The Evaluation of Data Distributions for Multi-Dimensional Sparse Arrays Based on the EKMR Scheme

In our previous work, we have studied the performance of three data distribution schemes, Send Followed Compress (SFC), Compress Followed Send (CFS), and Encoding-Decoding (ED), for sparse arrays based on the traditional matrix representation (TMR) scheme. Since multi-dimensional arrays can also be represented by the extended Karnaugh map representation (EKMR) scheme, in this paper, we first apply the SFC, the CFS, and the ED schemes for multi-dimensional sparse arrays based on the EKMR scheme. Then, we compare the performance of these three schemes based on the EKMR scheme with those based on the TMR scheme. Both theoretical analysis and experimental test were conducted. In theoretical analysis, we analyze the SFC, the CFS, and the ED schemes based on the TMR and the EKMR schemes in terms of the data distribution time and the data compression time. In experimental test, we implemented these three schemes based on the TMR and the EKMR schemes on an IBM SP2 parallel machine. From the experimental results, the ED scheme outperforms the CFS scheme that outperforms the SFC scheme for most of test sparse arrays. The SFC, the CFS, and the ED schemes based on the EKMR scheme outperform those based on the TMR scheme, respectively.