Why the speedup of a parallel algorithm will eventually reach some limit.?

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The speedup of a parallel algorithm refers to the improvement in performance achieved by executing the algorithm on multiple processors simultaneously. Parallel algorithms have become increasingly important in the field of computer science as they offer the potential to solve complex problems more efficiently. However, despite the advantages of parallel computing, there is a limit to how much speedup can be achieved. This article will explore the reasons why the speedup of a parallel algorithm eventually reaches a limit.

Factors Affecting Speedup

There are several factors that contribute to the eventual limit of speedup in parallel algorithms:

1. Amdahl’s Law: Amdahl’s Law states that the speedup of a parallel algorithm is limited by the portion of the algorithm that cannot be parallelized. In other words, if a certain portion of the algorithm must be executed sequentially, no matter how many processors are used, the overall speedup will be limited by the sequential portion.

2. Communication Overhead: In parallel computing, communication between processors is necessary to share data and coordinate tasks. However, this communication introduces overhead, which can reduce the overall speedup. As the number of processors increases, the amount of communication required also increases, eventually leading to diminishing returns.

3. Load Imbalance: Load imbalance occurs when the workload is not evenly distributed among the processors. Some processors may finish their tasks quickly and be idle while others are still working. This idle time reduces the overall speedup as the processors are not fully utilized.

Parallel Efficiency

Parallel efficiency is a measure of how effectively a parallel algorithm utilizes the available resources. It is defined as the ratio of the speedup achieved to the maximum possible speedup. As the number of processors increases, the parallel efficiency tends to decrease due to the factors mentioned above.

Parallel efficiency can be affected by various factors, including the algorithm design, the nature of the problem being solved, and the hardware architecture. In some cases, the algorithm itself may not be well-suited for parallelization, leading to lower efficiency and limited speedup.


Scalability refers to the ability of a parallel algorithm to maintain or improve its performance as the problem size or the number of processors increases. A scalable algorithm should be able to achieve a proportional increase in speedup as more processors are added.

However, the scalability of a parallel algorithm is often limited by the factors mentioned earlier. As the problem size or the number of processors increases, the communication overhead and load imbalance become more significant, leading to diminishing returns and reduced scalability.


While parallel algorithms offer the potential for significant speedup, there is a limit to how much improvement can be achieved. Factors such as Amdahl’s Law, communication overhead, and load imbalance contribute to this limit. As the number of processors increases, the efficiency and scalability of parallel algorithms tend to decrease. It is important to carefully analyze and design parallel algorithms to maximize their performance and overcome these limitations.


– Amdahl, G. M. (1967). Validity of the single processor approach to achieving large-scale computing capabilities. AFIPS Conference Proceedings, 30, 483-485.
– Gustafson, J. L. (1988). Reevaluating Amdahl’s Law. Communications of the ACM, 31(5), 532-533.
– Quinn, M. J. (2003). Parallel programming in C with MPI and OpenMP. McGraw-Hill Education.
– Hill, M. D., & Marty, M. R. (2008). Amdahl’s Law in the multicore era. Computer, 41(7), 33-38.