The examination of causal structures is crucial for data scientists in a variety of machine learning application scenarios. In recent years, the corresponding interest in methods of causal structure learning has led to a wide spectrum of independent implementations, each having specific accuracy characteristics and introducing implementation-specific overhead in the runtime. Hence, considering a selection of algorithms or different implementations in different programming languages utilizing different hardware setups becomes a tedious manual task with high setup costs. Consequently, a tool that enables to plug in existing methods from different libraries into a single system to compare and evaluate the results is substantial support for data scientists in their research efforts.

Learning the causal structures from high-dimensional observational data is an omnipresent challenge in data science.State-of-the-art methods for constraint-based Causal Structure Learning (CSL) apply conditional independence (CI) tests to determine the underlying causal structures.In the context of discrete data, each CI test requires calculating the marginals over contingency tables based on the respective observations.This calculation leads to long overall execution times.In our work, we propose a parallel execution strategy tailored for constraint-based CSL on a Graphics Processing Unit (GPU) to accelerate the execution for discrete data.Hence, we introduce the gpuPC algorithm that performs all CI tests on a GPU and extends the existing parallel execution strategy for constraint-based CSL by calculating the marginals over contingency tables within units of threads.Further, gpuPC implements explicit memory management to handle the corresponding auxiliary data structures in GPU memory.An experimental evaluation shows that gpuPC scales well even for higher-dimensional settings, with auxiliary data structures exceeding on-chip memory.In particular, running on NVIDIA Tesla V100 hardware gpuPC outperforms a GPU baseline by factors of up to 45.6 and further outperforms existing parallel CPU-based implementations running on 40 cores by a factor of 62.1.

The efficiency of modern automotive body shop assembly lines is highly related to the reduction of downtimes due to failures and quality deviations within the manufacturing process. Consequently, the need for implementing tools into the assembly lines for on-line monitoring, and failure diagnosis, also under the prism of improving the troubleshooting, is of great importance. While the identification of root causes and elimination of failures is usually built upon individual on-site expert knowledge, causal graphical models (CGMs) have opened the possibility to make a purely data-driven assessment. In this demo, we showcase how a CGM of the production process is incorporated into a monitoring tool to function as a decision-support system for an operator of a modern automotive body shop assembly line and enables fast and effective handling of failures and quality deviations.

Largely, the performance of main-memory databases is limited by the growing memory gap. To be efficient, a DBMS cannot waste any bandwidth. However, this is exactly the case when 64-byte cache lines are transferred but only parts of these are used. We present a cache simulator that measures this waste, show that current databases waste up to 70% of the available bandwidth, and discuss a new gather instruction with sub-cache line access granularity that could reduce this waste.

Learning the causal structures in high-dimensional datasets allows deriving advanced insights from observational data, thus creating the potential for new applications. One crucial limitation of state-of-the-art methods for learning causal relationships, such as the PC algorithm, is their long execution time. While, in the worst case, the execution time is exponential to the dimension of a given dataset, it is polynomial if the underlying causal structures are sparse. To address the long execution time, parallelized extensions of the algorithm have been developed addressing the Central Processing Unit (CPU) as the primary execution device. While modern multicore CPUs expose a decent level of parallelism, coprocessors, such as Graphics Processing Units (GPUs), are specifically designed to process thousands of data points in parallel, providing superior parallel processing capabilities compared to CPUs.