NV GPU FMA指令测试

NV GPU FMA指令测试

本文测试了NV GPU FMA指令的行为

一.小结

  • 哪怕一个空的Kernel,也有ULDC指令,从Constant Memory加载Context(>700cycle)和等待指令加载的stall(>100cycle)

    根据fma的峰值性能,smsp的一个active cycle跟fma pipe cycle的比为1:2

    如果一个smsp的fma pipe要达到峰值性能的95%,根据以下不等式:

    (2*fma_inst) / ((fma_inst[eligible]+fma_inst[issued]) + 上面的开销[>800cycle]) > 0.95

    得fma_inst>7600条指令

  • 相同的指令条数,拆到4个warp里执行比放在同一个warp里执行,fma pipe利用率高2倍(本次实验的规模)

    怀疑每一个warp slot里可以提前准备指令

    如果只有一个warp slot在工作,指令准备与执行是串行的,导致 fma pipe工作不饱和

    因此,一个warp里哪怕持续发射7600条fma指令,也打不满fma pipe

  • 测试以下二个规模(1, 1, 1)x(512, 1, 1) 和 (112, 1, 1)x(128, 1, 1),smsp.max的metrics一样

    512=32(warpsize)4(smsp) (4个warp slot都放上warp)

    128=32(warpsize)*4(smsp)

    112=28(sm个数)*2(每个sm放2个block)

    也就说,对某一个smsp而言,二种方案都分到了4个warp,warp slot是不区分warp来自哪一个block

    只要能放在warp slot中,性能都一样

  • 对算子开发的启示:

    启动开销远大于执行一条fma指令需要的cycle数,使得执行一个小kernel无法充分发挥SM的性能

    每个SM最放置(>smsp(个数)4warpsize)的线程,才能充分隐藏smsp指令调度的latency

二.复现步骤

1.获取FMA指令的峰值性能、启动开销

bash 复制代码
tee ncu_get_gpu_peak_sustained.cu<<-'EOF'
#include <iostream>
#include <cuda_runtime.h>

__global__ void fma_kernel_v0(float *input, float *d_out) {
    float a=clock();
    float b=clock();
    float c=clock();
    float d0;
    int tid  = threadIdx.x + blockIdx.x * blockDim.x;
    __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d0) : "f"(a),"f"(b),"f"(d0));
    input[tid]=d0;
}

__global__ void fma_kernel_v1(float *input, float *d_out) {
    float d0;
    float d1;
    float d2;
    float d3;
    float a=clock();
    float b=clock();
    float c=clock();
    #pragma unroll
    for(int i=0;i<1;i++)
    {
      __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d0) : "f"(a),"f"(b),"f"(d0));
    }
    __asm__  __volatile__("st.global.v4.f32 [%0],{%1,%2,%3,%4};" :: "l"(input),"f"(d0),"f"(d1),"f"(d2),"f"(d3): "memory");
}

int main() {
    float *d_in;
    float *d_out;
    int sm_count=1;int smsp_count=1;int warpsize=32;
    int total_count=sm_count*smsp_count*warpsize;    
    cudaMalloc((void**)&d_in, total_count * sizeof(float));
    cudaMalloc((void**)&d_out, total_count * sizeof(float));
    fma_kernel_v0<<<sm_count, warpsize*smsp_count>>>(d_in, d_out);cudaDeviceSynchronize();
    fma_kernel_v1<<<sm_count, warpsize*smsp_count>>>(d_in, d_out);cudaDeviceSynchronize();
    cudaFree(d_in);cudaFree(d_out);
    return 0;
}
EOF
/usr/local/cuda/bin/nvcc -std=c++17 -lineinfo ncu_get_gpu_peak_sustained.cu -o ncu_get_gpu_peak_sustained
/usr/local/cuda/bin/nvcc -std=c++17 -dc -lineinfo -arch=sm_86 -ptx ncu_get_gpu_peak_sustained.cu -o ncu_get_gpu_peak_sustained.ptx
# 生成cubin
/usr/local/cuda/bin/nvcc -arch=sm_86 ncu_get_gpu_peak_sustained.ptx -cubin -o ncu_get_gpu_peak_sustained.cubin
# 生成fatbin
/usr/local/cuda/bin/nvcc -arch=sm_86 ncu_get_gpu_peak_sustained.cubin -fatbin -o ncu_get_gpu_peak_sustained.fatbin
/usr/local/cuda/bin/cuobjdump --dump-sass ncu_get_gpu_peak_sustained.fatbin

/usr/local/NVIDIA-Nsight-Compute/ncu  --clock-control=none --metrics \
smsp__inst_issued.max,\
smsp__inst_executed.max,\
smsp__warps_eligible.max,\
smsp__cycles_elapsed.avg.per_second,\
smsp__cycles_elapsed.max,\
smsp__warps_active.max,\
smsp__issue_active.max,\
smsp__cycles_active.max,\
sm__cycles_active.max,\
sm__inst_executed_pipe_fma.max,\
smsp__inst_executed_pipe_fma.max,\
sm__sass_thread_inst_executed_op_ffma_pred_on.max,\
sm__pipe_fma_cycles_active.max,\
smsp__pipe_fma_cycles_active.max,\
sm__thread_inst_executed_pipe_fma_pred_on.max,\
smsp__pipe_fma_cycles_active.sum.peak_sustained,\
smsp__pipe_fma_cycles_active.avg.peak_sustained,\
smsp__pipe_fma_cycles_active.max.peak_sustained,\
sm__sass_thread_inst_executed_op_ffma_pred_on.sum.peak_sustained,\
sm__sass_thread_inst_executed_op_ffma_pred_on.avg.peak_sustained,\
smsp__inst_executed_pipe_fma.sum.peak_sustained,\
smsp__warps_issue_stalled_barrier.max,\
smsp__warps_issue_stalled_branch_resolving.max,\
smsp__warps_issue_stalled_dispatch_stall.max,\
smsp__warps_issue_stalled_drain.max,\
smsp__warps_issue_stalled_imc_miss.max,\
smsp__warps_issue_stalled_lg_throttle.max,\
smsp__warps_issue_stalled_long_scoreboard.max,\
smsp__warps_issue_stalled_long_scoreboard_pipe_l1tex.max,\
smsp__warps_issue_stalled_math_pipe_throttle.max,\
smsp__warps_issue_stalled_membar.max,\
smsp__warps_issue_stalled_mio_throttle.max,\
smsp__warps_issue_stalled_mio_throttle_pipe_mio.max,\
smsp__warps_issue_stalled_misc.max,\
smsp__warps_issue_stalled_no_instruction.max,\
smsp__warps_issue_stalled_not_selected.max,\
smsp__warps_issue_stalled_short_scoreboard.max,\
smsp__warps_issue_stalled_sleeping.max,\
smsp__warps_issue_stalled_tex_throttle.max,\
smsp__warps_issue_stalled_wait.max,\
smsp__warps_issue_stalled_selected.max,\
smsp__inst_executed_pipe_fma.avg.peak_sustained ./ncu_get_gpu_peak_sustained | grep -v "n/a"

输出

bash 复制代码
fma_kernel_v0(float *, float *) (1, 1, 1)x(32, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
---------------------------------------------------------------- ----------- ------------
Metric Name                                                      Metric Unit Metric Value
---------------------------------------------------------------- ----------- ------------
sm__cycles_active.max                                                  cycle          998
sm__sass_thread_inst_executed_op_ffma_pred_on.avg.peak_sustained  inst/cycle          128
sm__sass_thread_inst_executed_op_ffma_pred_on.max                       inst           32 # 1.实际只有一个warp,且只有一条fma sass指令
sm__sass_thread_inst_executed_op_ffma_pred_on.sum.peak_sustained  inst/cycle        3,584 # fma峰值性能
sm__thread_inst_executed_pipe_fma_pred_on.max                           inst           96
smsp__cycles_active.max                                                cycle          972
smsp__cycles_elapsed.avg.per_second                                      Ghz         1.88
smsp__cycles_elapsed.max                                               cycle        2,704
smsp__inst_executed.max                                                 inst           14
smsp__inst_executed_pipe_fma.avg.peak_sustained                   inst/cycle            1
smsp__inst_executed_pipe_fma.max                                        inst            3 # 2.实际执行了3条fma warp指令
smsp__inst_executed_pipe_fma.sum.peak_sustained                   inst/cycle          112
smsp__inst_issued.max                                                   inst           18
smsp__issue_active.max                                                 cycle           18 # 发射条数比实际执行的多
smsp__pipe_fma_cycles_active.avg.peak_sustained                                         2
smsp__pipe_fma_cycles_active.max                                       cycle            8 # 实际上3条fma用8个cycle
smsp__pipe_fma_cycles_active.max.peak_sustained                                         2 # 理论上一条fma指令需要2个cycle
smsp__pipe_fma_cycles_active.sum.peak_sustained                                       224 # 2*28(sm)*4(smsp)
smsp__warps_active.max                                                  warp          972
smsp__warps_eligible.max                                                warp           18
smsp__warps_issue_stalled_branch_resolving.max                          warp            8
smsp__warps_issue_stalled_dispatch_stall.max                            warp            2
smsp__warps_issue_stalled_drain.max                                     warp           21
smsp__warps_issue_stalled_imc_miss.max                                  warp          750 #等待加载context数据
smsp__warps_issue_stalled_misc.max                                      warp            2
smsp__warps_issue_stalled_no_instruction.max                            warp          114 #等待加载指令
smsp__warps_issue_stalled_selected.max                                  warp           18
smsp__warps_issue_stalled_short_scoreboard.max                          warp           46 #等待从share memory加载数据
smsp__warps_issue_stalled_wait.max                                      warp           39
---------------------------------------------------------------- ----------- ------------

fma_kernel_v1(float *, float *) (1, 1, 1)x(32, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
Warning: Data collection happened without fixed GPU frequencies. Profiling results may be inconsistent.
Section: Command line profiler metrics
---------------------------------------------------------------- ----------- ------------
Metric Name                                                      Metric Unit Metric Value
---------------------------------------------------------------- ----------- ------------
sm__cycles_active.max                                                  cycle        1,031
sm__inst_executed_pipe_fma.max                                          inst            1 # 正常了,一条fma指令2个cycle
sm__pipe_fma_cycles_active.max                                         cycle            2
sm__sass_thread_inst_executed_op_ffma_pred_on.avg.peak_sustained  inst/cycle          128
sm__sass_thread_inst_executed_op_ffma_pred_on.max                       inst           32
sm__sass_thread_inst_executed_op_ffma_pred_on.sum.peak_sustained  inst/cycle        3,584
sm__thread_inst_executed_pipe_fma_pred_on.max                           inst           32
smsp__cycles_active.max                                                cycle        1,000
smsp__cycles_elapsed.avg.per_second                                      Ghz         1.88
smsp__cycles_elapsed.max                                               cycle        2,711
smsp__inst_executed.max                                                 inst           11
smsp__inst_executed_pipe_fma.avg.peak_sustained                   inst/cycle            1
smsp__inst_executed_pipe_fma.max                                        inst            1
smsp__inst_executed_pipe_fma.sum.peak_sustained                   inst/cycle          112
smsp__inst_issued.max                                                   inst           16
smsp__issue_active.max                                                 cycle           16
smsp__pipe_fma_cycles_active.avg.peak_sustained                                         2
smsp__pipe_fma_cycles_active.max                                       cycle            2
smsp__pipe_fma_cycles_active.max.peak_sustained                                         2
smsp__pipe_fma_cycles_active.sum.peak_sustained                                       224
smsp__warps_active.max                                                  warp        1,000
smsp__warps_eligible.max                                                warp           16
smsp__warps_issue_stalled_branch_resolving.max                          warp            8
smsp__warps_issue_stalled_drain.max                                     warp           33
smsp__warps_issue_stalled_imc_miss.max                                  warp          743
smsp__warps_issue_stalled_misc.max                                      warp            1
smsp__warps_issue_stalled_no_instruction.max                            warp          157
smsp__warps_issue_stalled_selected.max                                  warp           16
smsp__warps_issue_stalled_short_scoreboard.max                          warp            6
smsp__warps_issue_stalled_wait.max                                      warp           33
---------------------------------------------------------------- ----------- ------------

小结

text 复制代码
1.不同的使用方式,可能会导致执行重复发射
2.加载context和指令的开销不可避免,远大于执行一条fma指令需要的cycle数
3.FMA PIPE利用率100%时,每个smsp cycle,fma_cycles为2个cycle,即fma pipe需要二个cycle
4.smsp__pipe_fma_cycles_active(2)=smsp__cycles_active(1000)*2 时才能达到峰值性能
5.smsp__cycles_active(1000)=smsp__warps_active(1000)+其它开销(0)
6.一个warp可能同时处于多个smsp__warps_issue_stalled状态,因此不能准确知道一共stall了多长时间
7.smsp__warps_active(1000)=smsp__issue_active(16)+smsp__warps_eligible(16)+smsp__warps_issue_stalled*(>743)
8.假设去掉加载context的时间(实际不能去掉).这个简单的kernel,加载指令也需要100多个cycle,视它为固定开销

2.假设固定开销为120个cycle,希望fma pipe利用率超过95%,需要多少条指令呢,求解以下不等式:

python 复制代码
tee solve.py<<-'EOF'
import sympy as sp
from sympy import Symbol, And
n = sp.symbols('n', positive=True)
inequality = (2*n) / ((n+n) + 120) > 0.95
sol = sp.solve([inequality])
print(sol)
EOF
python solve.py

输出

bash 复制代码
1140 < n  #最少需要1140条fma指令

3.采用1140条fma指令测试

tee fma_kernel.cu<<-'EOF'
#include <iostream>
#include <cuda_runtime.h>
__global__ void fma_kernel(float *input,float *output) {

    #define COUNT 4
    float d0[COUNT];
    float d1[COUNT];
    float d2[COUNT];
    float d3[COUNT];
    int tid  = threadIdx.x + blockIdx.x * blockDim.x;
    float a=clock();
    float b=clock();
    float c=clock();
    //4*4*72=1152条fma指令
    #pragma unroll
    for(int j=0;j<72;j++)
    {
        #pragma unroll
        for(int i=0;i<COUNT;i++)
        {
            d0[i]=input[i*32+tid];
            __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d0[i]) : "f"(a),"f"(b),"f"(d0[i]));
            __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d1[i]) : "f"(a),"f"(b),"f"(d1[i]));
            __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d2[i]) : "f"(a),"f"(b),"f"(d2[i]));
            __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d3[i]) : "f"(a),"f"(b),"f"(d3[i]));        
        }
    }
    #pragma unroll
    for(int i=0;i<COUNT;i++)
    {
        __asm__  __volatile__("st.global.v4.f32 [%0],{%1,%2,%3,%4};" :: "l"(&output[i*32+tid]),"f"(d0[i]),"f"(d1[i]),"f"(d2[i]),"f"(d3[i]): "memory");
    }
}
EOF

4.生成fatbin

bash 复制代码
# 生成ptx
/usr/local/cuda/bin/nvcc -std=c++17 -dc -lineinfo -arch=sm_86 -ptx fma_kernel.cu -o fma_kernel.ptx
# 生成cubin
/usr/local/cuda/bin/nvcc -arch=sm_86 fma_kernel.ptx -cubin -o fma_kernel.cubin
# 生成fatbin
/usr/local/cuda/bin/nvcc -arch=sm_86 fma_kernel.cubin -fatbin -o fma_kernel.fatbin
# 查看ptx
cat fma_kernel.ptx
# 查看sass指令
/usr/local/cuda/bin/cuobjdump --dump-sass fma_kernel.fatbin

5.修改SASS指令,删除掉STG.E.STRONG.SYS指令,重新生成fatbin

bash 复制代码
cuasm.py fma_kernel.cubin fma_kernel.cuasm
 
# 仅保留FMA指令
sed '/MOV/d' -i fma_kernel.cuasm
sed '/ULDC/d' -i fma_kernel.cuasm
sed '/STG/d' -i fma_kernel.cuasm
sed '/I2F/d' -i fma_kernel.cuasm
sed '/CS2R/d' -i fma_kernel.cuasm
sed '/BRA/d' -i fma_kernel.cuasm
sed '/LDG/d' -i fma_kernel.cuasm
sed '/IMAD/d' -i fma_kernel.cuasm
sed '/S2R/d' -i fma_kernel.cuasm

# 生新行成cubin
cuasm.py fma_kernel.cuasm
# 生成fatbin
/usr/local/cuda/bin/nvcc -arch=sm_86 fma_kernel.cubin -fatbin -o fma_kernel.fatbin

6.准备测试程序,加载fatbin并运行里面的Kernel

bash 复制代码
tee fma_kernel_main.cpp<<-'EOF'
#include <stdio.h>
#include <string.h>
#include <cuda_runtime.h>
#include <cuda.h>

int main(int argc,char *argv[])
{
    CUresult error;
    CUdevice cuDevice;
    cuInit(0);
    int deviceCount = 0;
    error = cuDeviceGetCount(&deviceCount);
    error = cuDeviceGet(&cuDevice, 0);
    if(error!=CUDA_SUCCESS)
        {
        printf("Error happened in get device!\n");
    }
    CUcontext cuContext;
    error = cuCtxCreate(&cuContext, 0, cuDevice);
    if(error!=CUDA_SUCCESS)
        {
        printf("Error happened in create context!\n");
    }

    CUmodule module;
    CUfunction function;

    const char* module_file = "fma_kernel.fatbin";
    const char* kernel_name = "_Z10fma_kernelPfS_";

    error = cuModuleLoad(&module, module_file);
    if(error!=CUDA_SUCCESS)
        {
        printf("Error happened in load moudle %d!\n",error);
    }

    error = cuModuleGetFunction(&function, module, kernel_name);
    if(error!=CUDA_SUCCESS)
    {
        printf("get function error!\n");
    }

    int data_size=sizeof(float)*8192;

    float *output_ptr=nullptr;
    float *input_ptr=nullptr;
    int cudaStatus=0;

    cudaStatus = cudaMalloc((void**)&input_ptr, data_size);
    cudaStatus = cudaMalloc((void**)&output_ptr, data_size);

    void *kernelParams[]= {(void*)&output_ptr, (void*)&input_ptr};

    cuLaunchKernel(function,
                1, 1, 1,
                32, 1, 1,
                0,0,kernelParams, 0);
  
    cuLaunchKernel(function,
                1, 1, 1,
                32*4, 1, 1,
                0,0,kernelParams, 0);

    cuLaunchKernel(function,
                1, 1, 1,
                32*4*2, 1, 1,
                0,0,kernelParams, 0);
                  
    cuLaunchKernel(function,
                28*2, 1, 1,
                32*4, 1, 1,
                0,0,kernelParams, 0);  
                
    cuLaunchKernel(function,
                1, 1, 1,
                32*4*4, 1, 1,
                0,0,kernelParams, 0);
           
    cuLaunchKernel(function,
                28*4, 1, 1,
                32*4, 1, 1,
                0,0,kernelParams, 0);  
           
    cudaFree(output_ptr);
    cudaFree(input_ptr);
    cuModuleUnload(module);
    cuCtxDestroy(cuContext);
    return 0;
}
EOF
g++ fma_kernel_main.cpp -o fma_kernel_main -I /usr/local/cuda/include -L /usr/local/cuda/lib64 -lcudart -lcuda

7.ncu profing

bash 复制代码
/usr/local/NVIDIA-Nsight-Compute/ncu --clock-control=none --metrics \
smsp__pipe_fma_cycles_active.max,\
sm__pipe_fma_cycles_active.max,\
sm__cycles_active.max,\
smsp__warps_active.max,\
smsp__cycles_active.max ./fma_kernel_main

输出

bash 复制代码
# 只有一个smsp 且只有一个warp
fma_kernel(float *, float *) (1, 1, 1)x(32, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
-------------------------------- ----------- ------------
Metric Name                      Metric Unit Metric Value
-------------------------------- ----------- ------------
sm__cycles_active.max                  cycle        1,999
sm__pipe_fma_cycles_active.max         cycle        1,736
smsp__cycles_active.max                cycle        1,981
smsp__pipe_fma_cycles_active.max       cycle        1,736
smsp__warps_active.max                  warp        1,991
-------------------------------- ----------- ------------

fma_kernel(float *, float *) (1, 1, 1)x(128, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
-------------------------------- ----------- ------------
Metric Name                      Metric Unit Metric Value
-------------------------------- ----------- ------------
sm__cycles_active.max                  cycle        1,988
sm__pipe_fma_cycles_active.max         cycle        6,944 # 4个smsp分别执行一个warp,同样的sm__cycles_active下fma性能提升了4倍
smsp__cycles_active.max                cycle        1,964
smsp__pipe_fma_cycles_active.max       cycle        1,736
smsp__warps_active.max                  warp        2,007
-------------------------------- ----------- ------------

# 1个sm 4个smsp,每个上分配2个warp
fma_kernel(float *, float *) (1, 1, 1)x(256, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
-------------------------------- ----------- ------------
Metric Name                      Metric Unit Metric Value
-------------------------------- ----------- ------------
sm__cycles_active.max                  cycle        2,034
sm__pipe_fma_cycles_active.max         cycle       13,888
smsp__cycles_active.max                cycle        2,012 # 2012-1964=只增加了48个cycle,但FMA的性能翻倍(3472/1736) 但只有理论值的86% (3472/2012/2)
smsp__pipe_fma_cycles_active.max       cycle        3,472
smsp__warps_active.max                  warp        3,951
-------------------------------- ----------- ------------

# 多个block跟
fma_kernel(float *, float *) (56, 1, 1)x(128, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
-------------------------------- ----------- ------------
Metric Name                      Metric Unit Metric Value
-------------------------------- ----------- ------------
sm__cycles_active.max                  cycle        2,000
sm__pipe_fma_cycles_active.max         cycle       13,888
smsp__cycles_active.max                cycle        1,982
smsp__pipe_fma_cycles_active.max       cycle        3,472
smsp__warps_active.max                  warp        3,972
-------------------------------- ----------- ------------

# 1个sm 4个smsp,每个上分配4个warp
fma_kernel(float *, float *) (1, 1, 1)x(512, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
-------------------------------- ----------- ------------
Metric Name                      Metric Unit Metric Value
-------------------------------- ----------- ------------
sm__cycles_active.max                  cycle        3,657
sm__pipe_fma_cycles_active.max         cycle       27,776
smsp__cycles_active.max                cycle        3,634 #3634-2012=增加了1622个cycle,FMA性能翻倍(6944/3472) 达到理论性能的95%(6944/3634/2)
smsp__pipe_fma_cycles_active.max       cycle        6,944
smsp__warps_active.max                  warp       11,444
-------------------------------- ----------- ------------

# 每个smsp 4个warp
fma_kernel(float *, float *) (112, 1, 1)x(128, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
-------------------------------- ----------- ------------
Metric Name                      Metric Unit Metric Value
-------------------------------- ----------- ------------
sm__cycles_active.max                  cycle        3,669
sm__pipe_fma_cycles_active.max         cycle       27,776
smsp__cycles_active.max                cycle        3,649
smsp__pipe_fma_cycles_active.max       cycle        6,944
smsp__warps_active.max                  warp       10,981
-------------------------------- ----------- ------------

8.将Kernel里的FMA指令增加4倍,一个smsp一个warp能打满利用率吗【不行】

bash 复制代码
tee fma_kernel.cu<<-'EOF'
#include <iostream>
#include <cuda_runtime.h>

__global__ void fma_kernel(float *input,float *output) {

    #define COUNT 4
    float d0[COUNT];
    float d1[COUNT];
    float d2[COUNT];
    float d3[COUNT];
    int tid  = threadIdx.x + blockIdx.x * blockDim.x;
    float a=clock();
    float b=clock();
    float c=clock();

    #pragma unroll
    for(int j=0;j<72*4;j++)
    {
        #pragma unroll
        for(int i=0;i<COUNT;i++)
        {
            d0[i]=input[i*32+tid];
            __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d0[i]) : "f"(a),"f"(b),"f"(d0[i]));
            __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d1[i]) : "f"(a),"f"(b),"f"(d1[i]));
            __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d2[i]) : "f"(a),"f"(b),"f"(d2[i]));
            __asm__  __volatile__("fma.rn.f32 %0,%1,%2,%3;" : "=f"(d3[i]) : "f"(a),"f"(b),"f"(d3[i]));        
        }
    }
    #pragma unroll
    for(int i=0;i<COUNT;i++)
    {
        __asm__  __volatile__("st.global.v4.f32 [%0],{%1,%2,%3,%4};" :: "l"(&output[i*32+tid]),"f"(d0[i]),"f"(d1[i]),"f"(d2[i]),"f"(d3[i]): "memory");
    }
}
EOF

# 生成ptx
/usr/local/cuda/bin/nvcc -std=c++17 -dc -lineinfo -arch=sm_86 -ptx fma_kernel.cu -o fma_kernel.ptx
# 生成cubin
/usr/local/cuda/bin/nvcc -arch=sm_86 fma_kernel.ptx -cubin -o fma_kernel.cubin
# 生成fatbin
/usr/local/cuda/bin/nvcc -arch=sm_86 fma_kernel.cubin -fatbin -o fma_kernel.fatbin

cuasm.py fma_kernel.cubin fma_kernel.cuasm
 
# 仅保留FMA指令
sed '/MOV/d' -i fma_kernel.cuasm
sed '/ULDC/d' -i fma_kernel.cuasm
sed '/STG/d' -i fma_kernel.cuasm
sed '/I2F/d' -i fma_kernel.cuasm
sed '/CS2R/d' -i fma_kernel.cuasm
sed '/BRA/d' -i fma_kernel.cuasm
sed '/LDG/d' -i fma_kernel.cuasm
sed '/IMAD/d' -i fma_kernel.cuasm
sed '/S2R/d' -i fma_kernel.cuasm

# 生新行成cubin
cuasm.py fma_kernel.cuasm
# 生成fatbin
/usr/local/cuda/bin/nvcc -arch=sm_86 fma_kernel.cubin -fatbin -o fma_kernel.fatbin
/usr/local/NVIDIA-Nsight-Compute/ncu --clock-control=none --metrics \
smsp__pipe_fma_cycles_active.max,\
sm__pipe_fma_cycles_active.max,\
sm__cycles_active.max,\
smsp__warps_active.max,\
smsp__cycles_active.max ./fma_kernel_main

输出

bash 复制代码
fma_kernel(float *, float *) (1, 1, 1)x(32, 1, 1), Context 1, Stream 7, Device 0, CC 8.6
-------------------------------- ----------- ------------
Metric Name                      Metric Unit Metric Value
-------------------------------- ----------- ------------
sm__cycles_active.max                  cycle        7,677
sm__pipe_fma_cycles_active.max         cycle        6,920
smsp__cycles_active.max                cycle        7,659
smsp__pipe_fma_cycles_active.max       cycle        6,920 # 不行
smsp__warps_active.max                  warp        7,399
-------------------------------- ----------- ------------
相关推荐
前端青山6 小时前
Node.js-增强 API 安全性和性能优化
开发语言·前端·javascript·性能优化·前端框架·node.js
青云交11 小时前
大数据新视界 -- 大数据大厂之 Impala 性能优化:应对海量复杂数据的挑战(上)(7/30)
大数据·性能优化·impala·数据分区·查询优化·海量复杂数据·经典案例
chusheng184014 小时前
Python 爬取大量数据如何并发抓取与性能优化
开发语言·python·性能优化
探索云原生1 天前
GPU 环境搭建指南:如何在裸机、Docker、K8s 等环境中使用 GPU
ai·云原生·kubernetes·go·gpu
XMYX-01 天前
MySQL 性能优化策略:提升响应速度与系统稳定性
mysql·性能优化
一个处女座的程序猿1 天前
AI之硬件对比:据传英伟达Nvidia2025年将推出RTX 5090-32GB/RTX 5080-24GB、华为2025年推出910C/910D
人工智能·gpu
PangPiLoLo2 天前
高可用架构-业务高可用
java·性能优化·架构
尸僵打怪兽2 天前
软考(中级-软件设计师)数据库篇(1101)
数据库·oracle·性能优化·软考
程序猿进阶2 天前
系统上云-流量分析和链路分析
java·后端·阿里云·面试·性能优化·系统架构·云计算
飞腾开发者3 天前
飞腾平台Arm ComputeLibrary编译安装指南
linux·服务器·arm开发·后端·性能优化