some useful Questions in computer

[logistic_guy]

\(\displaystyle \ \bold{1.}\) List and briefly define some of the techniques used in contemporary processors to increase speed.
\(\displaystyle \ \bold{2.}\) Explain the concept of performance balance.
\(\displaystyle \ \bold{3.}\) Explain the differences among multicore systems, \(\displaystyle \text{MIC}\)s, and \(\displaystyle \text{GPGPU}\)s.
\(\displaystyle \ \bold{4.}\) Briefly characterize \(\displaystyle \text{Amdahl}\)’s law.
\(\displaystyle \ \bold{5.}\) Briefly characterize \(\displaystyle \text{Little}\)’s law.
\(\displaystyle \ \bold{6.}\) Define \(\displaystyle \text{MIPS}\) and \(\displaystyle \text{FLOPS}\).
\(\displaystyle \ \bold{7.}\) List and define three methods for calculating a mean value of a set of data values.
\(\displaystyle \ \bold{8.}\) List the desirable characteristics of a benchmark program.
\(\displaystyle \ \bold{9.}\) What are the \(\displaystyle \text{SPEC}\) benchmarks?
\(\displaystyle \bold{10.}\) What are the differences among \(\displaystyle \textcolor{red}{\text{base}}\) metric, \(\displaystyle \textcolor{blue}{\text{peak}}\) metric, \(\displaystyle \textcolor{green}{\text{speed}}\) metric, and \(\displaystyle \textcolor{purple}{\text{rate}}\) metric?

 

[khansaheb]

\(\displaystyle \ \bold{1.}\) List and briefly define some of the techniques used in contemporary processors to increase speed.
\(\displaystyle \ \bold{2.}\) Explain the concept of performance balance.
\(\displaystyle \ \bold{3.}\) Explain the differences among multicore systems, \(\displaystyle \text{MIC}\)s, and \(\displaystyle \text{GPGPU}\)s.
\(\displaystyle \ \bold{4.}\) Briefly characterize \(\displaystyle \text{Amdahl}\)’s law.
\(\displaystyle \ \bold{5.}\) Briefly characterize \(\displaystyle \text{Little}\)’s law.
\(\displaystyle \ \bold{6.}\) Define \(\displaystyle \text{MIPS}\) and \(\displaystyle \text{FLOPS}\).
\(\displaystyle \ \bold{7.}\) List and define three methods for calculating a mean value of a set of data values.
\(\displaystyle \ \bold{8.}\) List the desirable characteristics of a benchmark program.
\(\displaystyle \ \bold{9.}\) What are the \(\displaystyle \text{SPEC}\) benchmarks?
\(\displaystyle \bold{10.}\) What are the differences among \(\displaystyle \textcolor{red}{\text{base}}\) metric, \(\displaystyle \textcolor{blue}{\text{peak}}\) metric, \(\displaystyle \textcolor{green}{\text{speed}}\) metric, and \(\displaystyle \textcolor{purple}{\text{rate}}\) metric?

Please show us what you have tried and exactly where you are stuck.

Please follow the rules of posting in this forum, as enunciated at:

READ BEFORE POSTING​

Please share your work/thoughts about this problem

 

[logistic_guy]

\(\displaystyle \ \bold{1.}\) List and briefly define some of the techniques used in contemporary processors to increase speed.

The capabilities of modern computers are advancing rapidly, driving the development of new techniques aimed at boosting processor performance. This overview explores some of the key strategies employed in contemporary processors to enhance execution speed and efficiency.

Specifically, it highlights \(\displaystyle \bold{five}\) widely used techniques in modern processor design.

\(\displaystyle \textcolor{red}{\bold{1.}}\) Pipelining.
\(\displaystyle \textcolor{blue}{\bold{2.}}\) Branch Prediction.
\(\displaystyle \textcolor{green}{\bold{3.}}\) Superscalar Execution.
\(\displaystyle \textcolor{purple}{\bold{4.}}\) Data Flow Analysis.
\(\displaystyle \textcolor{orange}{\bold{5.}}\) Speculative Execution.

 

[logistic_guy]

\(\displaystyle \textcolor{red}{\bold{1.}}\) Pipelining.

Even though executing a single instruction only takes a few nanoseconds, if we execute each instruction one after the other, a lot of time is wasted. That time could be used more efficiently.

Pipelining solves this problem. It takes advantage of the fact that executing an instruction involves several stages like fetching, decoding, etc. With pipelining, the processor can work on multiple instructions at the same time, by handling a different stage of each instruction simultaneously.

 

[logistic_guy]

\(\displaystyle \textcolor{blue}{\bold{2.}}\) Branch Prediction.

To stay productive and use time more efficiently, the processor attempts to take on any available work. One way it does this is by predicting which branches of code are most likely to be executed next.

It makes these predictions by analyzing the instruction code to determine the likely order of branches. With advancements in technology, branch prediction has become increasingly sophisticated, allowing the processor to anticipate multiple branches ahead.

 

[logistic_guy]

\(\displaystyle \textcolor{green}{\bold{3.}}\) Superscalar Execution.

Superscalar execution uses multiple parallel pipelines, enabling the processor to execute more than one instruction per clock cycle where typically, only one instruction would be processed.

 

[logistic_guy]

\(\displaystyle \textcolor{purple}{\bold{4.}}\) Data Flow Analysis.

To avoid unnecessary delays in instruction flow, the processor analyzes dependencies between instructions such as those based on data or results. This allows it to create an optimized execution schedule, ensuring each instruction runs as soon as it's ready.

 

[logistic_guy]

\(\displaystyle \textcolor{orange}{\bold{5.}}\) Speculative Execution.

The processor can further optimize performance by combining \(\displaystyle \text{branch prediction}\) and \(\displaystyle \text{data flow analysis}\). This allows it to speculatively execute instructions it anticipates will be needed, ahead of time. The results of these speculative executions are stored in temporary locations for later use, in case the predictions were correct.

 

[logistic_guy]

\(\displaystyle \ \bold{2.}\) Explain the concept of performance balance.

As processors advanced rapidly, other essential computer components struggled to keep pace. This created a performance imbalance, necessitating adjustments in both organizational and architectural aspects to address the mismatch in capabilities and enhance the overall computing experience.

Key areas of design focus in this effort include memory-to-processor communication and the management of I/O devices.

 

[logistic_guy]

\(\displaystyle \ \bold{3.}\) Explain the differences among multicore systems, \(\displaystyle \text{MIC}\)s, and \(\displaystyle \text{GPGPU}\)s.

The emergence of multicore systems, \(\displaystyle \textcolor{red}{\bold{MICs}}\), and \(\displaystyle \textcolor{blue}{\bold{GPGPUs}}\) represents a more advanced approach to improving performance than traditional performance balancing techniques. The core idea behind these architectures is to utilize and interconnect multiple processing units more efficiently, rather than relying on increasing the complexity of a single processing unit.

However, each of these systems differs significantly in its implementation. Below is a brief overview of each:

\(\displaystyle \textcolor{black}{\bold{Multicore \ Systems}}\): Involve integrating multiple processor cores onto a single chip to enhance performance without increasing the clock frequency.

\(\displaystyle \textcolor{red}{\bold{MICs}}\) (Many Integrated Cores): Consist of a large number of identical cores integrated into a single chip. This homogeneous design offers substantial performance improvements due to the high core count.

\(\displaystyle \textcolor{blue}{\bold{GPGPUs}}\) (General-Purpose Computing on GPUs): Involves leveraging graphics processing units to perform general-purpose computations, extending their use beyond graphics to a broad range of CPU-like tasks.

 

[logistic_guy]

\(\displaystyle \ \bold{4.}\) Briefly characterize \(\displaystyle \text{Amdahl}\)’s law.

Amdahl's Law estimates the maximum expected speedup when using \(\displaystyle N\) parallel processors compared to a single processor. In this context, \(\displaystyle f\) represents the fraction of the total execution time that can be parallelized, while \(\displaystyle 1 - f\) corresponds to the portion of the task that must be executed sequentially.

The speedup is given by the formula:

\(\displaystyle \text{Speedup} = \frac{1}{(1 - f) + \frac{f}{N}}\)

From the equation, it's evident that increasing the number of processors \(\displaystyle (N)\) or the parallelizable portion of the code \(\displaystyle (f)\) leads to greater overall speedup. However, the sequential part \(\displaystyle (1 - f)\) ultimately limits the maximum achievable performance gain.

 

[logistic_guy]

\(\displaystyle \ \bold{5.}\) Briefly characterize \(\displaystyle \text{Little}\)’s law.

Little's Law describes a queuing system where items arrive at an average rate of \(\displaystyle \lambda\) per unit of time. Each item spends an average of \(\displaystyle W\) time units in the system, and at any given moment, there are on average \(\displaystyle L\) items in the system. Combining these elements gives the formula: \(\displaystyle L = \lambda \times W\)

 

[logistic_guy]

\(\displaystyle \ \bold{6.}\) Define \(\displaystyle \text{MIPS}\) and \(\displaystyle \text{FLOPS}\).

\(\displaystyle \bold{MIPS}\) (Millions of Instructions Per Second): a metric for processor performance that indicates how quickly instructions are executed. It relates the processor’s clock rate and CPI through the following formula:

\(\displaystyle \bold{MIPS}\) rate \(\displaystyle = \frac{f}{\text{CPI} \times 10^6}\)



\(\displaystyle \bold{FLOPS}\) (Floating-Point Operations Per Second) or \(\displaystyle \bold{MFLOPS}\) (Millions of FLOPS): a processor performance metric that specifically measures the execution rate of floating-point operations. It is calculated using the following formula:

\(\displaystyle \bold{FLOPS}\) rate \(\displaystyle = \frac{\text{number of operations}}{\text{execution time}}\)

 

[logistic_guy]

\(\displaystyle \ \bold{7.}\) List and define three methods for calculating a mean value of a set of data values.

The \(\displaystyle \textcolor{red}{\bold{arithmetic \ mean}}\) is calculated by summing all the element values \(\displaystyle x_i\) and dividing the total by the number of elements \(\displaystyle n\). This method is appropriate when the total sum itself holds meaningful or relevant information.

\(\displaystyle \textcolor{black}{\bold{arithmetic \ mean}} = \frac{1}{n}\sum_{i=1}^{n}x_i\)

 

[logistic_guy]

\(\displaystyle \ \bold{7.}\) List and define three methods for calculating a mean value of a set of data values.

The \(\displaystyle \textcolor{red}{\bold{geometric \ mean}}\) follows a concept closely related to the arithmetic mean but uses multiplication and roots instead of addition. It is especially applicable when the values are interdependent or vary widely.

\(\displaystyle \textcolor{black}{\bold{geometric \ mean}} = \text{exp}\left(\frac{1}{n}\sum_{i=1}^{n}\ln x_i\right)\)

 

[logistic_guy]

\(\displaystyle \ \bold{7.}\) List and define three methods for calculating a mean value of a set of data values.

The \(\displaystyle \textcolor{red}{\bold{harmonic \ mean}}\) is used when analyzing rates, as it gives greater weight to smaller values, making it especially useful for quantities like execution times where lower values are more significant.

\(\displaystyle \textcolor{black}{\bold{harmonic \ mean}} = \frac{n}{\sum_{i=1}^{n} \frac{1}{x_i}}\)