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Learning the causal structures from observational data is an omnipresent challenge in data science. The amount of observational data available to Causal Structure Learning (CSL) algorithms is increasing as data is collected at high frequency from many data sources nowadays. While processing more data generally yields higher accuracy in CSL, the concomitant increase in the runtime of CSL algorithms hinders their widespread adoption in practice. CSL is a parallelizable problem. Existing parallel CSL algorithms address execution on multi-core Central Processing Units (CPUs) with dozens of compute cores. However, modern computing systems are often heterogeneous and equipped with Graphics Processing Units (GPUs) to accelerate computations. Typically, these GPUs provide several thousand compute cores for massively parallel data processing.
To shorten the runtime of CSL algorithms, we design efficient execution strategies that leverage the parallel processing power of GPUs. Particularly, we derive GPU-accelerated variants of a well-known constraint-based CSL method, the PC algorithm, as it allows choosing a statistical Conditional Independence test (CI test) appropriate to the observational data characteristics.
Our two main contributions are: (1) to reflect differences in the CI tests, we design three GPU-based variants of the PC algorithm tailored to CI tests that handle data with the following characteristics. We develop one variant for data assuming the Gaussian distribution model, one for discrete data, and another for mixed discrete-continuous data and data with non-linear relationships. Each variant is optimized for the appropriate CI test leveraging GPU hardware properties, such as shared or thread-local memory. Our GPU-accelerated variants outperform state-of-the-art parallel CPU-based algorithms by factors of up to 93.4× for data assuming the Gaussian distribution model, up to 54.3× for discrete data, up to 240× for continuous data with non-linear relationships and up to 655× for mixed discrete-continuous data. However, the proposed GPU-based variants are limited to datasets that fit into a single GPU’s memory. (2) To overcome this shortcoming, we develop approaches to scale our GPU-based variants beyond a single GPU’s memory capacity. For example, we design an out-of-core GPU variant that employs explicit memory management to process arbitrary-sized datasets. Runtime measurements on a large gene expression dataset reveal that our out-of-core GPU variant is 364 times faster than a parallel CPU-based CSL algorithm. Overall, our proposed GPU-accelerated variants speed up CSL in numerous settings to foster CSL’s adoption in practice and research.
In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience.