ComBack: A Versatile Dataset for Enhancing Compiler Backend Development Efficiency

ComBack is a large-scale multi-platform compiler backend code dataset. It is sourced from GCC and LLVM backends corresponding to 178 target platforms.

Dataset Information

• Source Data

  • GCC

  Category	Target Platform	Function	KLoC
  CPU	30	35,147	647.2
  MPU	33	6,010	183.9
  GPU	2	457	11.2
  VLIW	5	959	25.4
  DSP	3	399	9.6
  Virtual	4	327	6.5
  SUM	77	43,299	883.7

  • LLVM

  Category	Target Platform	Function	KLoC
  CPU	43	84,914	3,450.4
  MPU	30	11,311	173.0
  GPU	5	22,591	768.3
  VLIW	4	2,048	24.3
  DSP	7	9,646	263.2
  Virtual	12	8,430	168.3
  SUM	101	138,940	4,847.5

• Tasks

  • Statement-Level Completion: complete current statement.

  //Inputs:
  ...
  adjustReg(MBB,LastFrameDestroy, DL, SPReg, FPReg, -StackSize+RVFI->getVarArgsSaveSize() 
  //Ground Truth:
  MachineInstr::FrameDestroy);
  

  • Next-Statement Suggestion: predict the next statement.

  //Inputs:
  ...
  maxCallFrameSize = (maxCallFrameSize + AlignMask) & ~AlignMask;
  //Ground Truth:
  MFI -> setMaxCallFrameSize(maxCallFrameSize);
  

  • Code Generation: generate a function with function description in natrual language.

  //Inputs:
  getPointerRegClass: Returns a TargetRegisterClass used for pointer values.
  Target-Specific Value: Sparc, SP::I64RegsRegClass, SP::IntRegsRegClass.
  //Ground Truth:
  TargetRegisterClass *SparcRegisterInfo::getPointerRegClass(MachineFunction &MF ,unsigned Kind) {
     return Subtarget.is64Bit() ? &SP::I64RegsRegClass : &SP::IntRegsRegClass;
  }
  

Organization

• Code_Generation/* and Code_Completion/*: split data of 178 backends into train/valid/test set in the ratio of 80%:10%:10%

  Task	Train	Valid	Test
  Statement-Level Comp.	128,899(11.36M Token)	16,112(1.43M Token)	16,113(1.43M Token)
  Next-Statement Sugg.	173,052(15.69M Token)	21,631(1.99M Token)	21,632(1.98M Token)
  Code Generation.	36,236(5.10M Token)	4,530(0.64M Token)	4,530(0.64M Token)

• New_Target_Generation/Existing_Types/* and New_Target_Completion/Existing_Types/*: Take data of RISC-V,ARC,NVPTX both in GCC and LLVM as test set, split train/valid set in the ratio of 85%:15% of other CPU, MPU and GPU targets excluding RI5CY(RI5CY is custmoized based on RISCV)

  Task	Train	Valid	Test
  Statement-Level Comp.	114,016(10.20M Token)	20,121(1.81M Token)	6,645(0.58M Token)
  Next-Statement Sugg.	152,114(14.10M Token)	26,844(2.49M Token)	9,313(0.83M Token)
  Code Generation.	30,633(4.44M Token)	5,406(0.79M Token)	2,819(0.37M Token)

• New_Target_Generation/New_Types/* and New_Target_Completion/New_Types/*: Take data of ARC,NVPTX both in GCC and LLVM as test set, split train/valid set in the ratio of 85%:15% of other CPU targets excluding RI5CY(RI5CY is custmoized based on RISCV)

  Task	Train	Valid	Test
  Statement-Level Comp.	87,018(7.78M Token)	15,357(1.37M Token)	2,764(0.26M Token)
  Next-Statement Sugg.	113,684(10.65M Token)	20,063(1.87M Token)	4,029(0.38M Token)
  Code Generation.	21,184(3.14M Token)	3,739(0.55M Token)	1,372(0.18M Token)

• Iterative_Expansion_Generation/* and Iterative_Expansion_Completion/*: Take data of RI5CY in LLVM as test set, split train/valid set in the ratio of 85%:15% of other CPU targets excluding RISC-V(a) and including RISC-V(b)

  (a)

  Task	Train	Valid	Test
  Statement-Level Comp.	87,018(7.78M Token)	15,357(1.37M Token)	721(0.04M Token)
  Next-Statement Sugg.	113,684(10.65M Token)	20,063(1.87M Token)	1,035(0.06M Token)
  Code Generation.	21,184(3.14M Token)	3,739(0.55M Token)	219(0.02M Token)

  (b)

  Task	Train	Valid	Test
  Statement-Level Comp.	90,316(8.06M Token)	15,940(1.42M Token)	721(0.04M Token)
  Next-Statement Sugg.	118,175(11.04M Token)	20,856(1.94M Token)	1,035(0.06M Token)
  Code Generation.	22,413(3.30M Token)	3,957(0.58M Token)	219(0.02M Token)

Citation

@inproceedings{zhong2024comback,
  title={ComBack: A Versatile Dataset for Enhancing Compiler Backend Development Efficiency},
  author={Ming Zhong, Fang Lyu, Lulin Wang, Hongna Geng, Lei Qiu, Huimin Cui, Xiaobing Feng},
  booktitle={Thirty-eighth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2024}
}