Evaluation of different storage systems for Apache Spark and Apache Hadoop

Big Data systems have been used for multiple years to solve problems that require scale. A framework takes care of scalability and resiliency issues, and allows the user to focus on relevant computation, in the form of map and reduce functions. In these Big Data systems, we currently see a shift from the traditional use of the Hard Disk Drive ...

Exploring the Limits of Query Pushdown for SQL Acceleration on FPGAs

There has been an increasing interest in moving computation closer to storage in recent years due to significant improvements in memory technology. FPGAs were proven to be an exciting candidate for accelerating database workloads since they provide an energy-efficient, reconfigurable and high-performance computation platform. Therefore, FPGAs...

Transparently Accelerating Spark SQL Code on Computing Hardware

Through new digital business models, the importance of big data analytics continuously grows. Initially, data analytics clusters were mainly bounded by the throughput of network links and the performance of I/O operations. With current hardware development, this has changed, and often the performance of CPUs and memory access became the new...

SparkJNI: A Reference Design for a Heterogeneous Apache Spark Framework

The digital era's requirements pose many challenges related to deployment, implementation and efficient resource utilization in modern hybrid computing infrastructures. In light of the recent improvements in computing units, the defacto structure of a high-performance computing cluster, ordinarily consisted of CPUs only, is superseeded by...

Scalable machine learning algorithms on a big data infrastructure

Two currently popular topics in computer science are machine learning and big data. Often the two are combined to obtain powerful machines with learning capabilities or high throughput data analysis programs among others. This research analyses which machine learning techniques qualify to be efficiently implemented on a scalable big data...