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\begin{abstract}
High performance computing is needed for evaluating computationally "bulky" problems. For years, computers have progressed in architecture and hardware
aspects to exploit parallelism, but the algorithmic or software aspects are yet to be discovered fully in many fields. There are many aspects related to  
high performance computing, which we need to know before developing algorithms. Linear algebra lies at the heart of most calculations in scientific
computing. Thus there is a need for developing computationally "rich" algorithms for linear algebra. In this report we present some ways to exploit
parallelism for achieving high performance for linear algorithms, along with a summary of parallel processing and architecture overview with performance
evaluation analysis. 
\end{abstract} 
