ARM上的OpenBLAS性能优化技术

1、ARM上的OpenBLAS性能优化技术什么是BLAS？lBasic Linear Algebra Subprogramsl基本线性代数子程序BLAS3级：矩阵-矩阵BLAS2级：矩阵-向量BLAS1级：向量-向量BLAS与深度学习lAlexnetOpenBLASl2011年，forked from Gotoblas2l全球最好的开源矩阵计算库l2016 中国计算机学会科技进步二等奖l进入主流Linux发行版l进入OpenHPC套件OpenBLASl支持主流CPU处理器Intel，AMDARM，AArch64MIPS，龙芯IBM Powerl支持常见操作系统LinuxWindowsMac OSX

2、FreeBSDAndroidOpenBLAS用户OpenBLAS性能lIntel Sandy BridgeOpenBLAS性能l龙芯3AGEMM算法GEMM算法GEMM算法GEMM算法GEMM算法GEMM算法GEMM算法GEMM算法GEMM算法l核心汇编代码寄存器分块指令流水线SIMD，循环展开，预取BLAS性能优化流派l自动调优 Auto- tuningATLAS快速开发和移植性能一般l手工核心汇编GotoBLAS/Ope nBLAS性能好新架构？Auto-tuning生存高效代码？AUGEMlAutomatically Generate Efficient Matrix kernell目标

3、：自动生成BLAS中高效汇编l支持x86 ISASSE，AVX，AVX 2.0l支持ARMv7 ISANeonAUGEMl输入类似C代码片段l输出高性能汇编l基于模版Template隐含手工优化知识C级别Kernel优化Input simple C code of gemm kernelOutput Optimized C code of gemm kernelTemplate识别Output Optimized C code of gemm kernelLoad: Load:tmp0 = ptr_A0; tmp1 = ptr_B0;Multiply: tmp2 = tmp0 * tmp1;

4、Add:res0 = res0 + tmp2;Load: tmp0 = Cj*LDC+i; Add:res0 = res0 + tmp0; Store: Cj*LDC+i = res0;Template识别l预定义6种Template2组Template优化lSIMD向量化mmUnrolledCOMP(ptr_A,0,2,ptr_B,0,2,(res0,res1,res2,res3) mmCOMP(ptr_A,0,ptr_B,0,res0)1.tmp0 = ptr_A0; 2.tmp1 = ptr_B0; 3.tmp2 = tmp0 * tmp1; 4.res0 = res0 + tmp2;m

5、mCOMP(ptr_A,1,ptr_B,0,res1) 1.tmp0 = ptr_A1;2.tmp1 = ptr_B0; 3.tmp2 = tmp1 * tmp1; 4.res1 = res1 + tmp2;mmCOMP(ptr_A,0,ptr_B,1,res2) 1.tmp0 = ptr_A0;2.tmp1 = ptr_B1; 3.tmp2 = tmp0 * tmp1; 4.res2 = res2 + tmp2;mmCOMP(ptr_A,1,ptr_B,1,res3) 1.tmp0 = ptr_A1;2.tmp1 = ptr_B1; 3.tmp2 = tmp0 * tmp1; 4.res3

6、= res3 + tmp2;Vld ptr_A, 0, vec0Vdup ptr_B, 0, vec1Vmul vec0, vec1, vec2Vadd vec2, vec3, vec3Vld ptr_A, 0, vec4Vdup ptr_B, 1, vec5Vmul vec4, vec5, vec6Vadd vec6, vec7, vec7Template优化l寄存器分配根据用途分组ABC中间结果不考虑寄存器溢出临时保存到堆栈 XVld ptr_A, 0, vec0Vdup ptr_B, 0, vec1Vmul vec0, vec1, vec2Vadd vec2, vec3, vec3Vld

7、 ptr_A, 0, vec4Vdup ptr_B, 1, vec5Vmul vec4, vec5, vec6Vadd vec6, vec7, vec7Template优化l汇编指令映射Vld ptr_A, 0, reg0Vdup ptr_B, 0, reg1Vmul reg0, reg1, reg2Vadd reg2, reg3, reg3Vld ptr_A, 0, reg4Vdup ptr_B, 1, reg5Vmul reg4, reg5, reg6Vadd reg6, reg7, reg7InstructionsSSEAVXVld array, offset, regVld offse

8、t(array),regVld offset(array),regVst reg, array, offsetVst offset(array),regVst offset(array),regVmul reg0,reg1,reg2Vadd reg2,reg3,reg3Vmov reg1,reg2Vmul reg0,reg1 Vadd reg1,reg3Vmul reg0,reg1,reg2Vadd reg2,reg3,reg3Vld 0(ptr_A), reg0Vdup 0(ptr_B), reg1Vmov reg1,reg2Vmul reg0, reg1Vadd reg1, reg3 6.

9、Vld 0(ptr_A), reg0Vdup 0(ptr_B), reg1Vmul reg0, reg1, reg2Vadd reg2, reg3, reg3 5.汇编生成l将剩余代码生成汇编循环控制l保持寄存器分配的一致性引入reg_table全局记录表Algorithm of Template OptimizerAUGEM性能测试AUGEM性能测试lDGEMM on Intel Sandy BridgeAUGEM性能测试lDGEMM on AMD PiledriverAUGEM性能测试lDGEMV （BLAS2级）AUGEM性能测试lDAXPY (BLAS1级)PerfSGEMM on ARMl单精度SGEMMNeon SIMD指令与IEEE 754标准不一致Round mode (Flush to Zero)不影响深度学习的精度l AUGEM生成汇编代码支持ARM特殊指令vmla.f32c, a, bdPerfSGEMM on