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Matrix multiplication is a dominant but very time-consuming operation in many big data analytic applications. Thus its performance optimization is an important and fundamental research issue. The performance of large-scale matrix multiplication on distributed data-parallel platforms is determined by both computation and IO costs. For existing matrix multiplication execution strategies, when the execution...
Matrix computation is the core of many massive data-intensive analytical applications such mining social networks, recommendation systems and nature language processing. Due to the importance of matrix computation, it has been widely studied for many years. In the Big Data ear, as the scale of the matrix grows, traditional single-node matrix computation systems can hardly cope with such large data...
In a new era of Big Data, the rapid growth of the applications, such as social media and web-search, requires efficient and scalable machine learning and statistical analytical algorithms. However, there lacks easy-to-use and efficient software frameworks or systems that can support fast development of such big data analytical algorithms. To solve these problems, we propose Octopus, an easy-to-use...
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