For PageRank, SSSP, and connected components, iiHadoop reduces the runtime of computation by approximately 60% compared to Hadoop and HaLoop, as shown in Fig. 5a c.
The issue of interactive evolutionary computation started approximately from 1980 and it was first applied to art, graphics and animation and then to various areas such as industrial design, information retrieval, games, robotics, etc. [8].
The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine.
While this SC-BLSOM had an evidently reduced computation time (approximately one-fortieth part of the conventional BLSOM), this condition exhibited lower clustering performance.
The SC-BLSOM accomplished a reduced computation time approximately one-sixth of the time required for a conventional BLSOM, and as for the clustering performance the SC-BLSOM showed an improvement of about 3% over the conventional BLSOM.
Our analysis may lack sufficient power to detect a significant correlation between pediatric workforce density and dialysis initiation, given the relatively low number of children initiated on dialysis annually and missing data in USRDS, which prevented eGFR computation in approximately ten percent of the pediatric study population.
As seen in the previous section, iiHadoop 1.2.1 reduces the run time of iterative and incremental computations by approximately 23% compared to i2MapReduce.
As shown in Fig. 6, iiHadoop 2.7.3 reduces the run time of iterative and incremental computations by approximately 17 and 19% respectively compared to iiHadoop 1.2.1.
Thus we have a multiplier for the number of stage computations of approximately L for memory locations.
As with the L-level algorithm of Wheeler and Hughey (2000), with the optimal checkpointing algorithm, we have a multiplier for the number of stage computations of approximately L for memory locations.
