Kashmir: Occupation and Resistance

Kashmir became a part of the Muslim World in the 14th century C E. Under the local Sultans (1325-1585) , the Mughals (1586-1752) and the Pathans (1752-1819) , it developed into a state and society with its own peculiar Islamic culture. Kashmir was colonised by the Sikhs (1819-46) from the Punjab and the Hindu Dogra Maharajas (1846-1947) . The Muslim Kashmir stood for Islam through the freedom movement during this period. When the movement under the banner of Islam was at its apex and it seemed that the mahkutn (colonised / enslaved) Kashmir was about to achieve the goal, India ( Bharat) , a newly born nation (Aug 15, 1947) , invaded it on Oct. 27, almost 3 months after her own birth. Since then this newly emerging occupying -power through its policies of political deceit, cultural aggression, economic onslaught and delslamizing educational schemes has given rise to the problem of the IOK (Indian Occupied Kashmir) . The paper narrates the story of the past up to 1988-uprising & throws light on the oppression, persecution and discrimination that the Muslim Kashmir has been witnessing. The form offreedom-movement that followed from 1947 to 1989 is also outlined. In 1989 Jihad Kashmir, a new phase of the movement that invited the attention of the world begins. The paper helps to understand the background ofthe genesis ofthis new phase

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.