Comparison of Shared memory based parallel programming models

Abstract: Parallel programming models are quite challenging and emerging topic in the parallel computing era. These models allow a developer to port a sequential application on to a platform with more number of processors so that the problem or application can be solved easily. Adapting the applications in this manner using the Parallel programming models is often influenced by the type of the application, the type of the platform and many others. There are several parallel programming models developed and two main variants of parallel programming models classified are shared and distributed memory based parallel programming models. The recognition of the computing applications that entail immense computing requirements lead to the confrontation of the obstacle regarding the development of the efficient programming models that bridges the gap between the hardware ability to perform the computations and the software ability to support that performance for those applications [25][9]. And so a better programming model is needed that facilitates easy development and on the other hand porting high performance. To answer this challenge this thesis confines and compares four different shared memory based parallel programming models with respect to the development time of the application under a shared memory based parallel programming model to the performance enacted by that application in the same parallel programming model. The programming models are evaluated in this thesis by considering the data parallel applications and to verify their ability to support data parallelism with respect to the development time of those applications. The data parallel applications are borrowed from the Dense Matrix dwarfs and the dwarfs used are Matrix-Matrix multiplication, Jacobi Iteration and Laplace Heat Distribution. The experimental method consists of the selection of three data parallel bench marks and developed under the four shared memory based parallel programming models considered for the evaluation. Also the performance of those applications under each programming model is noted and at last the results are used to analytically compare the parallel programming models. Results for the study show that by sacrificing the development time a better performance is achieved for the chosen data parallel applications developed in Pthreads. On the other hand sacrificing a little performance data parallel applications are extremely easy to develop in task based parallel programming models. The directive models are moderate from both the perspectives and are rated in between the tasking models and threading models.