CUDA 编译器产生非最优的汇编器 [英] CUDA compiler produce unoptimal assembler
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问题描述
我已经编译了流动的简单测试内核(CUDA5，sm2.0):
I have compiled flowing simple test kernel (CUDA5, sm2.0):
__device__ void TestKernel(int *pdata)
{
int a0,b0,c0;
a0 = pdata[0];
b0 = pdata[1];
c0 = a0 + b0;
pdata[2] = c0;
}
并期待像流动汇编器这样的东西:
and expect something like flowing assembler:
LD R3,[R0]
LD R4,[R0+4]
IADD R4,R4,R3
ST [R0+8],R4
但是从 cuobjdump --dump-sass 我看到流动的结果要长得多:
but from cuobjdump --dump-sass I see flowing much longer result:
/*0000*/ /*0x10001de428000000*/ MOV R0, R4;
/*0008*/ /*0x00001de428000000*/ MOV R0, R0;
/*0010*/ /*0x00001de428000000*/ MOV R0, R0;
/*0018*/ /*0x00001de428000000*/ MOV R0, R0;
/*0020*/ /*0x0000dc8580000000*/ LD R3, [R0];
/*0028*/ /*0x0c00dde428000000*/ MOV R3, R3;
/*0030*/ /*0x10011c034800c000*/ IADD R4, R0, 0x4;
/*0038*/ /*0x10011de428000000*/ MOV R4, R4;
/*0040*/ /*0x00411c8580000000*/ LD R4, [R4];
/*0048*/ /*0x10011de428000000*/ MOV R4, R4;
/*0050*/ /*0x1030dc0348000000*/ IADD R3, R3, R4;
/*0058*/ /*0x20001c034800c000*/ IADD R0, R0, 0x8;
/*0060*/ /*0x00001de428000000*/ MOV R0, R0;
/*0068*/ /*0x0000dc8590000000*/ ST [R0], R3;
/*0070*/ /*0x00001de790000000*/ RET;
/*0078*/ /*0x00001de780000000*/ EXIT;
/*0080*/ /*0x00001de780000000*/ EXIT;
我很奇怪地址 8、10、18、28、38、60 中的 MOV 指令也没有使用加载/存储指令中的立即偏移量.所以我得到了 15可能的原因是什么?
Very strange to me MOVs instruction in addresses 8,10,18,28,38,60 also the immediate offset in load/store instruction doesn't used. So instead expected 4 (actually 6 including RET,EXIT) instruction I get 15 What is possible reason?
推荐答案
您所看到的几乎可以肯定是因为您在打开调试的情况下进行编译.如果我构建你的内核，我会得到:
What you are seeing is almost certainly because you are compiling with debugging turned on. If I build your kernel I get this:
$ nvcc -arch=sm_30 -c asmprob.cu
$ cuobjdump -sass asmprob.o
Fatbin elf code:
================
arch = sm_30
code version = [1,6]
producer = cuda
host = mac
compile_size = 32bit
identifier = asmprob.cu
code for sm_30
Function : _Z10TestKernelPi
/*0008*/ /*0x10005de428004001*/ MOV R1, c [0x0] [0x44];
/*0010*/ /*0x00009de428004005*/ MOV R2, c [0x0] [0x140];
/*0018*/ /*0x10211c034800c000*/ IADD R4, R2, 0x4;
/*0020*/ /*0x20209c034800c000*/ IADD R2, R2, 0x8;
/*0028*/ /*0x0040dc8580000000*/ LD R3, [R4];
/*0030*/ /*0xf0401c8583ffffff*/ LD R0, [R4+-0x4];
/*0038*/ /*0x00301c0348000000*/ IADD R0, R3, R0;
/*0048*/ /*0x00201c8590000000*/ ST [R2], R0;
/*0050*/ /*0x00001de780000000*/ EXIT;
/*0058*/ /*0xe0001de74003ffff*/ BRA 0x58;
/*0060*/ /*0x00001de440000000*/ NOP CC.T;
/*0068*/ /*0x00001de440000000*/ NOP CC.T;
/*0070*/ /*0x00001de440000000*/ NOP CC.T;
/*0078*/ /*0x00001de440000000*/ NOP CC.T;
.................................
另一方面，如果我使用调试设置构建它，我会得到与您显示的代码一样的代码:
on the other hand, if I build it with debug settings, I get code just like you show:
$ nvcc -arch=sm_30 -G -c asmprob.cu
$ cuobjdump -sass asmprob.o
Fatbin elf code:
================
arch = sm_30
code version = [1,6]
producer = cuda
host = mac
compile_size = 32bit
has debug info
compressed
identifier = asmprob.cu
code for sm_30
Function : _Z10TestKernelPi
/*0000*/ /*0x10005de428004001*/ MOV R1, c [0x0] [0x44];
/*0008*/ /*0x00001de218000005*/ MOV32I R0, 0x140;
/*0010*/ /*0x00001c8614000000*/ LDC R0, c [0x0] [R0];
/*0018*/ /*0x00001de428000000*/ MOV R0, R0;
/*0020*/ /*0x00009c8580000000*/ LD R2, [R0];
/*0028*/ /*0x08009de428000000*/ MOV R2, R2;
/*0030*/ /*0x1000dc034800c000*/ IADD R3, R0, 0x4;
/*0038*/ /*0x0c00dde428000000*/ MOV R3, R3;
/*0040*/ /*0x0030dc8580000000*/ LD R3, [R3];
/*0048*/ /*0x0c00dde428000000*/ MOV R3, R3;
/*0050*/ /*0x0c209c0348000000*/ IADD R2, R2, R3;
/*0058*/ /*0x20001c034800c000*/ IADD R0, R0, 0x8;
/*0060*/ /*0x00001de428000000*/ MOV R0, R0;
/*0068*/ /*0x00009c8590000000*/ ST [R0], R2;
/*0070*/ /*0x40001de740000000*/ BRA 0x88;
/*0078*/ /*0x00001de780000000*/ EXIT;
/*0080*/ /*0x00001de780000000*/ EXIT;
/*0088*/ /*0x00001de780000000*/ EXIT;
/*0090*/ /*0x00001de780000000*/ EXIT;
/*0098*/ /*0xe0001de74003ffff*/ BRA 0x98;
/*00a0*/ /*0x00001de440000000*/ NOP CC.T;
/*00a8*/ /*0x00001de440000000*/ NOP CC.T;
/*00b0*/ /*0x00001de440000000*/ NOP CC.T;
/*00b8*/ /*0x00001de440000000*/ NOP CC.T;
.................................
这让我觉得您的问题是为什么当我禁用优化并为调试器编译时编译器不生成最佳代码?"，我认为这是一个反问......
Which makes me think that your question is "why doesn't the compiler produce optimal code when I disable optimisations and compile for the debugger?", which is something of a rhetorical question, methinks....
为了避免有人怀疑启用 GPU 调试会禁用编译器优化，请考虑以下来自nvcc"的输出:
And lest there be any doubts that enabling GPU debugging disables compiler optimisation, consider the following output from ´nvcc´:
$ nvcc -arch=sm_30 -G -c --dryrun asmprob.cu
#$ _SPACE_=
#$ _CUDART_=cudart
#$ _HERE_=/usr/local/cuda/bin
#$ _THERE_=/usr/local/cuda/bin
#$ _TARGET_SIZE_=
#$ TOP=/usr/local/cuda/bin/..
#$ PATH=/usr/local/cuda/bin/../open64/bin:/usr/local/cuda/bin/../nvvm:/usr/local/cuda/bin:/opt/local/bin:/opt/local/sbin:/Library/Frameworks/Python.framework/Versions/Current/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/usr/local/git/bin:/usr/texbin:/usr/X11/bin:/usr/NX/bin:/usr/local/bin:/Users/talonmies/bin:/usr/local/cuda/bin
#$ INCLUDES="-I/usr/local/cuda/bin/../include"
#$ LIBRARIES= "-L/usr/local/cuda/bin/../lib" -lcudart
#$ CUDAFE_FLAGS=
#$ OPENCC_FLAGS=
#$ PTXAS_FLAGS=
#$ gcc -D__CUDA_ARCH__=300 -E -x c++ -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDACC__ -D__NVCC__ "-I/usr/local/cuda/bin/../include" -include "cuda_runtime.h" -m32 -malign-double -o "/tmp/tmpxft_00005ceb_00000000-6_asmprob.cpp1.ii" "asmprob.cu"
#$ cudafe --m32 --gnu_version=40201 -tused --no_remove_unneeded_entities --debug_mode --gen_c_file_name "/tmp/tmpxft_00005ceb_00000000-3_asmprob.cudafe1.c" --stub_file_name "/tmp/tmpxft_00005ceb_00000000-3_asmprob.cudafe1.stub.c" --gen_device_file_name "/tmp/tmpxft_00005ceb_00000000-3_asmprob.cudafe1.gpu" --nv_arch "compute_30" --gen_module_id_file --module_id_file_name "/tmp/tmpxft_00005ceb_00000000-2_asmprob.module_id" --include_file_name "tmpxft_00005ceb_00000000-1_asmprob.fatbin.c" "/tmp/tmpxft_00005ceb_00000000-6_asmprob.cpp1.ii"
#$ gcc -D__CUDA_ARCH__=300 -E -x c -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDACC__ -D__NVCC__ -D__CUDANVVM__ -D__CUDA_PREC_DIV -D__CUDA_PREC_SQRT "-I/usr/local/cuda/bin/../include" -m32 -malign-double -o "/tmp/tmpxft_00005ceb_00000000-7_asmprob.cpp2.i" "/tmp/tmpxft_00005ceb_00000000-3_asmprob.cudafe1.gpu"
#$ cudafe -w --m32 --gnu_version=40201 --c --debug_mode --gen_c_file_name "/tmp/tmpxft_00005ceb_00000000-8_asmprob.cudafe2.c" --stub_file_name "/tmp/tmpxft_00005ceb_00000000-8_asmprob.cudafe2.stub.c" --gen_device_file_name "/tmp/tmpxft_00005ceb_00000000-8_asmprob.cudafe2.gpu" --nv_arch "compute_30" --module_id_file_name "/tmp/tmpxft_00005ceb_00000000-2_asmprob.module_id" --include_file_name "tmpxft_00005ceb_00000000-1_asmprob.fatbin.c" "/tmp/tmpxft_00005ceb_00000000-7_asmprob.cpp2.i"
#$ gcc -D__CUDA_ARCH__=300 -E -x c -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDABE__ -D__CUDANVVM__ -D__CUDA_PREC_DIV -D__CUDA_PREC_SQRT "-I/usr/local/cuda/bin/../include" -m32 -malign-double -o "/tmp/tmpxft_00005ceb_00000000-9_asmprob.cpp3.i" "/tmp/tmpxft_00005ceb_00000000-8_asmprob.cudafe2.gpu"
#$ filehash -s " -g --dont-merge-basicblocks --return-at-end " "/tmp/tmpxft_00005ceb_00000000-9_asmprob.cpp3.i" > "/tmp/tmpxft_00005ceb_00000000-10_asmprob.hash"
#$ gcc -E -x c++ -D__CUDACC__ -D__NVCC__ "-I/usr/local/cuda/bin/../include" -include "cuda_runtime.h" -m32 -malign-double -o "/tmp/tmpxft_00005ceb_00000000-4_asmprob.cpp4.ii" "asmprob.cu"
#$ cudafe++ --m32 --gnu_version=40201 --parse_templates --debug_mode --gen_c_file_name "/tmp/tmpxft_00005ceb_00000000-3_asmprob.cudafe1.cpp" --stub_file_name "tmpxft_00005ceb_00000000-3_asmprob.cudafe1.stub.c" --module_id_file_name "/tmp/tmpxft_00005ceb_00000000-2_asmprob.module_id" "/tmp/tmpxft_00005ceb_00000000-4_asmprob.cpp4.ii"
#$ cicc -arch compute_30 -m32 -ftz=0 -prec_div=1 -prec_sqrt=1 -fmad=1 -g -O0 "/tmp/tmpxft_00005ceb_00000000-11_asmprob" "/tmp/tmpxft_00005ceb_00000000-9_asmprob.cpp3.i" -o "/tmp/tmpxft_00005ceb_00000000-5_asmprob.ptx"
#$ ptxas -arch=sm_30 -m32 -g --dont-merge-basicblocks --return-at-end "/tmp/tmpxft_00005ceb_00000000-5_asmprob.ptx" -o "/tmp/tmpxft_00005ceb_00000000-12_asmprob.sm_30.cubin"
#$ fatbinary --create="/tmp/tmpxft_00005ceb_00000000-1_asmprob.fatbin" -32 --key="xxxxxxxxxx" --ident="asmprob.cu" --cmdline=" -g --dont-merge-basicblocks --return-at-end " -g "--image=profile=sm_30,file=/tmp/tmpxft_00005ceb_00000000-12_asmprob.sm_30.cubin" "--image=profile=compute_30,file=/tmp/tmpxft_00005ceb_00000000-5_asmprob.ptx" --embedded-fatbin="/tmp/tmpxft_00005ceb_00000000-1_asmprob.fatbin.c" --cuda
#$ rm /tmp/tmpxft_00005ceb_00000000-1_asmprob.fatbin
#$ gcc -D__CUDA_ARCH__=300 -E -x c++ -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDA_PREC_DIV -D__CUDA_PREC_SQRT "-I/usr/local/cuda/bin/../include" -m32 -malign-double -o "/tmp/tmpxft_00005ceb_00000000-13_asmprob.ii" "/tmp/tmpxft_00005ceb_00000000-3_asmprob.cudafe1.cpp"
#$ gcc -c -x c++ "-I/usr/local/cuda/bin/../include" -fpreprocessed -m32 -malign-double -o "asmprob.o" "/tmp/tmpxft_00005ceb_00000000-13_asmprob.ii"
注意设备代码编译阶段命令:
Note the device code compilation phase command:
cicc -arch compute_30 -m32 -ftz=0 -prec_div=1 -prec_sqrt=1 -fmad=1 -g -O0 <----
即.调试构建编译时优化设置为 0.
ie. debugging builds compile with optimisation set to 0.
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