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...

Generating high-performance FPGA accelerator designs for big data analytics with Fletcher and Apache Arrow

As big data analytics systems are squeezing out the last bits of performance of CPUs and GPUs, the next near-term and widely available alternative industry is considering for higher performance in the data center and cloud is the FPGA accelerator. We discuss several challenges a developer has to face when designing and integrating FPGA...

Energy Efficient Multistandard Decompressor ASIP

Many applications make extensive use of various forms of compression techniques for storing and communicating data. As decompression is highly regular and repetitive, it is a suitable candidate for acceleration. Examples are offloading (de)compression to a dedicated circuit on a heterogeneous System-on-Chip, or attaching FPGAs or ASICs...

Methods for Efficient Integration of FPGA Accelerators with Big Data Systems

Because of fundamental limitations of CMOS technology, computing researchers and the computing industry are focusing on using transistors in integrated circuits more efficiently towards obtaining a computational goal. At the architectural level, this has led to an era of heterogeneous computing, where various types of computational components...

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...

Enabling FPGA Memory Management for Big Data Applications Using Fletcher

vailability of FPGAs is increasing due to cloud service offerings. In the wake of a new in-memory storage format specification, Apache Arrow, FPGAs are increasingly interesting for data processing acceleration in the big data domain. The Fletcher framework can be used to easily develop FPGA accelerated applications that access data stored in...

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...

A Hardware/Software Co-designed Partitioning Algorithm of Sparse Matrix Vector Multiplication into Multiple Independent Streams for Parallel Processing

The trend of computing faster and more efficiently has been a driver for the computing industry since its beginning. However, it is increasingly difficult to continue this trend because current CMOS technology cannot be down-scaled anymore due to physical restrictions. Consequently, to obtain the next major performance improvement, the focus is...

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...