infigure 018) or
load one (see color magenta shown infigure 018). Hence, during fundamental fatal following functional fact
flows of sequential digital data involvement insidefigure 011 require just language, which is defined infigure
018, of logic bitwise opcodes at any instantaneous time event value, which will be then converted to iteration
value inside co-software design of this just language of logic bitwise opcodes [10].
In fact, the main idea to drive the define circuit shown infigure 011, is to search the logic functional fact flows,
whereby the principal logic AND, and logic OR should be then used to achieve desirable aim object
functionalism. Furthermore, the primordial logic NOTcould then be evolved during direct connection of the
drain ofGate0 = {(push, pull)}(0)(n) to the source ofGate1 = {(push, pull)}(1)
(i). Indeed, based on transistorprogrammable language depicting infigure 002, the normal transistor output is defined within load0functionalism.
The rest of possible other logic operations such that logic XOR, logic NAND and so on could be easy defined
during enlivenment development of envisaged circuits shown in figure 011 andfigure 018.
Although, transistor basics modification is the intentional aim object of many workers inside sequential digitaldata proceeding processing basics. By this way Intel [11] consumer electronics and system on chip fabrication
fact flows are objective destinations into basic elementary gate composed of just language of three transistors,
because the variable resistor magnitude variation levels could be converted to transistor incoming voltage
variation levels (seefigure 018). Therefore Intel and co gates, which are composed of basic transistorowners, are
just language of three transistors inside the first following elementary gate, which is the logic NOT gate. The
transformation is involved infigure 001, which illustrates the three basic transistor gate. Thus, this gate
represents the digital elementary component. Hence, all implemented Intel consumer electronics and system on
chip components are gate associated sets. Therefore a core processing unit is a brain powerfulness whose
measurable data edge values are number of instantaneous bit register architectures. Even though these
instantaneous bit register architectures advance with a double unit measurement, which indicates that the
database information could be double the required number of connection communication wires.
5. Verification methodologies based on dictionary keymotor flow (map)
In fact, the aim object of this section is to present discrete event simulation based on Archimedes laws during
combining fast functional timing simulation techniques [3] and parallel scheduling job simulation [1], which are
static and formal timing aspect analysis. Hence, to fill in the requirement of the exactly true right definition of
discrete event simulation, number counting processing of years should be involved within any modeling-
simulation proceeding inside discrete event simulation. In fact, the start of year is first month birthday in any
year. Even though, event activity is add(month's birthday,one day). Although, increment(month's birthday) in
order to schedule one complete month counting, is proceeding processing to reach the second month and so on.
Furthermore, event attribute is characteristics within any event occurrence. Therefore event attribute is
magnitude variation level inside processing environment dynamics. Herewith, the envisaged event is thecounting of the number of years. Therefore, event attribute is an account identification, which is the most
significant number of days within any month, whereby December's account identification is thirty one days.
On other hand, discrete event based on Archimedes laws is selfish set of wood flat surfaceforms placed on any
water location. Therefore, the dictionary keymotor flow (map)is to associate a character keywordto any wood flat surfaceform in a given water location. Thus, the unknown is a list to engender instantaneous
event occurrences on this wood flat surfaceform.
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Character Water location Unknown x assignment
a Californian - Hawaii border {(timeindex, eventindex)occurrence}j IN
b Japanese - Russian border {(timeindex, eventindex)occurrence}i IN
c Australia - Southern border {(timeindex, eventindex)occurrence}p IN
d Norway - Northern border {(timeindex, eventindex)occurrence}q IN
Table 001: Water local for based Archimedes discrete event simulation
Hence water locations are Californian-Hawaii border, Japanese-Russian border, Australia-Southern border and
Norway-Northern border. Indeed, this sinusoidal following description function indicates the frequencysignification synchronization, which could be then used to drive the timing simulation and parallel job
scheduling for any occurrence. Thus, the occurrence description is basic logic influence systems.
Figure 012: Shadow of turbine motor tour numbers per unknown float x seconds time
Figure 012 illustrates the shadow of turbine motor tour number per unknown float x seconds. This any turbine
motor could be involved within any parallel hydro electrical power production in considered above waterlocations, which are Californian-Hawaii border, Japanese-Russian border, Australia-Southern border and
Norway-Northern border. Indeed, this sinusoidal following description function indicates the frequency
signification synchronization, which could be then used to drive the timing simulation and parallel job
scheduling for any occurrence. Thus, the occurrence description is basic logic influence systems.
In fact, for any discrete event simulation based on Archimedes law, system and signal need to be defined and
0 20 40 60 80 100 120 140 160
-1.5
-1
-0.5
0
0.5
1
1.5
iterationnumber
mag
nitudel
evelvariation
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clarified before any modeling-simulation thread-task proceeding processing. Even though, in this envisaged
discrete event simulation based on Archimedes law, a system is selfish set of following instantaneous item {(flat
surface wood, water location such that Norway-Northern border, natural wind, natural rain, natural snow, robot
mobile to control and proceed simulation)}. Though, a signal in this envisaged discrete event simulation based
on Archimedes law is putpixel(.,.,.,) following description functionalism function. Hence, thisputpixel(.,.,.,)
following description functionalism function illustration require hardware IBM-AT interface cards and whose
co-design software inspiration item.
Although, to avoid any hardware damage, this discrete event simulation proceeding processing requires a
responsible householder in any Earth's location near the considered water locations, which are Californian-
Hawaii border, Japanese-Russian border, Australia-Southern border and Norway-Northern border.
Furthermore, the evolved developed dynamics environment antennas are intentional requirement for anyputpixel(.,.,.,) following description functionalism function inside the built house for co-modeling simulation
hardware software co-design. Therefore, the antenna assignments could be wireless based on 4G+ technique
methodologies or similarly [19].
During proceeding processing of this discrete event simulation based on Archimedes law, the simulation result
effect are putpixel(.,.,.,) following description functionalism functions, which have to be shown as follows:
Figure 013: Shadow of fixed frequency modulation function per unknown float x seconds time
Figure 013 illustrates the shadow of the 2.5GHz fixed frequency modulation-correlation following timingdescription function, whereby whose level variation represents a modeling-simulation envelop environment
dynamism. In fact, in 1948, Claude Shannon [9], did invent effort to mathematically formulate this modeling-
simulation envelop environment dynamism as i
[p i log10[1
pi]] , wherepi is a stochastic parameter value to
measure uncertainty amount quantity for each simulation iteration and joined incoming signal.
In fact based on the timing functional echo works ofWorgang Beckerand Diekmann [11,12], Said Mchaalia
0 20 40 60 80 100 120 140 160
0
0.2
0.4
0.6
0.8
1
1.2
iterationnumber
magnitudel
evelvariation
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[10] and co working on this modeling-simulation envelop environment dynamism, did bring out the following
mathematical dynamics environment mechanism: i, j
[1
[1+[theta i+phij]]] .
The simulation results of this movable flat surface wood in any defined water location (see table 001) have to be
illustrated in the following section.
6. Simulation result overview viewpoints:
In fact, simulation result have to depict the movable fountain functionalism functional proceeding processing
of considered flat surfaceform wood. Thus, figure 014 shows the shadow of flat surfaceform wood motion
following fountain description function. Though, for just language of inside one period, the engendered
envisaged binary number, which is similar to VHDL-language and cadence VCD file encoding of binary
numbers, is the following binary number:
binary0x010x0z010z0x01b.
Therefore, the binary xb and binary zb are two fuzzy binary values. The original main sufficient suitable
parameter presentation of these fuzzy binary values is the half bit representation, which could be switched to
eitherbinary nil(logic false) orbinary one (logic true).
In fact, converting logic value ((logic false) or (logic true)) to measurable amount quantities of current data edge
flows (seefigure 016for details about current data edge flow), is achievable thread-task of Intel and consumer
electronics fabrication following flows.
Figure 014: Shadow of wood flat surface motion following description function
Thus, the main sufficient suitable aim object of sequential digital data encoding and compression [2], is to
predict undefined binary bits such that the binary xb and binary zb (see said-thesis.pdf, page 44). Therefore, the
hex-decimal encoding representation of a movable fixed 2.5 GHz frequency [18].
0 20 40 60 80 100 120 140 160
-1.5
-1
-0.5
0
0.5
1
1.5
iterationnumber
magnitudel
evelvariation
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Figure 015: Shadow based frequency of wood flat surface motion following description function
Figure 015 shows the shadow based on the frequency of wood flat surface motion following description
function. From shown infigure 015 magnitude level variation inside the considered interval time, which is [0
nano seconds, 1.8 nano seconds], the compiled-computed-concluded frequency is then5
9GHz , whereby this
frequency value of5
9GHz is intentional original main root mean frequency. Furthermore, the next up steps of
researchers within sequential digital data encoding and compression are to determine the harmonic frequencies
and then to provide necessary sequential digital data encoding leaflike scalar quantization (used in JPEG-LS data
compression techniques) and vector quantization too (see said-thesis.pdf, page 55 for more details). Thus, David
Huffman [17, 18] did invent in 1950 as student in MIT university (USA).
Figure 016illustrates Shadow based current data edge flows of wood flat surface motion following description
function. Hence, this control data flow graph is event basics logic influence system for any counting-computingopcodes signalizing signification synchronization environment dynamism. In fact, the best compiler
optimization is to directly implement a co-design hardware-software based on control data flow graph involving
transistor language transition transactions. The start interruption process node withinfigure 016 has role
to interrupt most around circuit to start collect data to be measured within any simulation time. End
interruption process node plays a similar rule as cutoff link switcher. Furthermore, the other nodes arearithmetic-logic operation nodes. The following motor flows of data edges is that the following values
are binary made representation, whose last values are instantaneously stored inside grounded togathering information database node.
Figure 016depicts an original primordial made representation of control data flow graph,
whereby a( . )sin2 ( . ) function could be then used to identify selfish set of current data edge flowstepping scales. During modeling-simulation thread-task ofco-design hardware-software based on controldata flow graph involving transistor language transition transactions, a list of couples (timeevent, valueevent)
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
-0.02
-0.02
-0.01
-0.01
-0.01
-0.01
-0.01
0
0
0
0
timeinnanoseconds
magnitudel
evelvariation
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would be involved within this simulation processing analysis.
Figure 016: Shadow based current data edge flows of wood flat surface motion following description function
Therefore, the main task in this modeling simulation proceeding processing analysis is to identifyevent values for each time value, whereby mathematical ratios are equations of x i to yj, which depict the
fraction between xi and yj; x iy j
. Thus, probabilities occur often in measurement processing analysis. Thus,
probabilities are ratio values less or equal to one. To associate with measurement processing analysis
probabilities, which are determined as ratios of values at time t to maximum value for all time. Therefore,
consider a set of measurements;{(t1,V1), (t2,V2), (t3,V3), ..,(tn,Vn)}.
The maximum value of all time is (tj, , Vmax). The probability determination is defined as follows;
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{(t1,V1
Vmax
), (t2,V2
Vmax), (t3,
V3
Vmax), ..,(tn,
Vn
Vmax)}.
Those probabilities would be involving within each measurement processing analysis of amounts and quantities,
which are gains in magnitudes or gains in amounts, legacies, error corrections and other kinds of measurement
processing analysis.
7. Transistor language transition transaction
In fact, transistor language transition transaction is background information database for sequential digital data
encoding and compression. Hence,figure 017illustrates the main principally of sequential digital transmission-
reception-absorption based on transistor language transition transactions. During modeling-simulation thread-
task opcode organization flows, many dynamics environment mechanics depict any functional timing simulation
of just language of considered digital-analog component, whereby inertial functional functionalism and
characteristics are viewpoints of modeling and simulation event blowing language inside systems (MaS-EBLiS).
Figure 017: basic logic influence systems on toggle template class map
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Therefore, infigure 017, the main keymotor flow inside technical methodologies of sequential digital encoding
and compression is the powerfulness of voltage level variations form 0.9 Volts to may be mega (1000 kilo volts)
Volts or perhaps tera (1000 giga volts) Volts. Furthermore, the intentional secret surround system is the wireless
antenna and connection communication principles [19]. Moreover, transistor language transition transaction [3],
involves involution investigate involvement of any sequential digital data encoding. Thus, during register
transfer logics from elaboration ofVHDL-language models based onXLINXand co digital background databasefor syntheses toolboxes [6].
Figure 018: Shadow based current data edge of fountain motion following description function
Figure 018 illustrates current data edge shadow based on following fountain motion description function,whereby event basic logic influence systems (basic logic influence system based on event occurrences) are just
languages of incoming distinct voltage levels from the wireless dynamics environment dynamism.
Even though,Figure 017illustrates current data edge shadow based on following fountain motion description
function, whereby event basic logic influence systems (basic logic influence system based on event occurrences)
are just languages of incoming distinct voltage levels from toggling dynamics environment dynamism, whereby
timing simulation and parallel job scheduling (magnetic flux, sequential binary outputs, sinusoidal inputs), are
involvement insight involution inside sequential digital data encoding technique idea brain powerfulness,
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Figure 019: Shadow of transistor language basic logics during current data edge fountain motion flow
Figure 019 depicts the shadow of transistor language basic logics during following current data edge fountain
motion function flows. Thus, this basic logic transistor transition transaction could be easy detected during the
elaboration of sequential digital database inside load 0 orload 1. The difference between the sequential digital
database inside load 0 and load 1 is intentional aim object of searchers inside system on chips and deepsubmicron designing description background languages [3] and [5].
Said Mchaalia [8,10] and other leaflike locking transistor logics inside digital design background, however, use
transistor language transition transaction as Petri nets defined in [4] and control data flow graph defined in [3,
10], to implement directly transistor language transition transaction inside digital design background.
Even though, figure 019 shows a basic logic influence system based on direct implementation of transistor
language transition transactions.
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Figure 020: Shadow of transistor language basic logics inside digital design background language
Figure 020 depicts Shadow of transistor language basic logics inside digital design background language.
Therefore, modeling transistor language basic logics within any timing simulation [1] and parallel schedulingjob simulation [3, 4, 6], describe hard thread and tasks for many searchers during digital design background
description.
In switching controlling mode, the transistor language could be modeled with some synchronized opcode
organization flows based on searching studying capacitance, whereby the original primordial principally threads
and tasks involving in this proceeding processing are saturation and cutoff frequencies. For me, Said Mchaalia,
the basic idea to model such a proceeding processing is to vary frequency ranges from nil (null) variation level
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value to unknown x variation level values.
Figure 021: inductor-capacitor-resistor circuit for frequency variation realization.
Figure 021 depicts the realization of frequency variation inside a circuit to filter the signal output. The
inductor L receive signal input edge characterizes current flows in Amperes. These current flows
sustain or incur some basic logic influences. These current flows incur phase shifting and magnitudemodification. Then they maintain their following flows within the circuit. Some of them will traverse
the resistor and others will contribute for capacitor charge. The output node is resistor-capacitor filter
characterizing the 3dB lossy magnitude for cutoff frequency. Thus, to measure this cutoff frequencywithin the 3dB lossy magnitude, the following mathematical functional operation
GaindB=20.Log10(VoutV
) should be onward proceeded. The output voltage waveform is illustrated by
1ea.RC.t . Although, the input voltage waveform is depicted by:sin(2.pi.f.t). Hence, 3dB lossy
magnitude from the maximum gain,Gain
dB=20.Log10(
Vout
V ) , allow then short time interval
measurement dt, which is characterizing cutoff frequency;1
dtin Hz. In fact, frequency oscillation
realizations is the aim object of digital data transmission branch fields. The simple way to achieve this
is the usage of circuit included infigure 021.
Incurring onwards send-receive those frequency is the subject aim of digital data transmission such this
involved within digital satellites processing analysis. The main original theme of this incurringonwards is the data encoding decoding processing analysis. Hence, Shannon did propose an idea of
data encoding based on the bit-word-length calculations thus the minimum amount of bits to be used to
encode a character a for example found in an alphabet set ofNcharacters. This number is thus
calculated 2x=N+1 . To search x, just introduce the logarithm function as follows:2x=N+1 log2(2
x )=log2(N+1) . Therefore, this x number could be determine as follows;
x=log2(N+1) . Indeed, the logarithm conversion between bases is: loga( y)=ln ( y)ln (a )
. Although,
x=log2(N+1)=ln (N+1)ln(2)
, where ln(.) is the natural logarithm function. As example, where theASCII
code (255 characters) were encoded, the amount of bits was eight bits. In fact, to encode one character
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from the 255 character alphabet set, a sequence of eight bits is required for example 10011010b. To
send this character, the above techniques such the charging and discharging of the capacitor of figure
11 eight times or more would be involved. In fact, the 1b represents the highest magnitude amount,
however the 0b represents the nil environment of the magnitude amount. An other methodology is toconvert such a binary sequence to integer value and to use the potentiometer command and controlling
for digital-analog converting.Figure 022 represents such a processing analysis. For further gathering
discovering database's information, functional description equation details are distinct.
Figure 022: waveform within modulation and modeling-simulation processing analysis.
Figure 22 shows the waveform depicted by the mathematical function [sin( .)] .e(.) . Thus, within thismathematical function the magnitude change values indicates the resistor value variations for resistor-
capacitor filter. The nil values indicates the logic false and the summit magnitude value indicates the
logic true. The between value variation determines the fuzzy logic processing analysis. Notice theoriginal frequency within this waveform is the ratio of one to the interval time separating two
successive magnitude summit values. This frequency is measured in Hertz (unit is Hz). So, varying
resistor values allows magnitude variations of output node. This produces a frequency variation insidethe envisaged circuit. For equivalence task, an inductor could be involved. Therefore, the new circuit
would be depicted with inductor-capacitor circuit. The inside circuit frequency could be measured
throughout this mathematical formula; L.C.4.p2 . f2=1 , wherep is a ratio of 314 to 100, andfis thefrequency to be calculated. The frequency calculation is background variations ofL when Cis constant
or vice versus or together varying in time. Therefore, the frequency is a ratio of 1 to 2.pL.C . In fact,
when wanting to set the frequency to its highest possible value, the inductanceL and the capacitance C,
should be less as they could be. As example for given values ofC = 1 micro Farads, andL = 1 microHenry, the frequency is then equal to about 159.23 Hz.
[sin(.)].[exp(.)]wave-formmodelingsimlationprocessing[sin(.)].[exp(.)]wave-formmodelingsimlationprocessing
time
magn
itude
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8. Multi-frequencies searching studying opcode organization flows:
Searching frequency for applied waves in some branch field applications, need basic liable intentional smart
modeling-simulation proceeding processing analysis. Although, the applicable frequency units are subject of
discussion in many frequency application disciplines.
Even though, as shown infigure 022, the magnitude level variation is a float number, which is the nearest float
to fourth (0.25). Not only his searching magnitude level variations based on frequency searching studyingopcode organization flows allow to investigate resonance effect aspects, but also bring out power involution
principles, where the lowest used power for controlling transistor language transition transaction is 1.5Volts. By
this way, the fourth of such a float number is 0.375 Volts, which may be present the lowest voltage magnitude
level variation in abs(x) value for drive transistor language transition transaction.
Figure 023 fourth magnitude level variation
Figure 023, depicts the magnitude level variation of frequency range . This frequency range allow to conclude
that there is a float number, which is the nearest float to fourth (0.25), depicting the maximum variation of
considered magnitudes.
Although, the frequency amount quantity in digital signal processing is determine throughout the famous
fundamental following formulation: magnitudemax 3dB (sub(max (magnitudemax ), 3dB)) (and then search the
corresponding inductance, capacitance and resistance values for validation.
On the hand, the primordial principal organized aim object of transistor language transaction is searching
studying of opcode organization based on just language of magnitude level variation, which is characterized byfollowing table:
sub(max (magnitudemax ), 3dB) = magnitudemax( 1
N
0
N
[weighti , j ,m , n , p , q[magnitudei]])3dB
Table 002: magnitude level variations based on frequency searching studying opcode organization flows
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
timeinnanoseconds
magnitudel
evelvariation
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Table 002 presents the basic logic intentional swing dynamics environment dynamism, whereby the magnitude
level variation make neutralism inside basic combination composition components during digital design
elaboration and compile-compute-conclude proceeding processing involving timing simulation and parallel job
scheduling for any possible mnemonic brain powerfulness and ideal aim objects.
9. Interfacing transistor language transition transaction
Indeed, based onLempeland Ziv [2], dictionary basic logic insight system is brain powerfulness effect resistanceand capacitance results. Therefore, to transact is to carry or conduct database information, whereby the trade
agreement assignment are the keymotor flows for any timing simulation and parallel job scheduling [1,3,4,7].
Hence, as shown infigure 017, a database transaction comprises a unit of work performed within a database
management neural system or similar system against a database and proceeded in a coherent and liableintentional way independent of other transactions. Transactions in a database environment have two main
purposes, which are
- first of all, it is to provide liable intentional central metric measurable units of searching studying opcode
organization flows, which allow correct recovery from failures and keep going database consistence even though
in cases of system failure. Although, when execution stops completely or partially and many operations upon
database remain completely with unclear status, then transition transactions are requirement involutions.
- secondly, it is to provide isolation between programs accessing a database concurrently. Thus, without isolation
the outcomes of programs are possible erroneous basic logic influence systems.
Furthermore, a database, by definition, must be atomic consistent isolated and durable. Thus, database
practitioner often refer to these properties of database transactions using the acronym synthesis toolboxes.
Transactions provide in fact an all or nothing proposition, starting that each centric metric measurable
methodology measurement unit performed in a database must either complete in its entirety or have no effect
whatsoever. Further, the system must isolate each transaction from other transactions, results must conform to
existing constraints in the database, and transactions that complete successfully must get written to durable
storage.
Moreover database and other stochastic storage space, which treat and proceed the integrity of database of data
as paramount often include the ability to handle transactions to maintain the integrity of data. A single
transaction consists of one or more independent units of work, each reading and/or writing information to a
database or other data store. When this happens it is often important to ensure that all such processing leaves the
database or data store in a consistent state.
Therefore, during interfacing transistor language transition transaction, bitwise operation operate on one or more
bit patterns or binary numerals at the level of their individual bits. Thus, on the most older micro-electronics
digital design description, bitwise operations are slightly faster than addition and subtraction operations and
usually significantly faster than multiplication and division operations. However, on modern Archimedes law
discrete event simulation (digital design architectures), this is not the case, whereby bitwise operations aregenerally the same speed up as it wants for addition (though still faster than multiplication).
In fact, bitwise operations have to consider sequential digital amount data measurable equivalence length for
centric metric measurable methodology modeling at any instantaneous inspiration insight inside time
environment dynamism. Thereby, bitwiseNOTis a unary opcode organization flow and though bitwise OR,AND,XOR,NAND and co are binary opcode organization flows.
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Figure 024: shadows of some bitwise opcodes organization flows.
Figure 024 presents some synchronized opcode organization flows, whereby bitwise operations are involving.
Even though,Figure 025 depicts the main principal of clock sequential functional digital data flows.
Figure 025 shadow of main principal functional digital data flow.
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In fact, the output of figure 025 depict a virtual logicNAND bitwise, whose inputs are the inside wiring outputs
of the lastNANDs involving inside this digital design. As defined in the wave-report.pdf[10], the relationships
between the fountain flow of considered possible outputs, permits the searching studying of clock velocity
opcode organization flows. Therefore, to achieve such a thread-task, an inspiration inserting of logicNAND
bitwise involves functional timing simulation and parallel job scheduling [1,3,4,7].
Figure 26: shadow magnitude level variations during frequency changes and TAN(x) modulation-correlation.
Figure 26shows shadow magnitude level variations during frequency changes and TAN(x) modulation-
correlation, whereby this frequency value of 59 GHz is intentional original main root mean frequency. For
magnitude frequency range level variation within just language of ratio variation from4
5to
1
10.
This proceeding processing, however, introduces the fast functional following fact flows for harmonic phase
introductions and then binary encoding technique brain powerfulness ideas.
Even though, the centric metric measurable unit of standard workload involving timing job scheduling and
parallel simulation is just language language of float number characterizing the number of tours per just
language of one second timeunit.
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
-5
-4
-3
-2
-1
0
1
2
timeinnanoseconds
ma
gnitudel
evelvariation
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Figure 027: Shadow of centric metric measurable standard workload unit.
In fact, as shown infigure 027, the original main primordial principal measurement unit of timing simulation and
parallel job scheduling is to search and study the valuelevel variations inside frequency branch field investigate
involutions. Even to depict a rotation motion in a planar system coordination is to plot sin(x) function cos(x).
Thus the number of rotation per just language of one second time, determines frequency involvement.
10. Conclusion viewpoint overview
In nowadays designers have to verify a huge of complex levels of digital circuits, embedded software and on-
chip analog circuitry with fragmented methodologies that substantially impede verification speed and efficiency.
They also face a large number of technical issues including design performance, capacity, test development, test
coverage, mixed-signal verification, and hardware-software co-verification methodologies. In fact, optimizing
verification speed is a complex research subject. Overall, verification methodologies are used by the designers at
a variety of design integration levels. To improve digital hardware design using these verification methodologies,
many digital simulation techniques are used. One of them is discrete event simulation, which has successful
track record in the improvement of hardware verification process. In contrast to other simulation methods
(leaflike differential equations) in which systems evolve continuously in continuous time, the systems in discrete
event simulation are described by discrete events and appropriate processes. Discrete event simulation performs,
indeed, each event or transaction or item individually using an appropriate process.
In fact, simulation, however, is not a satisfactory solution to the validation problem of digital hardware
for many reasons such as: each schedule (run) proves only the correctness of the design under
verification for that particular sequence of inputs (stimuli); and only one design under verification stateand input combination are visited per simulated clock cycle. However cycle-based simulation involves
these simulation limitations, it is still a sophisticated technology choice for the validation process of
-1.5 -1 -0.5 0 0.5 1 1.5
-20
-10
0
10
20
30
40
cos(theta+phi)
valuelevelvariation
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large synchronous systems, in which logical simulation is nicely scalable regarding to designer
requests. To propagate values from system inputs to system outputs, a simulation clock cycle is
required. After finishing one cycle, the next cycle will be begun. Moreover, practical cycle-based
simulators allow for circuits with multiple clocks and interface to event-based simulation.
However, cycle-based simulation ignores system delays and inter-phase relationships. This limits the
amount of information about the design that can be extracted from the simulation. Note that cycle-based simulation does not work for asynchronous designs and cannot be used in timing verification.
Event-driven simulation environments uses the traditional discrete event simulation mechanism and
considers system delays and inter-phases. Thus, during each verification process using either cycle-basesimulation or event-driven simulation, we have the opportunity of outputting the simulation results to waveform
diagrams.
Figure 025: model card meilhaus300 for basic logic bus interface communication during
measurement principles proceeding processing dynamics analysis
Figure 025presents a card model for database acquiring and acquisition to further model and simulate general
purpose sequential digital data.
11. Reference papers
[1] Uwe Schwiegelsohn,A system centric metric for the evaluation of online job schedules, 2011, Journal of
Scheduling, 14, 6, 571 -581
[2] J. Ziv and A. Lempel, A Universal algorithm for sequential data compression, IEEE transaction on
information theory, vol. IT-23, No-03, 1997 May.
[3] Edwin Naroska,Parallel VHDL simulation, Proceedings of the Design, Automation and Test in Europe
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Conference DATE'98, 159--163, 1998
[4] Uwe Schwiegelshohn and Lothar Thiele,Proceedings of the 19th International Conference on Application
and Theory of Petri Nets, Workshop Hardware Design and Petri Nets (HWPN 98), 12--25, 1998
[5] Uwe Schwiegelshohn and Lothar Thiele,Periodic and Non-periodic Min-Max Equations, Proceedings of
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Springer, 1997
[6] Naroska and Uwe Schwiegelsohn,A new scheduling method for parallel discrete event simulation, Proc.
2nd International Euro-Par Conference on Parallel Processing, 582-593, Springer-Verlag, 1996
[7] Uwe Schwiegelsohn,Preemptive weighted completion time scheduling of parallel jobs, Proceedings of the4th Annual European Symposium on Algorithms (ESA96), 39-51, Springer-Verlag, 1996
[8] Said Mchaalia, Waveform compression draft copy, December 11th 2002, CEI, Dortmund University,
Germany
[9] Claude Shannon, mathematical information theory, IEEE, 1948.
[10] Edwin Naroska and Said Mchaalia,Free HDL Compiler Control Data Flow Graph and its application in
Waveform compression draft copy, August 07th 2002, CEI, Dortmund University, Germany
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measurement, IEEE confirence on medicine biology, Lyon 1992, pp 34-36.
[12] Diekmann and al., Comparison of MEG, EEG and frequency MRI responses to identical electrical stimuli
delivered peripheral nerve.[
[14] Bodanis, David (2005),Electric Universe, New York: Three Rivers Press, ISBN-978-0-307-33598-2.
[15] Background electrical electronic magnetic flux and power courses.
[16] G. E. Blelloch, Introduction to data compression technical report, computer science department, Carnegie
Mellon University, October 2001.
[17] J. Vitter,Design and analysis of dynamic Huffman codes, Journal of ACM, 1987
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Application Protocol Forum, 1999.
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