Numerical Problem on Processor Pool Model

In summary, for a distributed computing system based on the processor-pool model with ##P## processors, the maximum gain in speed for a computing job consisting of ##F## source files is given by (Ft - t)/Ft, where 't' is the time required by each processor to complete the job. However, in a real-world scenario, other factors such as varying compile times and library requirements, and the serial activity of linking and creating the load image, may cause the actual gain in speed to be less than the maximum. This can be explained by Amdahl's law.
  • #1
22990atinesh
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Consider the case of a distributed computing system based on the processor-pool model that has ##P## processors in the pool. In this system, suppose a user start a computing job that involves compilation of a program consisting of ##F## source file ##(F < P)##. Assume that at this time the user is the only user using the system. What maximum gain in speed can be hoped for this job in this systems compared to its execution on a single processor system ? What factors might cause the gain in speed to be less than this maximum ?

Attempt:
Let 't' secs are required by each processor in processor pool model to complete the job. Hence overall 't' secs will be needed as in processor pool model as all processor are running in parallel.

In case of single processor system time required will be = Ft s

Hence gain in speed is = (Ft - t)/Ft

is it correct or I'm assuming sth wrong. Can anybody help.
 
  • #3
Your reasoning is fine if you make the assumption that every file requires the same time to compile, that there is no linking and loading of the compiled modules into a single runtime image, and that there are no resources that are competed for. In this case you have found an expression for the maximum, best case speedup.

In a real-world situation there's likely to be a range of compile times and library requirements, and the link/load step would require access to all the compiled modules after compilation.

The compilations would then be finished when the longest module is done, and linking could only start when that is complete. Linking and creating/writing the load image would likely involve some irreducible serial activity. Refer to Amdahl's law.
 
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1. What is a Processor Pool Model?

The Processor Pool Model is a mathematical model used in computer science to analyze the performance of a multi-processing system. It involves a pool of processors that are shared among multiple tasks or processes, and the model helps determine the efficiency and speed of the system.

2. How is the Processor Pool Model used in real-world applications?

The Processor Pool Model is used in various industries, such as telecommunications, banking, and transportation, to optimize the use of resources and improve system performance. It is often used in designing and analyzing computer networks, as well as in developing scheduling algorithms for high-performance computing.

3. What are the key parameters in the Processor Pool Model?

The key parameters in the Processor Pool Model include the number of processors in the pool, the arrival rate of tasks, the processing time of each task, and the scheduling algorithm used. These parameters can be adjusted to simulate different scenarios and analyze their impact on system performance.

4. How does the Processor Pool Model handle varying workload?

The Processor Pool Model can handle varying workload by adjusting the number of processors in the pool. If the workload increases, more processors can be added to the pool to improve performance. Similarly, if the workload decreases, some processors can be removed from the pool to save resources.

5. What are the limitations of the Processor Pool Model?

While the Processor Pool Model can provide valuable insights into the performance of a multi-processing system, it has some limitations. It assumes that tasks arrive and are processed independently, which may not always be the case in real-world applications. It also does not take into account factors such as system failures and communication delays, which can affect the overall performance.

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