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How to improve communication Skills for a job interview..!?

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Communication is a language of expressing thoughts or imparting views of a person in specific forms of speaking, writing, gestures, etc., It is an act of sending information from one person to another. The definition may seem simple but the actual process is complex.

Communication in turn can be expressible as Verbal and Non-Verbal forms. Effective communication is when both the forms tag along. To understand the information quickly, a good amount of communication skills are essential. It is a vital skill in our life and cannot be overlooked. To make understand and be understood is one line phrasal defining the meaning of communicating well.

 

Why should we communicate?

  • To give information.

E.g. Two people talking to each other (or) NEWS from a newspaper/radio.

  • To persuade.

E.g. To sell a product or convince a person.

  • Express needs.

E.g. Ask for food when you are hungry (or) need a report for the meeting.

  • Form social bonds.

E.g. Greeting others (or) introducing yourself.

  • Share feelings.

E.g. Share your happiness (or) sorrow.

 

“If you improve your communication skills, I guarantee you that you will earn 50% more money over your lifetime”

-Warren Buffet

 

As we know Communication can be Verbal & Non-Verbal in its form.

Verbal:

  • Face to face.

A social interaction carried out without the use of any mediating technology and it is a mutual influence of individuals involving direct physical presence through the person’s body language.

  • Written

Expressing your thoughts and views in the form of words on a sheet of paper and this way of communicating can happen in physical absence.

  • Telephonic Communication.

It happens when people are separated by almost any distance and telephone or mobile phone acts as a mediating device to enable the information transfer.

Non-Verbal:

  • Facial expressions.

Communication through facial expressions is universal as the human face is extremely expressive and can convey the information without saying a word.

  • Paralanguage

Non-phonemic properties of speech like speaking tempo, vocal pitch, intonational contours, and hesitation noises can form the factors under consideration in communicating attitude and other shades of expression.

  • Gestures

To express an emotion or information, we move with a part of our body, especially hands.

  • Posture

This is the position in which a person behaves or holds the body when standing or sitting.

  • Eye contact.

It is an important sign of confidence, respect, focus, and attention paying. This way has a large influence on social behavior.

  • Appearance

It is an outward look of a person and it done maintained to improve self-confidence and a good dressing also impresses and attracts other people.

 

DO’s and DON’Ts while having a communication:

DO’s:

  • Maintain eye contact.
  • Dress for the occasion.
  • Speak with clarity.
  • The right tone of voice.
  • Correct posture.
  • Positive facial expressions.
  • Positive gestures.

DON’Ts:

  • Checking the phone while conversing.
  • Dressing shabbily or inappropriately.
  • Fumbling, using too many words filler words while talking.
  • Speaking rudely.
  • Slouching and looking nervous.
  • Looking disinterested.
  • Unnecessary fidgeting.

 

How to ace an INTERVIEW with desired communication skills?

What is an interview?

It is a widely used process of screening applicants for jobs that provides the most direct information about a candidate, his/her skills, background, and personality type.

One can improve communication skills for a job interview by knowing the process of what and how to respond and transmit the information by taking a mock interview or self-practicing and improving the essentials.

Let’s get into detail about the preparation, do’s and don’ts, techniques, tips, and tricks to give a sure shot to an interview.

Preparation for a job interview:

Every interview is an experience of learning which takes place during the preparation and is useful for the interview you are appearing for. The initial preparation requires a thorough investigation of skills, accomplishments, expertise, and interests.

The interview preparation includes 4P’s namely Prepare, Practice, Present, and Participate.

DO’s of an interview:

  • Treat everybody with courtesy and respect.
  • Greet everyone with a smile.
  • Be ready for a handshake.
  • Be an active listener to comprehend and understand the questions properly.
  • Address the interviewer as Sir/Ma’am
  • Maintain good eye contact throughout the interview.
  • Be alert and sit straight in your seat.
  • Ask for clarification if you don’t understand a question.
  • Be brief and concise in your response’
  • Use formal words and expressions and appropriate grammar.
  • Display your interest in the employer and the job you are being interviewed for.
  • Reply to the questions in a positive manner.
  • Keep your tone polite yet firm.
  • Show your enthusiasm.

DON’Ts of an interview:

  • Don’t take a seat until you are offered one.
  • Don’t slouch and fidget.
  • Don’t talk negatively about previous employers (or) managers.
  • Don’t show a lack of interest during the interview.
  • Don’t give the impression that you are interested in money or salary.
  • Don’t be rude and imprudent; say ‘NO’ politely.
  • Don’t use slang and minimize the use of filters like “okay, you know, yah, etc.,”
  • Don’t chew gum.
  • Don’t keep your mobile ON during the interview.
  • Don’t leave in a hurry.

Negotiation Skills:

The main purpose of negotiating is to get closer to your objectives, as many people say “Negotiation is all about a win-win situation”. The negotiation process maybe during the interview process about the salary upon selection to a job or maybe on a telephonic call later after the interview process.

Techniques to be a good negotiator:

  • Put yourself in another person’s shoes and consider how they would react to your proposals.
  • Do not stick to a specific pint of negotiation.
  • Follow different styles and mannerisms to negotiate.
  • Be comfortable in whatever style you choose.
  • Be calm, relaxed, and focused.

Tips to handle negotiation responses:

  • After getting a response from the other side, do not feel obliged to respond immediately. Take time, ask for clarifications, if required.
  • Evaluate the given proposal and compare it with what you have proposed.
  • Discuss the responses in detail.
  • Share your feedback on the terms offered.
  • Talk about the inconsistencies, if any.
  • Give suggestions on how things can come closer to your objectives.
  • Negotiate in a calm but firm way but don’t forget to be polite.
  • Settle the things if it is agreeable to you.
  • Always have a written agreement to avoid disputes in the future.
  • Always conclude on good terms.
  • Give a positive response for continuing correspondence.

Basic tips and tricks to crack an interview:

  • Be brief while responding to the question “Tell me about yourself”. Don’t include irrelevant details.
  • Start the conversation with a greeting.
  • Give a brief on your career objectives and academic qualifications.
  • Talk about your strengths and skills you can offer them and show your interest in joining.
  • Speak about your achievements, the special skills you possess, and job experience (if you have).
  • Talk about the best you have in you to the question “Why should we hire you?”
  • Make sure your strengths match the job profile.
  • Make a weakness of yours appear to be a strength and describe it as an action that needs to be improved.
  • Research about the organization details thoroughly and know your job role for which you are interviewed for.
  • Thank politely and end the conversation gracefully.
  • Don’t forget to follow up after the interview.

 

 

 

 

 

 

 

 

 

COVID19: Economic Impact

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Has the humankind ever imagined that an invisible micro-creature to the naked eye, a virus, can drop them on their knees to its surrender?

Has there been a scenario of this kind hitherto that hit the entire world demography and pulled down in every aspect possible?

Has anyone ever dreamt of such an unforeseen plummet in the global economy just because of a microscopic organism?

A zillion such puzzling questions arise and probably this pandemic makes the world populace preset a bearance to such future outbreaks in terms of health, hygiene and economy.

How did corona virus affect the World Economy?

Corona virus, which was first detected in China has spread its wings of infection in more than 185 countries. Its outbreak has left businesses across the globe counting the costs. As lockdown has formed an essence to contain the virus outbreak, many countries and world capitals have halted major industrial production and the work mode of organizations have turned up chairs for their employees, on the technology platforms enabling them to work from the places of their stay.

Global shares, oil prices, travel and tourism industry, hospitality chain, fields of cinema, sports so on and so forth have suffered a major hit which has tend to a risk of recession. All these happenings are due to stagnating of the workforce, unemployment caused due to layoffs, pause in trading and the foremost factor is locking the movement of people. The International Monetary Fund (IMF) has revised the global GDP to 3% which was estimated to a growth of 3.3% just three months ago. It is far worse than the financial crisis that occurred back in 2007-2008.

How long is this economic slow-down going to be?

The world is almost entrapped in the economic slump owing to the pandemic spread and its reconfiguration cannot be at an expected pace because the fear to interact and gather is yet instilled in people. So, as long as human interactivity remains a menace, there is no scope of businesses returns to normalcy down the timeline in the near future. Public health is above all in concern rather the economy.

Even after the unlock, people may be less inclined to move in crowds albeit the virus being contained to a large extent. When the virus quells, enabling people to snap back into a usual go of life, there may be a resurrection in the curve of economy. Once, the virus is completely tamed through medication (no one knows when it’s probably going to happen), there will be many challenges to confront for the economic revival.

“Psychology won’t bounce back”, said an economist named Charles Dumas “As people underwent a real shock, there is certainty in change of behavioral patterns which may not be forever, but lasts for a long while”. Everything depends on how long it lasts, but if it is for a long while, then it is going to be the mother of all financial crisis because people form the basis for the survival of the nation’s economy.

Why stock markets are recovering but the economy is still slowing down?

Stock markets had a massive melt worldwide earlier during the corona virus crisis but somehow seem to be in surge apart from the snail walk of the economy in this pandemic scenario. The stock market is taking shape and projecting up in graphs in recent weeks as if the pandemic vanished away in time with all the devastating impact on the economy. The gap has never been so long before between the markets and the economic data.

It’s definitely onerous to pinpoint what exactly moves the market. But there are a few in consideration to explain the happening.

1. “Prop up markets by pumping money into the economy”, is something done by American and some of the European governments. Their amount of spending so far constitutes one-third of the GDP in a short time period.

2. TINA- There is no alternative, is the way investors are going through. They do not have a lot of places to go with their money as the returns offered by bank FDR and bonds are very low. The real estate is dead and the gold prices pitched in price. Hence, investing in stock markets may lead to their rescue.

3. A fear of missing out has griped in the investors and it seems that the retail investors are playing the stock markets in this quarantine period.

Sometimes, the stock market is considered to be a leading indicator of the economy but no one knows what’s really going on. It is admitted by many traders, experts and analysts that everyone is operating in a black box.

Is there going to be a ‘Second Wave’ of Corona virus?

As the curve tends to flatten with the decline shown in the virus affected people in certain countries, the respective governments are freeing the movement of people with certain guidelines to be followed. Industries, transportation, e-commerce etc., are open to functioning to revive with the economy but are operating within the bounds.

As lockdown restrictions across the world begin to lift up, we are definite to give another chance to virus to flourish back. All these happen if the unlock happens too earlier or if people may think it’s over which isn’t yet. If we let the way for a second go, then the consequences just flow out of our hands.

An economy can never be revived with the aid from the IMF or World Bank and the countries have to look up to each other to obtain help and a strive of generations may cause a revert back to the economic trends which are seen before the pandemic.

How to survive this global economic collapse?

The COVID-19 pandemic is a public health emergency which leads to sickness and death. As lockdown is necessary to contain the disease, it, in turn, ceased the economic activities. It is expected by many expert economists that there will be a double-digit decline in the second quarter of 2020. Many firms go out of business and millions are likely to lose their livelihood which in modern history is unprecedented. Moreover, it is pretty difficult to predict whether the revival in the economy will be a V-shaped or U-shaped curve.
Making a living post-pandemic seems daunting. Indian industries must turn self-reliant in production and manufacturing with low or zero reliance on imports from foreign countries. ‘Make in India’ must prosper in our country as the government reaches us to support through which the Start-up era makes bloom and thus job opportunities can be created. This pandemic has left us a long term drop in the economy to restore with and also taught us to be future safe and go the distance against for such future happenings.

Future of Parallel Computing

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Future-of-Parallel-Computing

Parallel Computing is a field that is undergoing tremendous progress and has the potential to scale great heights. All the problems that come up in the future are mostly parallel in nature. The problems that cannot be solved in any serial approach and need a framework of a parallel workflow can be coined as parallel problems. Post an extensive study it is understood that many problems that are present in today’s world can not only be solved but can be solved more efficiently with the use of parallelism.

Parallel computing uses technologies that solve problems encountered in distributed systems and systems connected to complex networks. NP-hard problems are the problems that cannot be solved in a specified time frame. These problems require huge computing resources that are practically not possible in the case of traditional systems. Usually, supercomputers are adopted for this purpose. Supercomputers are those systems that are highly interconnected with huge chunks of processing units and comparably exponential memory available for use.

Practically, we can use parallel computing to our benefit as at times we can achieve a system efficiency of 100%. This is fulfilled by a concept called Super Linear speedup. This is gained when the total amount of work done by n-processors is less than the total amount of work performed by a single processor. Such efficiency is impossible to achieve theoretically, though it can be possible in some cases. For example, if we have 8 processors, each having a 2 MB cache, and your computation uses 6MB of data. If it were to be done sequentially on just one processor, the amount of computation by that processor will be a lot because of the data movement between CPU, cache, and RAM (The CPU needs to move in parts of 2MB sequentially). On the other hand, if we use 8 processors the overhead of data movement will be eliminated since the whole data can now be fit in the cache memory of 8 processors (total 16MB).  This way we can achieve super-linear speedup.

We have observed that whenever we have lower than threshold performance from the serial execution we use the parallel algorithm to achieve super-linear speedup. The concept is however rarely possible as the requirements of each process are far greater than the memory size of each cache. We try to understand the future scope in the field of parallel computing and algorithms by considering the alternative solutions that are already starting to boom. The two major competing computing architectures under study are the Superconducting computers and Quantum computers.

Superconducting computers is a field of research that has been active for more than 50 years now. But even with many breakthroughs in this field, there has not been a single commercial implementation of these computers to date. These computers use the concept of Exascale computing which has been brought back to the research field in late 2015. Exascale computing is believed to be in the order of the performance of a human brain. These systems do not have any exhibit relation to quantum effects hence there are different in comparison to the quantum computers.

On the other hand, Quantum computing is a field of study that is also termed as instant computing. These systems are inherently parallel but they are not conventionally parallel. We measure the processing of each quantum in qubits. According to a study “the overall computational power a quantum computer is doubled every time we add a quantum to a quantum computer”. Fact: “If we have a 300 qubit quantum computer it would be more powerful than all the computers in the world put together”.

The two biggest contenders’ in this field are IBM and Google and have already switched to work with Superconducting computers rather than parallel and distributed computers. They use their systems at subzero temperatures to avoid any resistance and provide better throughput. The future of parallelism may still be of great application in many fields and computers, but the concept of parallel computers and distributed systems may be on the verge of extinction a decade down the line. It is safe to say that the introduction of the aforementioned computing methodologies proves that parallelism is here to stay but may soon enough be replaced by quantum parallelism.

Embarrassingly Parallel Computations

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Embarrassingly-Parallel-Computations

Did you ever imagine that there would be such an adverb in front of the term ‘parallel
computations’? Surprising right!! (no offence, I would say it a little weird too) Let’s dig
what’s more is coming out of it.

This topic is named after a workload problem that arises in the parallel job’s computation
environment in an acceptable way. It is an alternative used to say that it is embarrassingly
easy to do. Any parallel algorithm is called embarrassing when it can execute all by itself
without any dependency on peers nor communication from any external source. It even
needs less intervention while breaking a task into sub-components to make it execute like a parallel problem. This type of parallel paradigm helps in multiple executions of other jobs
and creating a job that is more professional made by itself. The efficiency resulted from
these tasks are more reliable and they are highly suitable for distributed computing
ambience. Thus, environments are actually present in supercomputer clusters with the least infrastructural designs and support the embarrassingly parallel algorithms.

The standard type of these algorithms can be expressed with the following rules:

  • To achieve this name, the foremost quality of these algorithms is that the systematic
    detailing of the computation steps is pre-defined in this model.
  • The entire system consists of sub-modules and sub-tasks. So, each of these
    components have to stored uniquely in different memory space.
  • Independency of the algorithm can be obtained by provision of a clear routing path for
    the computation.
  • To avoid intermediate conversations or communications between the submodules,
    the initial and end nodes used in the computation are responsible for doing this job at
    necessary intervals or pre-fixed periods.

Unlike the minimal outcomes out of these parallel algorithms, it has more of a bright side
for the complex connected distributed platforms. Another great advantage with these types
of algorithms is that they help to retribute the most common problems like parallel slowdown and parallel overhead in a well-maintained subtle background. In recent times, it is advanced to act dynamically by configuring a master and slave components in the parallel computations.

The most common application of the embarrassingly parallel computations is the rendering
of a 3D image using the ray tracing algorithm. This process is handled by the graphic processing unit (GPU) that is specially designed to support the high computation speed with the least level of resources embedded in it. As there are multiple sub-components inside this pixel ling and printing job, the calculations involve a series of mathematical derivations and geometrical operations for moving the variables. As all these guidelines are specified and trained ahead in the computation process, they switch between the operations like shifting, scaling, rotation and clipping of the associated variables.

Other non-prominent applications also include protein folding and unfolding software,
password cracking and some significant calculations like Mandelbrot set and Monte Carlo
calculations.

The upgraded version of the Monte Carlo was proposed as Markov Chain Monte Carlo
(MCMC) algorithm. It looks like it is an advanced model of algorithm that can produce the
asymptotically correct samplings and practically proved the speed up the achievement in
burn-in processes. It is also proposed that this model can be extended on multiple machines within minimal interconnected systems. This type of system is encouraged as a MapReduce setting, which is supported by multi-level organizations to boost their processing and performance levels in the computations field.

In another research work, it has been proved that the embarrassingly parallel search
techniques are simple and reliable to solve the constraint programming models in the field
of artificial intelligence. It is seen that main processes in constraint programming involves
the data propagation process followed by the search process. It constitutes two level
instances like the distributed followed by the parallelized one. If a variable flow consistently
between these two levels made up by the CP solver, then it is said to be consistent variable.

This approach knowingly or unknowingly solves the classical problems that have a higher
capacity rates at the multi-core servers and CPU’s. This not only helps to parallelize the
resolution of a problem but also benefits to modify the solver code (rewrite any parallel
source code) and also replay the resolution of the given problem.

These are some of the least known, interesting facts and under-appreciated applications of
the embarrassingly parallel computations.

Introducing Scalability to IoT

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Internet of Things (IoT) is a field that is making tremendous progress and it is believed that there would be nearly 25billion connected devices by the end of 2020. As this number is increasing exponentially, it is of paramount importance to scale from small to large deployments. The concept of scaling the IoT sensors above the leaf level is explored. IoT is being used everywhere right from smart homes, cities, and buildings, etc. We handle the scaling between them by a rather new parallel technology of a novel gateway that has been proposed in the year 2015 from edge nodes. These devices are also being considered for the purpose of prototype nodes in Exascale systems in the future and for complex machine learning tasks.

Within IoT we can categorize the types of devices as edge devices and gateways. Edge devices are the simplified devices that are used within an IoT system in massive quantities. An edge device is a limited resource device typically designed to perform a single function. These devices collect data and transmit data to one or more gateways. The device may also perform a given task delegated to them from the gateway.

The second primary device group is gateways. Gateways are also termed as middlemen between an edge device and the Internet. Each gateway device is responsible for the collection of data from a certain set of edge devices and for using the collected data to perform a designated task or tasks as well as reporting “findings” to a higher level aggregation infrastructure like the cloud. On a practical comparison, it is quite evident that gateways are much more powerful devices than the edge devices. The value added by implementing parallelism on the GPU acceleration further added to the gateway is observed.

The algorithm focusses on two main aspects namely, is it possible to achieve a non-trivial increase in the validation of devices and can the number of edge devices increases without increasing the number of gateways by using data-parallel computations?

The answer to these questions has been addressed by the model in which we perform a data generation for a set of 50 devices and calculating the time needed to perform the linear regression on the model using the GPU tools. The execution for such a small number of devices proves that sequential execution proves to work better than the parallel implementation. AAS we increase the number of devices exponentially we observe that 25% speedup is observed while implementing the parallel workflow.

Enabling the gateway in the transport layer to perform parallel computations significantly improves the performance and further helps in connecting more number of edge nodes to a single gateway. It also allows the gateway to monitor the health of the edge nodes connected to them by comparing the data received and analysis performed.

We have a continuous stream of input and output in the case of IoT devices which makes it more suitable for parallel applications. If we have a continuous stream of inputs we can feed them to processors or threads in parallel and obtain the outputs desired. Even if there would be a change in the instruction type it would be easy to handle the instruction in parallel so that the system is not confusing (when running in serial).

We have two main workflow models that have been discussed and addressed here. Bounded and Unbounded streams of data. Bounded means that it has a defined start and end whereas the unbounded has a defined start but no defined end. The application of parallelism in these models of IoT has also proven to be quite fruitful as the processing and traversing of data is quite simple due to the indexing method followed and the speed of parallel search algorithms. All the above-mentioned functionalities make parallel computing most desirable for data collection, processing, and providing value to IoT based sensors. By the use of the model explained for increasing the number of edge nodes per gateway we also achieved the goal of introducing scalability to IoT.

Introduction to Parallel Computing

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Introduction-to-Parallel-Computing

The main aim or goal to increase the computations and efficiency in computers can be
achieved through a branch of science called Parallel computing. The alternative attributes
for these goals are also represented in terms of performance and scalability measurement.
So, here is a paper to discuss some of the parallel systems, their goals, especially on
challenges and its countermeasures in their application. Now let us understand some
terminology and deeper insight into these systems.

Parallel Systems is defined that the parallel systems comprise of an algorithm and the
parallel architecture that the algorithm is executed. The main attributes that are used as
metrics to it are runtime of computation, speedup, complexity, efficiency, cost, granularity,
portability, performance and scalability.

Some of the researchers have come up with their theories and proposed laws that helps to
represent them as notable numbers for each and combined attributes. Laws proposed by
Amdahl, Gustafson and reducing algorithm are most prominent ones. Of all above
mentioned computational runtime is the system’s key attribute to other higher-level
attributes like efficiency, performance and scalability are highly desired features to regulate
and access the parallel systems.

One of the main disadvantages of parallel systems is Parallel slowdown. It is a
the phenomenon in which the parallel algorithm is parallelized beyond a threshold point in
parallel computing and causing to run the entire program slowly.

Another major deprecating cause that pulls down the parallel systems is the parallel
overhead. The amount of time which is wasted to keep all the parallel tasks in coordination
then the time that actually is used to work a solution is called the parallel overhead. It
constitutes of:

  • Time to start up the task (ts )
  • Time to coordinate and synchronise between the parallel tasks (t syn )
  • Time to transfer the data between the processors (t data )
  • Software overhead to implement the parallel architecture in OS and libraries (S p )
  • Time is taken to terminate the task

Alongside these major drawbacks, there are other factors that challenge the parallel
systems like lacking dynamic topology selection based on the computing algorithm, more
power consumption, limitations of I/O units and memory in the systems and choosing
algorithms with higher efficiency for computation nodes.
The following countermeasures are proposed and followed to eradicate the drawbacks
uniquely to avoid further implications arrived through them.

Firstly, the parallel slowdown is a deficiency that is caused by blocking the computation
resources which thereby affects the whole system components. In order to understand the
diagnostic features under the system performance to improve the slowdown. The
process/thread that is involved with any of these components:

  • CPU bound (needs more of CPU resources)
  • Memory bound (requires RAM resources)
  • I/O bound (Network and/or hard drive resources)

The above-mentioned resources are said to be limited unless the computation is never
shared with any virtual or remote services. Also, every feature is vital because the access
lock on any one of them could lead to a closure path for another resource type. As the
chances of slowdown occurring are high in a multi-threaded environment, it is highly advised
to create more singleton applications where there exists the least dependency between the
resources accessed by those threads.

To retribute the causes and drawbacks of parallel overhead, the main sources that stand as
pillars for it has to be suppressed. The main sources of parallel overhead are:

  • Inter Processor communication
  • Load imbalance
  • Extra computation

The ways that are possible for the reduction of parallel overhead are to increase the parallel
performance. That is:

1. The data locality can be decreased to maximize the communications, and therefore
they can reduce the overhead.
2. Load balancing the distribution of work among the processors should be made
uniform and parallelism is achieved from this to make it less effect prone from the
overhead.
3. Modification of the sequential algorithm may result in extra computational work
leading to overhead. This can be reduced by focusing of parallelizable points like
loops and recursive calls and make the algorithm run parallel in a more efficient way.

Another underlying development like improving the querying techniques in the parallel
query executions, by reducing the number of CPU’s execution in the interdependent
database environment. This has tables in its system where there is a chance of exchanging
the information through complex connections between the dependent tables inside it.
Usage of other strategies like performance comparisons, increase in the parallel speedups
and comparative speedups are also some of the upcoming techniques to boost the
performance in parallel systems.

All the above-discussed features and techniques are interconnected which also determines
the consequences of parallel applications that further ameliorate the performance and
scalability.