Coarse- and Fine-Grained Parallelism

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Table of Contents

Introduction
Coarse-Grained Parallelism
Fine-Grained Parallelism
Limits of Parallel Programming
Conclusion
References

Introduction

Parallelism is one of the most important programming concepts that allow for improved performance and more efficient applications. The two main approaches in this process are coarse-grained parallelism and fine-grained parallelism. Each of these has its benefits and limitations, which will be discussed in this paper. Parallel programming is a highly useful tool for solving complex tasks quickly and efficiently. However, there are also limits to parallel programming that need to be taken into consideration when planning and using parallel software projects.

Coarse-Grained Parallelism

Coarse-grained parallelism uses fewer threads and consumes less computational time than is utilized in fine-grained parallelism. Coarse-grained parallelism involves larger parts of the program for parallel processing and requires a small number of threads to perform tasks (Yoon et al., 2019). This method is useful for large and complex tasks such as data processing and machine learning due to its ability to divide tasks into smaller parts and accelerate their execution. One of the main advantages of coarse-grained parallelism is its efficiency utilized in working with large and complex tasks (Gao et al., 2019). Coarse-grained parallelism can divide tasks into small parts and accelerate their completion with the use of a low number of threads.

However, coarse-grained parallelism is not void of drawbacks, one of them being its inefficiency when working with small and simple tasks. Coarse-grained parallelism is used in OpenMP and MPI (Yoon et al., 2019). OpenMP is a library for creating programs that can use parallel code execution on multiple processors. OpenMP utilizes atomic commands to lock and unlock threads. Meanwhile, MPI is a library for creating programs that can use parallel code execution on multiple computers by generating messages to transfer data between processors.

Fine-Grained Parallelism

Fine-grained parallelism uses more threads and consumes more computational time than coarse-grained parallelism does. Fine-grained parallelism uses smaller parts of the program for parallel processing and requires a large number of threads to perform tasks (Chen et al., 2022). It is useful for small and simple tasks such as parallel calculations and image processing since it can divide tasks into smaller parts, leading to greater performance. One of the main assets of fine-grained parallelism is its efficiency when working with small and simple tasks (Jiang et al., 2020). It can divide tasks into small parts and achieve greater performance using a large number of threads.

However, one of the limitations of fine-grained parallelism is a large number of threads, which can slow down the programs execution. Fine-grained parallelism is used in OpenCL and CUDA. OpenCL is a library for creating programs that can use parallel code execution on multiple processors and graphics processors, which uses cores for multithreaded data processing (Chen et al., 2022). CUDA is a library for creating programs that can use parallel code execution on multiple graphics processors (Jiang et al., 2020). This library utilizes cores for multithreaded data processing and provides high performance.

Limits of Parallel Programming

Parallel programming allows multiple tasks to be executed simultaneously by providing significant improvements in computing performance by parallelizing them across multiple threads. However, there are certain limitations to parallel programming that need to be taken into consideration when using this tool. The drawbacks of parallel programming include the lower and upper limits (Kumar, 2022). The lower limit is the one at which the cost of launching a parallel process to solve the problem will not incur any computational costs. In its turn, the upper limit marks the point at which the computational costs of parallelism start to outweigh the benefits of using it.

The lower limit of parallel programming means the cost of launching a parallel process to solve a problem without additional computational costs. Since launching a parallel process will take some time and consume resources, it is relevant to use parallel programming only for sufficiently large tasks (Kumar, 2022). If the problem is too small, the time it takes to start and the resources can exceed the benefits of parallel processing. Meanwhile, the upper limit of parallel programming incorporates the ratio between computational costs and the benefits of using a parallel process. For example, if the completion time is reduced by 50% and the parallel process launching time is increased by 20%, the computational costs may be high enough to gain the benefit of the parallel process.

Therefore, it is very important to assess the benefits of parallel processing carefully before launching a parallel process. Additional unforeseen costs related to parallel programming should also be taken into account. For instance, parallel programming requires more memory to launch and use multiple threads (Kumar, 2022). This can affect performance and needs additional costs for buying additional memory. Also, parallel programming requires more code, which will lead to additional effort for design and implementation.

Conclusion

In this paper, two approaches to parallelism have been considered: coarse-grained parallelism and fine-grained parallelism. Each of them has its benefits and limitations, which can be used to determine the best approach for the task at hand. However, when designing and implementing parallel software projects, it is necessary to take into account the limits of parallel programming. The first of the latter includes the lower limit, at which the cost of launching a parallel process to solve the problem will not be worth the computational costs. The second disadvantage is the upper limit, at which the computational costs for parallelism start to outweigh the benefits of using it. Only with proper planning and application of parallel programming, is it possible to achieve maximum performance.

References

Chen, Q., Tian, B., & Gao, M. (2022). FINGERS: Exploiting fine-grained parallelism in graph mining accelerators. ASPLOS 22: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 4355. Web.

Gao, M., Yang, X., Pu, J., Horowitz, M., & Kozyrakis, C. (2019). TANGRAM: Optimized coarse-grained dataflow for scalable NN accelerators. ASPLOS 19: Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, 807820. Web.

Jiang, W., Zhang, Y., Liu, P., Peng, J., Yang, L. T., Ye, G., & Jin, H. (2020). Exploiting potential of deep neural networks by layer-wise fine-grained parallelism. Future Generation Computer Systems, 102, 210-221. Web.

Kumar, S. (2022). Introduction to parallel programming. Cambridge University Press.

Yoon, D.-H., Kang, S.-K., Kim, M., & Han, Y. (2019). Exploiting coarse-grained parallelism using cloud computing in massive power flow computation. Energies, 11(9), 2268. Web.

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