Apache Spark is a popular open-source distributed processing framework that enables
efficient processing of massive amounts of data. It has a large number of configuration …

Data analytic applications built upon big data processing frameworks such as Apache Spark
are an important class of applications. Many of these applications are not latency-sensitive …

Big data applications and analytics are employed in many sectors for a variety of goals:
improving customers satisfaction, predicting market behavior or improving processes in …

To process massive quantities of data, developers leverage Data-Intensive Scalable
Computing (DISC) systems such as Apache Spark. In terms of debugging, DISC systems …

Tuning complex machine learning systems is challenging. Machine learning typically
requires to set hyperparameters, be it regularization, architecture, or optimization …

Metaheuristics are search procedures used to solve complex, often intractable problems for
which other approaches are unsuitable or unable to provide solutions in reasonable times …

Summary The Apache Spark framework for distributed computation is popular in the data
analytics community due to its ease of use, but its MapReduce‐style programming model …

Offline design optimization problem arises in numerous science and engineering
applications including material and chemical design, where expensive online …

Training deep networks is a time-consuming process, with networks for object recognition
often requiring multiple days to train. For this reason, leveraging the resources of a cluster to …

With the recent success of deep learning, the amount of data and computation continues to
grow daily. Hence a distributed deep learning system that shares the training workload has …