Parallel Processing Design of Latent Semantic Analysis Based Essay Grading System with OpenMP

In this paper, a parallel processing design for an short-answer essay grading system based on Latent Semantic Analysis (LSA) is presented. This system was developed to ease the lecturers for short-essay assessment process. With the number of documents to be assessed in an exam period, efficiency of processing and time are needed. Parallel processing is a solution for advancing the system to be implemented for wide scope of use. The design was implemented using OpenMP Application Programming Interface (API) to achieve this research purpose. Most of the system's processes utilize the outer loop or repetitive process with data level parallelism to process the same process for different data at a time. Early experiment on SVD process has promising result for parallelization implementation compared to the serial process. With parallelization, SVD could process 50 documents in 1.773 microseconds (ms), 3 times faster than the serial process in 5.676 ms. There were 2 other experiments to obtain the optimum number of matrix size and number of threads for parallelization; the experiment showed that two threads division on SVD process is the best option for this system and more threads are appropriate for more complex implementation and larger LSA based system.