Comparison of checkpointed aided parallel execution against MapReduce

International audience; Researchers have been actively working for the past few decades in parallelizing programs so as to cut through massive data chunks for faster response. Current day processors are faster and have more number of cores. So as to utilize the computational capabilities of the processors to its full extend, processes need to be run in parallel. There are several advantages for parallel programs over sequential programs. In sequential programming, the processes execute in a sequential order one after the other. But in parallel programming, we have multiple processes and threads that execute simultaneously at the same time. A task can be performed in lesser time by using parallel programming. But writing a parallel programming manually is a difficult and time consuming task. So we have to use tools to convert a sequential program to a parallel one automatically. OpenMP (Open Multi- Processing) is a set of directives which can be used to generate parallel programs written in C, C++, and FORTRAN to an efficient parallel program. CAPE ( Checkpointing Aided Parallel Execution) is a new paradigm that uses checkpointing technique to generate parallel programs from sequential programs provided with OpenMP directives. is a programming model for performing parallel processing. The main purpose of this paper is to compare the performance and coding complexity of against CAPE under different levels of difficulties