عہد نبوی میں نوجوان صحابہ کرام کا بطور معلم تقرر اور اصلاح معاشرہ میں ان کا کردار

It is above-board that teachers play an important role in forming, formulating, molding and developing the society as individuals and as a whole. The youth has ever been an icon to lead the community in every sphere of life. The young stuff has played the pivotal role in preaching, scribing, teaching, political, economic and even diplomatic fields. The present research article explores the role of various companions of Holy Prophet (r) in these fields. Firstly, the Holy Prophet (r) groomed his companions, stormed their brains and paved them on the Divine way, then sent them to the said fields to work. Among those companions, Ḥaḍrat Muṣ‘ab bin ‘Umayr, Mu‘ādh bin Jabal, ‘Abdullāh ibn e Maktūm, Rāfi‘ bin Mālik, ‘Abdullāh ibn e Mas‘ūd, ‘Abdullāh ibn e ‘Abbās, Abū Sa‘īd Khudrī (y) as well as from females Ḥaḍrat ‘Āyshah, Ḥaḍrat Ḥafṣah, Shifā bint-e-‘Abdullah etc. Were appointed as preachers. Their task was not only to teach and educate the community rather to present themselves before them as paragon for their particular fields. The research concludes that the Prophet (r) laid down a criteria for selection of the teachers of Muslim Ummah. The selection criteria of the Prophet (r) was based not only on contingent variables but also on some special characteristics like teaching and training, potential empathy for the learners and a passion for social reformation. As a result, these preachers, after practicing their ideal and best performance, produced numerous educations, merchants, facilitators and reformers in the society. The present research paper will explore the companions’ efforts for the reformation of the society.

Performance Improvement of Parallel Sparse Matrix-Vector Product on Pc Cluster

The efficient parallelization of sparse matrix-vector product (SMVP) is of prime importance in scientific computing. To achieve this on a distributed memory computers, we concentrate on minimizing the inter-processor communication, achieving a good balance of workload, overlapping communication with computation along with optimizing single processor performance. The thesis consists of two parts presenting the optimization and improvement of sparse matrix-vector multiplication performance on single as well as multi processors. For the performance improvement of SMVP on a single scalar processor, we propose two sparse storage formats, namely the grouped compressed row storage with permutation (GCRSP) and the blocked compressed row storage with permutation (BCRSP). The proposed formats are designed to efficiently exploit the benefits of blocking such as reduced indirect addressing, increased spatial and temporal locality along with eliminating the corresponding overheads. For the good load balancing and low communication cost, reordering of sparse matrices according to their sparsity structure is highly important. For this purpose we proposed reordering based partitioning strategies that tend to exploit sparsity of input matrix presenting the balanced load distribution along with the reduced communication cost. It has been observed that GCRSP improves the performance over simple compressed row storage (CRS) and compressed row storage with permutation (CRSP) with an average of 16% and 25%, respectively. Moreover, due to blocking in BCRSP, the performance improvements of an average of 32%, 41% and 20% are observed over CRS, CRSP and GCRSP respectively. Likewise, the proposed partitioning models permuted row column matrix produce an average of 49% better load balancing and 14% better communication than the corresponding naïve row/column and checker board models. Moreover, they produce same level of balanced load and an average of 78% better communication than the corresponding balanced naïve partitioning i.e. row/column block and balanced checker board (BCH) models. On the whole an average of 30% performance gain for parallel SMVP is achieved by using BCRSP format along with permuted row partitioning over the implementation using CRS format with naïve row partitioning using cluster of eight processors.