FPGAs in Big Data: On the transparent and efficient acceleration of big data frameworks

The increasing volume and latency requirements of big data impose challenges on the processing capacity of existing computing systems. FPGA accelerators can be leveraged to overcome these challenges, but questions remain as to how these accelerators are best deployed to accelerate big data frameworks. This work investigates how future big data...

Dataflow Hardware Design for Big Data Acceleration Using Typed Interfaces

Recent trends in large-scale computing demonstrate continuous growth in the need for raw processing performance. At the same time, the slowdown of vertical scaling pushes the industry towards more energy-efficient heterogeneous architectures. With the appearance of FPGAs in the cloud and data centers, a new architecture is offered for offloading...

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...

High-Throughput Big Data Analytics Through Accelerated Parquet to Arrow Conversion

With the advent of high-bandwidth non-volatile storage devices, the classical assumption that database analytics applications are bottlenecked by CPUs having to wait for slow I/O devices is being flipped around. Instead, CPUs are no longer able to decompress and deserialize the data stored in storage-focused file formats fast enough to keep up...