SQL2FPGA

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[FCCM'23] SQL2FPGA: Automatic Acceleration of SQL Query Processing on Modern CPU-FPGA Platforms

SQL2FPGA 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# SQL2FPGA: Automatic Acceleration of SQL Query Processing on Modern CPU-FPGA Platforms

This repository includes the code for SQL2FPGA. SQL2FPGA is a hardware-aware SQL query compilation framwork for translating and efficiently mapping SQL queries on the modern heterogeneous CPU-FPGA platforms. SQL2FPGA takes the optimized query execution plans of SQL queries from big data query processing engines (Spark SQL for now); performs hardware-aware optimizations to map query operations to FPGA accelerators (AMD/Xilinx Vitis database overlays for now); and lastly generates the deployable CPU host code and the associated FPGA accelerator configuration code.

If you find this project useful in your research, please consider citing:

@inproceedings{lu2023sql2fpga,
      title={SQL2FPGA: Automatic Acceleration of SQL Query Processing on Modern CPU-FPGA Platforms},
      author={Lu, Alec and Fang, Zhenman},
      booktitle={Proceedings of the 31st IEEE International Symposium On Field-Programmable Custom Computing Machines},
      year={2023}
    }

## Download SQL2FPGA
```
git clone https://github.com/SFU-HiAccel/SQL2FPGA.git
```

## Environmental Setup
1. **Hardware platforms (evaluated):**
    * **Host CPU:**
      * 64-bit Ubuntu 18.04.2 LTS
    * **FPGA Accelerator Design:**
      * [Xilinx Database Library 2020.1](https://github.com/Xilinx/Vitis_Libraries/tree/2020.1/database)
    * **FPGA:**
      * Xilinx Alveo U280

2. **Software tools (evaluated):**
    * **Big data query processing tool:**
      * Spark 3.1.1
    * **HLS tool:**
      * Vitis 2020.1
      * Xilinx Runtime(XRT) 2020.1

## Accelerate SQL Query Processing using SQL2FPGA
1. Import SQL2FPGA Project using IntelliJ
    * Install `Scala` plugin
    * Open project ...
    * Select `pom.xml` project file
    * Select Scala version to match 2.13
    * Build module `SQL2FPGA_Top`

2. Run SQL2FPGA Project on TPC-H Dataset
    * Download TPC-H Benchmark Generator
        ```
        git clone https://github.com/electrum/tpch-dbgen.git
        ```
    * Generate TPC-H Dataset (our evaluation covers SF1 and SF30)
        ```
        cd tpch-dbgen/
        make
        ./dbgen -s <#> 
        ```
    * Specifiy Query Configurations (in `SQL2FPGA_Top.scala`)
        * Specify Dataset File Path
            * Modify `INPUT_DIR_TPCH` and `OUTPUT_DIR_TPCH` with the generated TPC-H dataset
        * Query Specifications
            * Modify `qConfig.tpch_queryNum_start` and `qConfig.tpch_queryNum_end` to specifiy the range of queries to generate code
        * Execution Mode
            * `qConfig.pure_sw_mode = 1` indicates all operators are executed on CPU and `qConfig.pure_sw_mode = 0` indicates a hybrid execution mode where both CPU and FPGA accelerators are used for execution 
            * `qConfig.scale_factor = 1` specifies the scale factor (SF)
    * Build and Run SQL2FPGA Module
       * Build and run module `SQL2FPGA_Top`
    * Output:
       * CPU Host Code: `test_q#.cpp` 
       * FPGA Configuration Code: `cfgFunc_q#.hpp`
       * SW Operator Function Code: `q#.hpp`

3. Build AMD-Xilinx's Database Accelerator Overlay Designs
    * Clone AMD-Xilinx's Vitis Libraries: 
        ```
        git clone https://github.com/Xilinx/Vitis_Libraries.git
        ```
    * Switch to the `2020.1` branch
        ```
        cd Vitis_Libraries
        git checkout 2020.1
        ```
    * Build gqeJoin and gqeAggr accelerator overlay designs (this will take more than 10 hours to finish)
        ```
        cd database/L2/demos
        make -C build_join_partition/ TARGET=hw xclbin DEVICE=xilinx_u280_xdma_201920_3
        make -C build_aggr_partition/ TARGET=hw xclbin DEVICE=xilinx_u280_xdma_201920_3
        ```
        
4. Run SQL2FPGA-generated Designs on Device
    * Replace `makefile` at `/Vitis_Libraries/database/L2/demos` with the  `<$SQL2FPGA_HOME>/makefile`
    * Move SQL2FPGA generated code to `/Vitis_Libraries/database/L2/demos/host/q#/sfsql2fpga_fpga`
    * Compile and execute the design at `/Vitis_Libraries/database/L2/demos`
        ```
        make clean
        make run TARGET=hw MODE=FPGA TB=Q# DEVICE=xilinx_u280_xdma_201920_3 TEST=SQL2FPGA
        ```

Now you have compeletd the entire tool flow of SQL2FPGA. Hack the code and have fun!

## Known Limitations
* Whether to use hash partition logic requires manual specification in SF30.
* Device selection between CPU and FPGA overlay design is currently manually selected through empirical results.
* Output table size required manual input to achieve the best CPU-to-FPGA memory transfer time.

## Authors and Contributors
SQL2FPGA is currently maintained by [Alec Lu](http://www.sfu.ca/~fla30/).

Besides, we thank AMD-Xilinx Vitis DB team, Prof. Jiannan Wang and Dr. Jinglin Peng from Simon Fraser University, for their insightful discussion and technical support.

## Papers
More implementation details of SQL2FPGA are covered in [our paper](http://www.sfu.ca/~fla30/papers/C9_FCCM_2023_SQL2FPGA.pdf).

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