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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 an important computational kernel in many applications. For performance improvement, software libraries designated for SpMV computation have been introduced, e.g., MKL library for CPUs and cuSPARSE library for GPUs. However, the computational throughput of these libraries is far below the peak floating-point performance offered by hardware platforms, because...
Recently, tapping into renewable energy sources has shown great promise in alleviating server energy poverty and reducing IT carbon footprint. Due to the limited, time-varying green power generation, matching server power demand to runtime power budget is often crucial in green data centers. However, existing studies mainly focus on the temporal variability of the power supply and demand, while largely...
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