We consider the question: what is the abstraction that should be implemented by the
computational engine of a machine learning system? Current machine learning systems …

We consider the question: what is the abstraction that should be implemented by the
computational engine of a machine learning system? Current machine learning systems …

When numerical and machine learning (ML) computations are expressed relationally,
classical query execution strategies (hash-based joins and aggregations) can do a poor job …

Multidimensional arrays are a fundamental abstraction to represent data across scientific
domains ranging from astronomy to genetics, medicine, business intelligence, and …

Operator fusion is essentially and widely used in a large number of matrix computation
systems in science and industry. The existing distributed operator fusion methods focus on …

The resurgence of machine learning has increased the demand for high‐performance basic
linear algebra subroutines (BLAS), which have long depended on libraries to achieve peak …

As matrix computation becomes increasingly prevalent in large-scale data analysis,
distributed matrix computation solutions have emerged. These solutions support query …

在大数据治理应用中, 数据分析是必不可少的一环, 且具有耗时长, 计算资源需求大的特点, 因此,
优化其执行效率至关重要. 早期由于数据规模不大, 数据分析师可以利用传统的矩阵计算工具 …

With the increasing of matrix size in large-scale data analysis, a series of Spark-based
distributed matrix computation systems have emerged. Typically, these systems split a matrix …

Distributed matrix computation is common in large-scale data processing and machine
learning applications. Existing systems that support distributed matrix computation already …