Modern big data systems are highly heterogeneous. The components found in their many layers of abstraction are often implemented in a wide variety of programming languages and frameworks. Due to language implementation differences, interfaces between these components, including hardware accelerated components, are often burdened by serialization overhead. Serialization bandwidth of many high-level language frameworks is an order of magnitude lower than contemporary FPGA accelerator interface bandwidth, especially when objects are small but numerous. Therefore, serialization bounds the effective end-to-end performance of FPGA-accelerated solutions integrated with applications written in high-level languages. The Apache Arrow project defines a language agnostic columnar in-memory format optimized for big data applications, preventing the need to serialize or even make copies during communication between components. To enable FPGA accelerators to benefit from the approach of Arrow, we first investigate the properties of its format in relation to hardware interfaces and establish that the format is usable. Second, we present the Fletcher framework, that automatically generates highly efficient hardware interfaces to access data of potentially complex, nested Arrow data types. Our approach allows 11 of the languages supported by Apache Arrow libraries to efficiently communicate large data sets with FPGA accelerators at system bandwidth. Furthermore, on the hardware side, the generated interfaces deliver any data type that Arrow can represent as groups of streams, providing a better starting point for data-flow-oriented kernel development, compared to manually creating custom interfaces to address issues related to pointer arithmetic, bus word misalignment and latency. For example applications, as measured on an AWS EC2 F1 and CAPI2-enabled POWER9 system, accelerated end-to-end application performance improves by 1.3x-49x compared to a hardware accelerated solution that still requires serialization.@en

In the domain of big data analytics, the bottleneck of converting storage-focused file formats to in-memory data structures has shifted from the bandwidth of storage to the performance of decoding and decompression software. Two widely used formats for big data storage and in-memory data are Apache Parquet and Apache Arrow, respectively. In order to improve the speed at which data can be loaded from disk to memory, we propose an FPGA accelerator design that converts Parquet files to Arrow in-memory data structures. We describe an extensible, publicly available, free and open-source implementation of the proposed converter that supports various Parquet file configurations. The performance of the converter is measured on an AWS EC2 F1 system and on a POWER9 system using the recently released OpenCAPI interface. A single instance of the converter can reach between 6 and 12 GB/s of end-to-end throughput, and shows up to a threefold improvement over the fastest single-thread CPU implementation. It has a low resource utilization (less than 5% for all types of FPGA resources). This allows scaling out the design to match the bandwidth of the coming generation of accelerator interfaces. The proposed design and implementation can be extended to support more of the many possible Parquet file configurations.@en