1、ARM上的OpenBLAS性能優化技術什么是BLAS？lBasic Linear Algebra Subprogramsl基本線性代數子程序BLAS3級：矩陣-矩陣BLAS2級：矩陣-向量BLAS1級：向量-向量BLAS與深度學習lAlexnetOpenBLASl2011年，forked from Gotoblas2l全球最好的開源矩陣計算庫l2016 中國計算機學會科技進步二等獎l進入主流Linux發行版l進入OpenHPC套件OpenBLASl支持主流CPU處理器Intel，AMDARM，AArch64MIPS，龍芯IBM Powerl支持常見操作系統LinuxWindowsMac OSX

2、FreeBSDAndroidOpenBLAS用戶OpenBLAS性能lIntel Sandy BridgeOpenBLAS性能l龍芯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核心匯編代碼寄存器分塊指令流水線SIMD，循環展開，預取BLAS性能優化流派l自動調優 Auto- tuningATLAS快速開發和移植性能一般l手工核心匯編GotoBLAS/Ope nBLAS性能好新架構？Auto-tuning生存高效代碼？AUGEMlAutomatically Generate Efficient Matrix kernell目標

3、：自動生成BLAS中高效匯編l支持x86 ISASSE，AVX，AVX 2.0l支持ARMv7 ISANeonAUGEMl輸入類似C代碼片段l輸出高性能匯編l基于模版Template隱含手工優化知識C級別Kernel優化Input simple C code of gemm kernelOutput Optimized C code of gemm kernelTemplate識別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識別l預定義6種Template2組Template優化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優化l寄存器分配根據用途分組ABC中間結果不考慮寄存器溢出臨時保存到堆棧 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優化l匯編指令映射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.匯編生成l將剩余代碼生成匯編循環控制l保持寄存器分配的一致性引入reg_table全局記錄表Algorithm of Template OptimizerAUGEM性能測試AUGEM性能測試lDGEMM on Intel Sandy BridgeAUGEM性能測試lDGEMM on AMD PiledriverAUGEM性能測試lDGEMV （BLAS2級）AUGEM性能測試lDAXPY (BLAS1級)PerfSGEMM on ARMl單精度SGEMMNeon SIMD指令與IEEE 754標準不一致Round mode (Flush to Zero)不影響深度學習的精度l AUGEM生成匯編代碼支持ARM特殊指令vmla.f32c, a, bdPerfSGEMM on