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This repo is archived. For latest version, please see https://github.com/UCLA-VAST/soda.

SODA Compiler

Stencil with Optimized Dataflow Architecture Compiler

Publication

SODA DSL Example

# comments start with hashtag(#)

kernel: blur      # the kernel name, will be used as the kernel name in HLS
burst width: 512  # DRAM burst I/O width in bits, for Xilinx platform by default it's 512
unroll factor: 16 # how many pixels are generated per cycle

# specify the dram bank, type, name, and dimension of the input tile
# the last dimension is not needed and a placeholder '*' must be given
# dram bank is optional
# multiple inputs can be specified but 1 and only 1 must specify the dimensions
input dram 1 uint16: input(2000, *)

# specify an intermediate stage of computation, may appear 0 or more times
local uint16: blur_x(0, 0) = (input(0, 0) + input(0, 1) + input(0, 2)) / 3

# specify the output
# dram bank is optional
output dram 1 uint16: blur_y(0, 0) = (blur_x(0, 0) + blur_x(1, 0) + blur_x(2, 0)) / 3

# how many times the whole computation is repeated (only works if input matches output)
iterate: 1

Getting Started

Prerequisites

  • Python 3.5+ and corresponding pip
  • SDAccel 2018.3 (earlier versions might work but won't be supported)
How to install Python 3.5+ on Ubuntu 16.04+ and CentOS 7?

Ubuntu 16.04+

sudo apt install python3 python3-pip

CentOS 7

sudo yum install python36 python36-pip
sudo alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 100

Clone the Repo

git clone https://github.com/UCLA-VAST/soda-compiler.git
cd soda-compiler
python3 -m pip install --user -r requirements.txt

Parameter Setup

app=blur
platform=xilinx_u200_xdma_201830_1
# The following can be set via sourcing /path/to/xilinx/sdx/settings64.sh
XILINX_SDX=/path/to/xilinx/sdx
XILINX_VIVADO=/path/to/xilinx/vivado

Generate HLS Kernel Code

src/sodac tests/src/${app}.soda --xocl-kernel ${app}_kernel.cpp

Generate OpenCL Host Code

src/sodac tests/src/${app}.soda --xocl-header ${app}.h
src/sodac tests/src/${app}.soda --xocl-host ${app}.cpp

Create Testbench

cat >${app}_run.cpp <<EOF
#include <cstdio>
#include <cstdlib>

#include "${app}.h"                                                                            

int ${app}_test(const char* xclbin, const int dims[4]);
int main(int argc, char **argv) {
  if (argc != 4) {
    fprintf(stderr, "Usage: \n    %s <xclbin> <input width> <input height>\n", argv[0]);
    return 1;
  }
  int dims[4] = {atoi(argv[2]), atoi(argv[3]), 0, 0};
  return ${app}_test(argv[1], dims);
}
EOF

Compile OpenCL Host Executable

# Please set TILE_SIZE_DIM_0 and UNROLL_FACTOR macros to match the kernel.
g++ -std=c++11 -I${XILINX_SDX}/runtime/include -I${XILINX_VIVADO}/include ${app}.cpp ${app}_run.cpp -o ${app} \
    -lxilinxopencl -DTILE_SIZE_DIM_0=2000 -DUNROLL_FACTOR=2 -fopenmp -Wno-deprecated-declarations -Wall

Create Emulation Config

emconfigutil -f ${platform}

Software Emulation

Compile for Software Emulation

xocc -t sw_emu -f ${platform} --kernel ${app}_kernel --xp prop:kernel.${app}_kernel.kernel_flags="-std=c++0x" \
    -c ${app}_kernel.cpp -o ${app}.sw_emu.xo

Link for Software Emulation

xocc -t sw_emu -f ${platform} -l ${app}.sw_emu.xo -o ${app}.sw_emu.xclbin

Run Software Emulation

XCL_EMULATION_MODE=sw_emu ./${app} ${app}.sw_emu.xclbin 2000 100

High-Level Synthesis

xocc -t hw -f ${platform} --kernel ${app}_kernel --xp prop:kernel.${app}_kernel.kernel_flags="-std=c++0x" \
    -c ${app}_kernel.cpp -o ${app}.hw.xo

Hardware Emulation

Link for Hardware Emulation

xocc -t hw_emu -f ${platform} -l ${app}.hw.xo -o ${app}.hw_emu.xclbin

Run Hardware Emulation

# By default, kernel ports are connected via DRAM bank 1 on the xilinx_u200_xdma_201830_1 platform.
DRAM_IN=1 DRAM_OUT=1 XCL_EMULATION_MODE=hw_emu ./${app} ${app}.hw_emu.xclbin 2000 10

Hardware Deployment

Logic Synthesis, Place, and Route

xocc -t hw -f ${platform} -l ${app}.hw.xo -o ${app}.hw.xclbin

Run Bitstream on FPGA

# By default, kernel ports are connected via DRAM bank 1 on the xilinx_u200_xdma_201830_1 platform.
DRAM_IN=1 DRAM_OUT=1 ./${app} ${app}.hw.xclbin 2000 1000

Code Snippet Example

Source Code

kernel: jacobi2d
burst width: 512
unroll factor: 2
input float: t1(2000, *)
output float: t0(0, 0) = (t1(0, 1) + t1(1, 0) + t1(0, 0) + t1(0, -1) + t1(-1, 0)) * 0.2f
iterate: 1

HLS Kernel Code

Each function in the below code snippets is synthesized into an RTL module. Their arguments are all hls::stream FIFOs; Without unrolling, a simple line-buffer pipeline is generated, producing 1 pixel per cycle. With unrolling, a SODA microarchitecture pipeline is generated, procuding 2 pixeles per cycle.

Without Unrolling (--unroll-factor=1)

#pragma HLS dataflow
Module1Func(
  /*output*/ &from_t1_offset_0_to_t1_offset_1999,
  /*output*/ &from_t1_offset_0_to_t0_pe_0,
  /* input*/ &from_super_source_to_t1_offset_0);
Module2Func(
  /*output*/ &from_t1_offset_1999_to_t1_offset_2000,
  /*output*/ &from_t1_offset_1999_to_t0_pe_0,
  /* input*/ &from_t1_offset_0_to_t1_offset_1999);
Module3Func(
  /*output*/ &from_t1_offset_2000_to_t1_offset_2001,
  /*output*/ &from_t1_offset_2000_to_t0_pe_0,
  /* input*/ &from_t1_offset_1999_to_t1_offset_2000);
Module3Func(
  /*output*/ &from_t1_offset_2001_to_t1_offset_4000,
  /*output*/ &from_t1_offset_2001_to_t0_pe_0,
  /* input*/ &from_t1_offset_2000_to_t1_offset_2001);
Module4Func(
  /*output*/ &from_t1_offset_4000_to_t0_pe_0,
  /* input*/ &from_t1_offset_2001_to_t1_offset_4000);
Module5Func(
  /*output*/ &from_t0_pe_0_to_super_sink,
  /* input*/ &from_t1_offset_0_to_t0_pe_0,
  /* input*/ &from_t1_offset_1999_to_t0_pe_0,
  /* input*/ &from_t1_offset_2000_to_t0_pe_0,
  /* input*/ &from_t1_offset_4000_to_t0_pe_0,
  /* input*/ &from_t1_offset_2001_to_t0_pe_0);

In the above code snippet, Module1Func to Module4Func are forwarding modules; they constitute the data-reuse line buffer. The line buffer size is approximately two lines of pixels, i.e. 4000 pixels. Module5Func is a computing module; it implements the computation kernel. The whole design is fully pipelined; however, with only 1 computing module, it can only produce 1 pixel per cycle.

Unroll 2 Times (--unroll-factor=2)

#pragma HLS dataflow
Module1Func(
  /*output*/ &from_t1_offset_1_to_t1_offset_1999,
  /*output*/ &from_t1_offset_1_to_t0_pe_0,
  /* input*/ &from_super_source_to_t1_offset_1);
Module1Func(
  /*output*/ &from_t1_offset_0_to_t1_offset_2000,
  /*output*/ &from_t1_offset_0_to_t0_pe_1,
  /* input*/ &from_super_source_to_t1_offset_0);
Module2Func(
  /*output*/ &from_t1_offset_1999_to_t1_offset_2001,
  /*output*/ &from_t1_offset_1999_to_t0_pe_1,
  /* input*/ &from_t1_offset_1_to_t1_offset_1999);
Module3Func(
  /*output*/ &from_t1_offset_2000_to_t1_offset_2002,
  /*output*/ &from_t1_offset_2000_to_t0_pe_1,
  /*output*/ &from_t1_offset_2000_to_t0_pe_0,
  /* input*/ &from_t1_offset_0_to_t1_offset_2000);
Module4Func(
  /*output*/ &from_t1_offset_2001_to_t1_offset_4001,
  /*output*/ &from_t1_offset_2001_to_t0_pe_1,
  /*output*/ &from_t1_offset_2001_to_t0_pe_0,
  /* input*/ &from_t1_offset_1999_to_t1_offset_2001);
Module5Func(
  /*output*/ &from_t1_offset_2002_to_t1_offset_4000,
  /*output*/ &from_t1_offset_2002_to_t0_pe_0,
  /* input*/ &from_t1_offset_2000_to_t1_offset_2002);
Module6Func(
  /*output*/ &from_t1_offset_4001_to_t0_pe_0,
  /* input*/ &from_t1_offset_2001_to_t1_offset_4001);
Module7Func(
  /*output*/ &from_t0_pe_0_to_super_sink,
  /* input*/ &from_t1_offset_1_to_t0_pe_0,
  /* input*/ &from_t1_offset_2000_to_t0_pe_0,
  /* input*/ &from_t1_offset_2001_to_t0_pe_0,
  /* input*/ &from_t1_offset_4001_to_t0_pe_0,
  /* input*/ &from_t1_offset_2002_to_t0_pe_0);
Module8Func(
  /*output*/ &from_t1_offset_4000_to_t0_pe_1,
  /* input*/ &from_t1_offset_2002_to_t1_offset_4000);
Module7Func(
  /*output*/ &from_t0_pe_1_to_super_sink,
  /* input*/ &from_t1_offset_0_to_t0_pe_1,
  /* input*/ &from_t1_offset_1999_to_t0_pe_1,
  /* input*/ &from_t1_offset_2000_to_t0_pe_1,
  /* input*/ &from_t1_offset_4000_to_t0_pe_1,
  /* input*/ &from_t1_offset_2001_to_t0_pe_1);

In the above code snippet, Module1Func to Module6Func and Module8Func are forwarding modules; they constitute the reuse buffers of the SODA microarchitecture. Although unrolled, the reuse buffer size is still approximately two lines of pixels, i.e. 4000 pixels. Module7Func is a computing module; it is instanciated twice. The whole design is fully pipelined and can produce 2 pixel per cycle. In general, the unroll factor can be set to any number that satisfies the throughput requirement.

Design Considerations

  • kernel, burst width, unroll factor, input, output, and iterate keywords are mandatory
  • For non-iterative stencil, unroll factor shall be determined by the DRAM bandwidth, i.e. saturate the external bandwidth, since the resource is usually not the bottleneck
  • For iterative stencil, prefer to use more PEs in a single iteration rather than implement more iterations
  • Note that 2.0 will be a double number. To generate float, use 2.0f. This may help reduce DSP usage
  • SODA is tiling-based and the size of the tile is specified in the input keyword. The last dimension is a placeholder because it is not needed in the reuse buffer generation

Projects Using SODA