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Accelerating sequential algorithms in order to achieve high performance is often a nontrivial task. However, there are certain properties that can exacerbate this process and make it particularly daunting. For example, building an efficient parallel solution for a data-intensive algorithm requires a deep analysis of the memory access patterns and data reuse potential. Attempting to scale out the computations...
Recent advancements in machine learning algorithms have transformed the data analytics domain and provided innovative solutions to inherently difficult problems. However, training models at scale over large data sets remains a daunting challenge. One such problem is the detection of overlapping communities within graphs. For example, a social network can be modeled as a graph where the vertices and...
Glass wing is a MapReduce framework that uses OpenCL to exploit multi-core CPUs and accelerators. However, compute device capabilities may vary significantly and require targeted optimization. Similarly, the availability of resources such as memory, storage and interconnects can severely impact overall job performance. In this paper, we present and analyze how MapReduce applications can improve their...
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