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The evolution of digital libraries and the Internet has dramatically transformed the processing , storage, and retrieval of information. Efforts to digitize text, images, video, and audio now consume a substantial portion of both academic and industrial activity. Even when there is no shortage of textual materials on a particular topic, procedures for(More)
Memory bandwidth limits the performance of important kernels in many scientific applications. Such applications often use sequences of Basic Linear Algebra Subprograms (BLAS), and highly efficient implementations of those routines enable scientists to achieve high performance at little cost. However, tuning the BLAS in isolation misses opportunities for(More)
The performance bottleneck for many scientific applications is the cost of memory access inside linear algebra kernels. Tuning such kernels for memory efficiency is a complex task that reduces the productivity of computational scientists. Software libraries such as the Basic Linear Algebra Subprograms (BLAS) ameliorate this problem by providing a standard(More)
The increasing gap between processor performance and memory access time warrants the re-examination of data movement in iterative linear solver algorithms. For this reason, we explore and establish the feasibility of modifying a standard iterative linear solver algorithm in a manner that reduces the movement of data through memory. In particular, we present(More)