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Text analytics applications using machine learning techniques have grown in importance with ever increasing amount of data being generated from web-scale applications, social media and digital repositories. Apart from being large in size, these generated data are often unstructured and are heavily sparse in nature. The performance of these applications on current systems is hampered by hard to predict...
Rapid growth of Internet led to web applications that produce large unstructured sparse datasets (e.g., texts, ratings). Machine learning (ML) algorithms are the basis for many important analytics workloads that extract knowledge from these datasets. This paper characterizes such workloads on a high-end server for real-world datasets and shows that a set of sparse matrix operations dominates runtime...
Sparse matrix vector multiplication (SpMV) is a linear algebra construct commonly found in machine learning (ML) algorithms, such as support vector machine (SVM). We profiled a popular SVM software (libSVM) on an energy-efficient microserver and a high-performance server for real-world ML datasets, and observed that SpMV dominates runtime. We propose a novel SpMV algorithm tailored for ML and a hardware...
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