SLOPE: Structural Locality-Aware Programming Model for Composing Array Data Analysis

MapReduce brought on the Big Data revolution. However, its impact on scientific data analyses has been limited because of fundamental limitations in its data and programming models. Scientific data is typically stored as multidimensional arrays, while MapReduce is based on key-value (KV) pairs. Applying MapReduce to analyze array-based scientific data requires a conversion of arrays to KV pairs. This conversion incurs a large storage overhead and loses structural information embedded in the array. For example, analysis operations, such as convolution, are defined on the neighbors of an array element. Accessing these neighbors is straightforward using array indexes, but requires complex and expensive operations like self-join in the KV data model. In this work, we introduce a novel ‘structural locality’-aware programming model (SLOPE) to compose data analysis directly on multidimensional arrays. We also develop a parallel execution engine for SLOPE to transparently partition the data, to cache intermediate results, to support in-place modification, and to recover from failures. Our evaluations with real applications show that SLOPE is over ninety thousand times faster than Apache Spark and is \(38\%\) faster than TensorFlow.