Taking advantage of recently released hybrid multicore architectures, such as the Intel's Xeon+FPGA machine, where the FPGA has coherent access to the main memory through the QPI bus, we explore the benefits of specializing operators to hardware. We focus on two commonly used SQL operators for strings: LIKE, and REGEXP_LIKE, and provide a novel and efficient implementation of these operators in reconfigurable hardware. We integrate the hardware accelerator into MonetDB, a main-memory column store, and demonstrate a significant improvement in response time and throughput. Our Hardware User Defined Function (HUDF) can speed up complex pattern matching by an order of magnitude in comparison to the database running on a 10-core CPU. The insights gained from integrating hardware based string operators into MonetDB should also be useful for future designs combining hardware specialization and databases.

Accelerating Pattern Matching Queries in Hybrid CPU-FPGA Architectures

While FPGAs have seen prior use in database systems, in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons. First, specifically for in-memory databases, FPGAs integrated with conventional I/O provide insufficient bandwidth, limiting performance. Second, GPUs, which can also provide high throughput, and are easier to program, have emerged as a strong accelerator alternative. Third, programming FPGAs required developers to have full-stack skills, from high-level algorithm design to low-level circuit implementations. The good news is that these challenges are being addressed. New interface technologies connect FPGAs into the system at main-memory bandwidth and the latest FPGAs provide local memory competitive in capacity and bandwidth with GPUs. Ease of programming is improving through support of shared coherent virtual memory between the host and the accelerator, support for higher-level languages, and domain-specific tools to generate FPGA designs automatically. Therefore, this paper surveys using FPGAs to accelerate in-memory database systems targeting designs that can operate at the speed of main memory.

/pdf/in-memory-database-acceleration-on-fpgas-a-survey-4vep74bwu1.pdf

In-memory database acceleration on FPGAs: a survey

In this paper, we investigate the use of field programmable gate arrays (FPGAs) to accelerate relational joins. Relational join is one of the most CPU-intensive, yet commonly used, database operations. Hashing can be used to reduce the time complexity from quadratic (naive) to linear time. However, doing so can introduce false positives to the results which must be resolved. We present a hash-join engine on FPGA that performs hashing, conflict resolution, and joining on a PCIe-attached system, achieving greater than 11x speedup over software.

Accelerating Join Operation for Relational Databases with FPGAs

Large relational databases often rely on fast join implementations for good performance. Recent paradigm shifts in processor architectures has reinvigorated research into how the join operation can be implemented. The FPGA community has also been developing new architectures with the potential to push performance even further. Hashing is a common method used to implement joins, but its poor spatial locality can hinder performance on processor architectures. Multithreaded architectures can better cope with poor spatial locality by masking memory/cache latencies with many outstanding requests. In this paper we present the first end-to-end in-memory FPGA hash join implementation. The FGPA uses massive multithreading during the build and probe phases to mask long memory delays, while it concurrently manages hundreds of thread states locally. Throughput results show a speedup between 2x and 3.4x over the best multi-core approaches with comparable memory bandwidths on uniform and skewed datasets; however, this advantage diminishes for extremely skewed datasets. Results using the FPGA’s full 76.8 GB/s of bandwidth show throughput up to 1.6 billion tuples per second for uniform and skewed datasets.

/pdf/fpga-based-multithreading-for-in-memory-hash-joins-2n8e3s07b8.pdf

FPGA-based Multithreading for In-Memory Hash Joins

Stream processing is an emerging paradigm to handle data streams upon arrival, powering latency-critical application such as fraud detection, algorithmic trading, and health surveillance. Though there are a variety of Distributed Stream Processing Systems (DSPSs) that facilitate the development of streaming applications, resource management and task scheduling is not automatically handled by the DSPS middleware and requires a laborious process to tune toward specific deployment targets. As the advent of cloud computing has supported renting resources on-demand, it is of great interest to review the research progress of hosting streaming systems in clouds under certain Service Level Agreements (SLA) and cost constraints. In this article, we introduce the hierarchical structure of streaming systems, define the scope of the resource management problem, and present a comprehensive taxonomy in this context covering critical research topics such as resource provisioning, operator parallelisation, and task scheduling. The literature is then reviewed following the taxonomy structure, facilitating a deeper understanding of the research landscape through classification and comparison of existing works. Finally, we discuss the open issues and future research directions toward realising an automatic, SLA-aware resource management framework.

Resource Management and Scheduling in Distributed Stream Processing Systems: A Taxonomy, Review, and Future Directions

We present an efficient multi-query event stream platform to support query processing over high-frequency event streams. Our platform is built over reconfigurable hardware -- FPGAs -- to achieve line-rate multi-query processing by exploiting unprecedented degrees of parallelism and potential for pipelining, only available through custom-built, application-specific and low-level logic design. Moreover, a multi-query event stream processing engine is at the core of a wide range of applications including real-time data analytics, algorithmic trading, targeted advertisement, and (complex) event processing.

/pdf/multi-query-stream-processing-on-fpgas-57v3a7ww6c.pdf

Multi-query Stream Processing on FPGAs

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Multi-query Stream Processing on FPGAs