workload proceeding

7/29/2019 Workload Proceeding

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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


21/28

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


22/28

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


23/28

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.


24/28

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.


25/28

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


26/28

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


27/28

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

the 24th International Colloquium on Automata, Languages, and Programming (ICALP 97), 379--389,

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

[11] Worgang Becker and al.,Magetic Localization of EEG Electrodes for Simultaneous EEG and MEG

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

[18] W. A. P. F. Ltd., Wireless Application Protocol, Wireless Session Protocol Specification. 5, Wireless

Application Protocol Forum, 1999.

[19] Dror G. Feitelson, Dan Tsafrir and David Krakov,Experience with parallel workloads archive, Department

of computer science at the Hebrew University of Jerusalem and Computer science department of Haifa,Israel, September 04th 2012.
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