Yasin Oge

An Implementation of Handshake Join on FPGA

This paper shows an implementation of handshake join on field-programmable gate array (FPGA). Handshake join is one of stream join algorithms, proposed by Teubner and Mueller. It can support very high degrees of parallelism and attain unprecedented success in throughput speed in order to achieve efficient support for window-based join in streaming databases. In handshake join, it is necessary to take into account the problems with regard to the capacity of the output channel and the limitation of the internal buffer sizes, in order to apply join operation to input tuples efficiently in a correct manner. However, the implementation has not necessarily clarified in detail yet in their paper. In this paper, to solve the issues, we propose the merging network and the admission controller. Then we evaluate the architecture in terms of the hardware resource usage, the maximum clock frequency, and the operation performance.

A fast handshake join implementation on FPGA with adaptive merging network

One of a critical design issues for implementing handshake-join hardware is result collection performed by a merging network. To address the issue, we introduce an adaptive merging network. Our implementation achieves over 3 million tuples per second when the selectivity is 0.1. The proposed implementation attains up to 5.2x higher throughput than original handshake-join hardware. In this demonstration, we apply the proposed technique to filter out malicious packets from packet streams. To the best of our knowledge, our system is the fastest handshake join implementation on FPGA.

An FPGA-Based Tightly Coupled Accelerator for Data-Intensive Applications

Computation beside a data source plays an important role in achieving a high performance with low energy consumption in Big Data processing. In contrast to that of a conventional workload, the processing of Big Data frequently requires that a massive amount of data in distributed storage be scanned. A key technique for reducing energy-consuming processor loads is to install a reconfigurable accelerator that is tightly coupled to a computational resource with interfaces. The accelerator is capable of configuring application-specific hardware modules to allow some logical and arithmetic operations for data stream transmission between interfaces, as well as the offloading of control protocols for communication with other computing nodes or storage. In this paper, an FPGA-based accelerator, which is directly attached to DRAM, the network, and storage, is proposed in order to realize an energy efficient computing system. A simple application that counts the words appearing in the data is implemented to evaluate a prototype system. As the accelerator outperforms by 80.66 to 429 times similar applications executed on an SSD-based Hadoop framework, we confirm that the accelerators utilization for Big Data processing is beneficial.

An Efficient and Scalable Implementation of Sliding-Window Aggregate Operator on FPGA

This paper presents an efficient and scalable implementation of an FPGA-based accelerator for sliding-window aggregates over disordered data streams. With an increasing number of overlapping sliding-windows, the window aggregates have a serious scalability issue, especially when it comes to implementing them in parallel processing hardware (e.g., FPGAs). To address the issue, we propose a resource-efficient, scalable, and order-agnostic hardware design and its implementation by examining and integrating two key concepts, called Window-ID and Pane, which are originally proposed for software implementation, respectively. Evaluation results show that the proposed implementation scales well compared to the previous FPGA implementation in terms of both resource consumption and performance. The proposed design is fully pipelined and our implementation can process out-of-order data items, or tuples, at wire speed up to 200 million tuples per second.

Accelerating OLAP Workload on Interconnected FPGAs with Flash Storage

The data volume used in online analytical processing (OLAP) applications is rapidly increasing because of the increasing popularity of various Web services and emerging sensor technologies. Since the amount of accumulated data is frequently too large to store in an in-memory database, it is necessary to have a secondary storage to store such big data. On the basis of this premise, the most important factor to determine the performance of data-intensive applications is to reduce the number and the size of the data transfers between the secondary storage and the main memory. To achieve an energy-efficient computing environment, offloading a user-defined function (UDF) onto interconnected FPGA-boards that equip high-speed storage is effective due to FPGAs performance ratio of operations per I/O. In this paper, we focus on the aggregate operations that are popularly used UDF in OLAP, and propose an acceleration scheme utilizing interconnected FPGAs with flash storage. The scheme is by introducing an accelerator modules which apply operations to data-stream passing through the FPGA, in addition to appropriate data distribution and partitioning. We implemented an accelerator module that aggregates the data transferred from the flash storage to the DRAM in order to show availability. Through preliminary evaluations of the accelerator, we confirmed that aggregate operations supported by the active-disk mechanism outperforms a software-based database management system by more than 30 times.

Design and Implementation of a Merging Network Architecture for Handshake Join Operator on FPGA

A novel merging network architecture is proposed for a handshake join operator in order to achieve much higher data throughput than ever before. Handshake join is a highly parallelized algorithm for window-based stream joins. Result collection performed by a merging network is a significant design issue for the handshake join operator because the merging network becomes an overwhelming bottleneck for scalable performance. To address the issue, an adaptive merging network is proposed for hardware implementation of the algorithm. The proposed architecture is implemented on an FPGA and it is evaluated in terms of the hardware resource usage, the maximum clock frequency, and the performance. Experimental results demonstrate up to 16.3 times higher throughput than nested loops-style join implementation without dropping any tuples. To the best of our knowledge, this is the best performance for handshake join operator implemented on an FPGA.

Pipelined Parallel Join and Its FPGA-Based Acceleration

A huge amount of data is being generated and accumulated in data centers, which leads to an important increase in the required energy consumption to analyze these data. Thus, we must consider the redesign of current computer systems architectures to be more friendly to applications based on distributed algorithms that require a high data transfer rate. Novel computer architectures that introduce dedicated accelerators to enable near-data processing have been discussed and developed for high-speed big-data analysis. In this work, we propose a computer system with an FPGA-based accelerator, namely, interconnected-FPGAs, which offers two advantages: (1) direct data transmission and (2) offloading computation into data-flow in the FPGA. In this article, we demonstrate the capability of the proposed interconnected-FPGAs system to accelerate join operations in a relational database. We developed a new parallel join algorithm, PPJoin, targeted to big-data analysis in a shared-nothing architecture. PPJoin is an extended version of the NUMA-based parallel join algorithm, created by overlapping computation by multicore processors and data communication. The data communication between computational nodes can be accelerated by direct data transmission without passing through the main memory of the hosts. To confirm the performance of the PPJoin algorithm and its acceleration process using an interconnected-FPGA platform, we evaluated a simple query for large tables. Additionally, to support availability, we also evaluated the actual benchmark query. Our evaluation results confirm that the PPJoin algorithm is faster than a software-based query engine by 1.5--5 times. Moreover, we experimentally confirmed that the direct data transmission by interconnected FPGAs reduces computational time around 20% for PPJoin.