WholeReduce

函数功能

全归约指令分为三种：

• WholeReduceMax：每个repeat内所有数据求最大值以及其索引index。（返回的索引值每个repeat内都会从0开始计数）
• WholeReduceMin：每个repeat内所有数据求最小值以及其索引index。（返回的索引值每个repeat内都会从0开始计数）
• WholeReduceSum：每个repeat内所有数据求和。

函数原型

参数说明

返回值

无

支持的型号

Atlas 训练系列产品

Atlas推理系列产品AI Core

Atlas A2训练系列产品

约束说明

• srcLocal和dstLocal地址对齐要求请见 ：通用约束。
• 对于WholeReduceMax/WholeReduceMin接口，dstLocal结果存储顺序由order决定，默认为最值，最值索引。返回结果中索引index数据是按照dstLocal的数据类型进行存储的，比如dstLocal使用half类型时，index是按照half类型进行存储的，读取时需要使用reinterpret_cast方法转换到整数类型，若输入数据类型是half，需要使用reinterpret_cast<uint16_t*>，若输入是float，需要使用reinterpret_cast<uint32_t*>。比如完整样例一中，前两个计算结果为[9.980e-01 5.364e-06], 5.364e-06需要使用reinterpret_cast方法转换得到索引值90。
• 对于WholeReduceSum，其内部的相加方式采用二叉树方式，两两相加：

  假设源操作数为128个float16的数据[data0,data1,data2...data127]，一个repeat可以计算完，计算过程如下。

  1. data0和data1相加得到data00，data2和data3相加得到data01...data124和data125相加得到data62，data126和data127相加得到data63；
  2. data00和data01相加得到data000，data02和data03相加得到data001...data62和data63相加得到data031；
  3. 以此类推，得到目的操作数为1个float16的数据[data]。

  需要注意的是两两相加的计算过程中，计算结果大于65504时结果保存为65504。例如源操作数为[60000,60000,-30000,100]，首先60000+60000溢出，结果为65504，第二步计算-30000+100=-29900，第四步计算65504-29900=35604。

调用示例

• 0级接口样例-mask连续模式

  // dstLocal,srcLocal均为half类型,srcLocal的计算数据量为512，连续排布，计算结果也需要连续排布，使用0级接口，设定mask为最多的128个全部元素参与计算
  // 根据以上信息，推断出repeatTimes为4，dstRepStride为1，srcBlkStride为1，srcRepStride为8
  // 若求最大值及索引，并且需要存储顺序为[value, index]的结果，可以使用默认order，接口示例为：
  WholeReduceMax<half>(dstLocal, srcLocal, 128, 4, 1, 1, 8);
  // 若求最大值及索引，并且需要存储顺序为[index, value]的结果，接口示例为：
  WholeReduceMax<half>(dstLocal, srcLocal, 128, 4, 1, 1, 8, ReduceOrder::ORDER_INDEX_VALUE);
  
  // 若求和，则接口示例为：
  WholeReduceSum<half>(dstLocal, srcLocal, 128, 4, 1, 1, 8); 

• 0级接口样例-mask逐bit模式

  // dstLocal,srcLocal均为half类型,srcLocal的计算数据量为512，连续排布，计算结果也需要连续排布，使用0级接口，设定mask为最多的128个全部元素参与计算
  // 根据以上信息，推断出repeatTimes为4，dstRepStride为1，srcBlkStride为1，srcRepStride为8
  // 若求最大值及索引，并且需要存储顺序为[value, index]的结果，使用默认order，接口示例为：
  WholeReduceMax<half>(dstLocal, srcLocal, { 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF }, 4, 1, 1, 8);

• 完整样例一

#include "kernel_operator.h"

namespace AscendC {
class KernelReduce {
public:
    __aicore__ inline KernelReduce() {}
    __aicore__ inline void Init(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
    {
        srcGlobal.SetGlobalBuffer((__gm__ half*)src);
        dstGlobal.SetGlobalBuffer((__gm__ half*)dstGm);

        repeat = srcDataSize / mask;
        pipe.InitBuffer(inQueueSrc, 1, srcDataSize * sizeof(half));
        pipe.InitBuffer(outQueueDst, 1, dstDataSize * sizeof(half));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        Compute();
        CopyOut();
    }

private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<half> srcLocal = inQueueSrc.AllocTensor<half>();
        DataCopy(srcLocal, srcGlobal, srcDataSize);
        inQueueSrc.EnQue(srcLocal);
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<half> srcLocal = inQueueSrc.DeQue<half>();
        LocalTensor<half> dstLocal = outQueueDst.AllocTensor<half>();

        WholeReduceMax<half>(dstLocal, srcLocal, mask, repeat, 1, 1, 8); // 使用默认order

        outQueueDst.EnQue<half>(dstLocal);
        inQueueSrc.FreeTensor(srcLocal);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<half> dstLocal = outQueueDst.DeQue<half>();
        DataCopy(dstGlobal, dstLocal, dstDataSize);
        outQueueDst.FreeTensor(dstLocal);
    }

private:
    TPipe pipe;
    TQue<QuePosition::VECIN, 1> inQueueSrc;
    TQue<QuePosition::VECOUT, 1> outQueueDst;
    GlobalTensor<half> srcGlobal, dstGlobal;
    int srcDataSize = 1024;
    int dstDataSize = 16;
    int mask = 128;
    int repeat = 0;
};
} // namespace AscendC

extern "C" __global__ __aicore__ void reduce_kernel(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
{
    AscendC::KernelReduce op;
    op.Init(src, dstGm);
    op.Process();
}
输入数据(src_gm):
[0.00787  0.8516   0.01558  0.152    0.887    0.2532   0.2272   0.1295
 0.7207   0.628    0.5522   0.991    0.3164   0.961    0.526    0.5513
 0.03973  0.3293   0.809    0.562    0.915    0.56     0.3464   0.3438
 0.6094   0.1201   0.8384   0.848    0.004436 0.4263   0.01917  0.753
 0.9126   0.2307   0.1066   0.644    0.8657   0.7085   0.7915   0.1707
 0.3806   0.957    0.0483   0.858    0.10675  0.21     0.03345  0.55
 0.3757   0.3281   0.927    0.09406  0.6445   0.985    0.405    0.09393
 0.773    0.7227   0.03714  0.595    0.889    0.0948   0.4202   0.2747
 0.5894   0.3022   0.894    0.675    0.6016   0.938    0.585    0.5244
 0.8643   0.888    0.794    0.636    0.976    0.148    0.7427   0.1742
 0.32     0.0649   0.2954   0.2018   0.833    0.0976   0.4048   0.2861
 0.8765   0.722    0.998    0.03041  0.005512 0.9087   0.9873   0.1436
 0.4812   0.1901   0.78     0.6934   0.2317   0.3782   0.8613   0.808
 0.06885  0.3584   0.5684   0.541    0.5415   0.3096   0.5957   0.9043
 0.7964   0.501    0.4324   0.7544   0.687    0.8447   0.526    0.548
 0.926    0.9106   0.1616   0.183    0.6704   0.642    0.4783   0.1797
 0.2078   0.59     0.4866   0.4683   0.649    0.7266   0.4976   0.8364
 0.6245   0.07385  0.0786   0.586    0.7827   0.3298   0.9497   0.1617
 0.4375   0.3572   0.2896   0.6465   0.1156   0.4905   0.2617   0.8267
 0.2054   0.1415   0.2993   0.8374   0.754    0.942    0.6416   0.1222
 0.1465   0.3335   0.3577   0.6484   0.614    0.5825   0.6807   0.9297
 0.694    0.759    0.908    0.9126   0.4731   0.963    0.3271   0.724
 0.4077   0.335    0.672    0.4219   0.1818   0.843    0.2708   0.0816
 0.457    0.3481   0.67     0.6895   0.6924   0.191    0.2013   0.2484
 0.8833   0.9146   0.4102   0.1063   0.6685   0.804    0.6606   0.2491
 0.34     0.3281   0.823    0.603    0.521    0.6797   0.401    0.5
 0.03683  0.04758  0.507    0.667    0.9014   0.263    0.2477   0.0179
 0.8735   0.007023 0.545    0.758    0.3508   0.6333   0.9375   0.5903
 0.2732   0.0847   0.489    0.196    0.5557   0.403    0.9204   0.3655
 0.5083   0.7515   0.3347   0.6914   0.2185   0.2458   0.5537   0.3457
 0.4878   0.869    0.908    0.0877   0.295    0.9      0.9307   0.05545
 0.4639   0.4001   0.8433   0.4883   0.916    0.7026   0.5063   0.05164
 0.936    0.844    0.2086   0.625    0.0197   0.4312   0.3677   0.983
 0.625    0.004665 0.2479   0.3093   0.9214   0.003672 0.7915   0.921
 0.331    0.01127  0.703    0.6416   0.4053   0.53     0.9688   0.10297
 0.5547   0.07367  0.2305   0.02821  0.8115   0.4202   0.0561   0.0917
 0.04828  0.536    0.0905   0.328    0.8413   0.3696   0.982    0.3733
 0.436    0.753    0.1937   0.8706   0.991    0.273    0.763    0.418
 0.4446   0.513    0.6724   0.1179   0.921    0.756    0.7144   0.6196
 0.9634   0.562    0.3088   0.864    0.709    0.6797   0.2114   0.534
 0.5225   0.1852   0.038    0.5454   0.8823   0.849    0.608    0.7734
 0.7446   0.7236   0.1903   0.1031   0.497    0.57     0.172    0.1907
 0.6333   0.641    0.681    0.2323   0.1007   0.4094   0.3655   0.4248
 0.08044  0.1483   0.08716  0.354    0.128    0.3933   0.775    0.215
 0.728    0.909    0.4204   0.618    0.2517   0.9106   0.3647   0.5977
 0.3445   0.315    0.488    0.99     0.9443   0.6196   0.9287   0.088
 0.9946   0.796    0.7515   0.1912   0.4312   0.7974   0.735    0.01536
 0.7456   0.643    0.484    0.218    0.9272   0.1703   0.1885   0.1982
 0.754    0.902    0.848    0.05832  0.4138   0.6885   0.3853   0.3499
 0.639    0.5786   0.6353   0.5664   0.02621  0.56     0.532    0.08246
 0.733    0.1334   0.0728   0.7817   0.5273   0.126    0.179    0.7334
 0.1565   0.457    0.4807   0.6987   0.5845   0.6206   0.902    0.9277
 0.501    0.6763   0.3418   0.7925   0.07556  0.0929   0.9014   0.3145
 0.04907  0.7188   0.958    0.7275   0.1963   0.1742   0.785    0.518
 0.61     0.1112   0.481    0.10583  0.198    0.181    0.3271   0.2773
 0.2391   0.5625   0.621    0.173    0.05936  0.5654   0.838    0.865
 0.01523  0.6724   0.546    0.737    0.778    0.8613   0.7085   0.8213
 0.08826  0.818    0.4866   0.159    0.4143   0.1007   0.7773   0.487
 0.5225   0.8984   0.4907   0.525    0.4075   0.2632   0.2292   0.134
 0.4622   0.65     0.294    0.607    0.2725   0.2603   0.9326   0.787
 0.9478   0.941    0.3066   0.2944   0.3928   0.73     0.1797   0.2157
 0.609    0.4216   0.8984   0.8477   0.863    0.2478   0.993    0.6274
 0.724    0.03668  0.0991   0.5825   0.662    0.6904   0.7017   0.2379
 0.514    0.1646   0.3245   0.03072  0.3232   0.907    0.9966   0.6396
 0.2969   0.02539  0.66     0.764    0.7803   0.515    0.04074  0.2258
 0.08887  0.1782   0.875    0.1517   0.2351   0.3848   0.5933   0.6875
 0.1969   0.1283   0.06232  0.4348   0.168    0.6904   0.5464   0.12036
 0.885    0.007717 0.5967   0.2856   0.628    0.62     0.854    0.4297
 0.733    0.2274   0.9736   0.01622  0.456    0.4763   0.9707   0.874
 0.8794   0.511    0.1628   0.03458  0.506    0.1464   0.3674   0.1532
 0.786    0.3809   0.406    0.015434 0.901    0.951    0.3018   0.3584
 0.5337   0.4983   0.85     0.833    0.7324   0.492    0.39     0.09845
 0.8965   0.862    0.4033   0.181    0.2203   0.3738   0.2761   0.9653
 0.3577   0.289    0.3167   0.91     0.2688   0.3972   0.585    0.2178
 0.307    0.4966   0.513    0.5225   0.786    0.1888   0.9287   0.5093
 0.1193   0.3987   0.799    0.9995   0.611    0.9897   0.7515   0.4478
 0.3232   0.2426   0.3323   0.7134   0.77     0.7275   0.02043  0.3132
 0.3555   0.03122  0.8623   0.4705   0.6357   0.3157   0.5063   0.1711
 0.885    0.7554   0.815    0.0213   0.4346   0.049    0.905    0.525
 0.921    0.02411  0.771    0.7227   0.1786   0.278    0.03387  0.7744
 0.05875  0.8955   0.8374   0.715    0.3765   0.02075  0.675    0.9883
 0.63     0.7017   0.299    0.92     0.1644   0.3977   0.487    0.818
 0.636    0.3452   0.6406   0.783    0.3728   0.1619   0.7725   0.4673
 0.297    0.9375   0.083    0.0914   0.6704   0.08923  0.332    0.0973
 0.507    0.201    0.1658   0.2358   0.8706   0.6846   0.6396   0.289
 0.831    0.669    0.4683   0.2568   0.219    0.616    0.978    0.1564
 0.925    0.4265   0.6055   0.7246   0.235    0.5376   0.03668  0.2441
 0.7935   0.383    0.2996   0.3523   0.2544   0.6006   0.8896   0.757
 0.7134   0.3196   0.3657   0.249    0.2429   0.921    0.877    0.728
 0.8853   0.1635   0.546    0.9243   0.676    0.4749   0.3928   0.4187
 0.612    0.3953   0.2372   0.4092   0.1523   0.1599   0.03108  0.1602
 0.2474   0.3572   0.0643   0.9434   0.52     0.8574   0.959    0.7593
 0.2318   0.5444   0.2222   0.3884   0.8066   0.4573   0.664    0.335
 0.02025  0.1519   0.01386  0.989    0.852    0.695    0.01289  0.3433
 0.2148   0.9404   0.6753   0.704    0.11163  0.675    0.5264   0.1514
 0.5273   0.9785   0.2769   0.4846   0.2747   0.558    0.742    0.681
 0.835    0.9546   0.941    0.588    0.785    0.2095   0.07294  0.4343
 0.086    0.5825   0.513    0.6313   0.04236  0.4072   0.558    0.681
 0.4805   0.492    0.625    0.7744   0.002626 0.662    0.9043   0.4766
 0.6597   0.6934   0.3394   0.05453  0.9146   0.2222   0.7925   0.605
 0.812    0.671    0.4329   0.2118   0.363    0.1444   0.0955   0.692
 0.675    0.3      0.6846   0.535    0.9834   0.929    0.3582   0.964
 0.3835   0.1466   0.801    0.954    0.2554   0.01357  0.6636   0.8325
 0.6494   0.817    0.2268   0.00904  0.0487   0.08716  0.6753   0.3833
 0.663    0.396    0.6685   0.983    0.0728   0.694    0.02364  0.137
 0.1727   0.231    0.7896   0.8057   0.478    0.883    0.1785   0.5938
 0.11456  0.6997   0.1945   0.02365  0.7236   0.8623   0.2178   0.1295
 0.3867   0.7188   0.11475  0.6      0.419    0.2673   0.4404   0.0107
 0.4304   0.1364   0.3708   0.1158   0.1714   0.3123   0.3403   0.7163
 0.079    0.6245   0.719    0.558    0.4526   0.09924  0.512    0.2452
 0.519    0.999    0.7207   0.5605   0.7217   0.653    0.1164   0.789
 0.4724   0.2727   0.10315  0.9644   0.7573   0.06464  0.858    0.7847
 0.958    0.618    0.9536   0.46     0.9766   0.4263   0.4363   0.4434
 0.95     0.3032   0.4338   0.809    0.1642   0.0561   0.2668   0.1853
 0.356    0.934    0.968    0.327    0.913    0.434    0.6616   0.00502
 0.05066  0.5327   0.276    0.5176   0.0674   0.6143   0.8345   0.2976
 0.315    0.6646   0.527    0.791    0.0299   0.4558   0.8354   0.3115
 0.3735   0.3582   0.742    0.2637   0.8877   0.7603   0.4568   0.2045
 0.4746   0.392    0.65     0.391    0.972    0.6973   0.2297   0.568
 0.49     0.1895   0.547    0.79     0.747    0.5205   0.313    0.3809
 0.7817   0.32     0.1012   0.339    0.716    0.8955   0.8564   0.126
 0.6597   0.228    0.1194   0.4775   0.173    0.0265   0.7456   0.859
 0.4841   0.595    0.4553   0.1351   0.2246   0.3564   0.1832   0.8535
 0.703    0.2423   0.04187  0.145    0.997    0.1919   0.571    0.8555
 0.1578   0.2688   0.405    0.3909   0.1428   0.863    0.7295   0.3267
 0.1294   0.5986   0.677    0.7065   0.8853   0.923    0.9385   0.935
 0.1747   0.32     0.2292   0.2676   0.1161   0.4666   0.3826   0.2588
 0.1863   0.7993   0.3984   0.2961   0.2952   0.3247   0.923    0.05746 ]
输出数据(dst_gm):
[9.980e-01 5.364e-06 9.629e-01 2.682e-06 9.946e-01 6.676e-06 9.966e-01
 7.510e-06 9.995e-01 5.424e-06 9.888e-01 6.378e-06 9.990e-01 6.735e-06
 9.971e-01 5.484e-06]

• 完整样例二

#include "kernel_operator.h"

namespace AscendC {
class KernelReduce {
public:
    __aicore__ inline KernelReduce() {}
    __aicore__ inline void Init(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
    {
        srcGlobal.SetGlobalBuffer((__gm__ half*)src);
        dstGlobal.SetGlobalBuffer((__gm__ half*)dstGm);

        repeat = srcDataSize / mask;
        pipe.InitBuffer(inQueueSrc, 1, srcDataSize * sizeof(half));
        pipe.InitBuffer(outQueueDst, 1, dstDataSize * sizeof(half));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        Compute();
        CopyOut();
    }

private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<half> srcLocal = inQueueSrc.AllocTensor<half>();
        DataCopy(srcLocal, srcGlobal, srcDataSize);
        inQueueSrc.EnQue(srcLocal);
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<half> srcLocal = inQueueSrc.DeQue<half>();
        LocalTensor<half> dstLocal = outQueueDst.AllocTensor<half>();

        WholeReduceMin<half>(dstLocal, srcLocal, mask, repeat, 1, 1, 8); // 使用默认order

        outQueueDst.EnQue<half>(dstLocal);
        inQueueSrc.FreeTensor(srcLocal);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<half> dstLocal = outQueueDst.DeQue<half>();
        DataCopy(dstGlobal, dstLocal, dstDataSize);
        outQueueDst.FreeTensor(dstLocal);
    }

private:
    TPipe pipe;
    TQue<QuePosition::VECIN, 1> inQueueSrc;
    TQue<QuePosition::VECOUT, 1> outQueueDst;
    GlobalTensor<half> srcGlobal, dstGlobal;
    int srcDataSize = 1024;
    int dstDataSize = 16;
    int mask = 128;
    int repeat = 0;
};
} // namespace AscendC

extern "C" __global__ __aicore__ void reduce_kernel(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
{
    AscendC::KernelReduce op;
    op.Init(src, dstGm);
    op.Process();
}

输入数据(src_gm):
[8.94   4.773  8.53   6.047  7.883  3.824  8.04   1.233  9.125  7.484
 8.21   1.197  4.34   2.99   6.55   2.494  2.758  9.664  3.406  1.665
 2.059  3.836  8.83   2.72   5.81   9.055  2.95   5.906  2.29   1.999
 8.27   3.234  2.389  4.73   8.21   6.945  1.834  1.227  4.598  2.285
 3.504  6.48   4.984  6.125  6.21   4.035  8.375  6.89   5.258  9.43
 9.805  5.195  2.143  2.36   3.467  2.746  4.203  1.737  4.734  2.717
 3.8    1.995  4.133  4.742  5.83   7.844  2.182  5.95   6.887  9.13
 3.393  6.938  8.33   4.074  5.812  4.805  5.92   5.832  7.176  8.01
 6.64   1.468  5.61   4.49   2.736  6.766  8.5    4.164  5.1    7.58
 2.771  1.703  2.588  5.53   6.773  4.758  1.837  6.08   5.555  9.55
 6.3    8.086  7.043  7.383  9.73   7.484  6.113  7.93   9.11   2.72
 5.406  8.9    6.688  5.73   3.037  1.871  5.33   6.633  9.43   8.805
 1.092  9.2    4.16   2.543  2.072  5.297  1.922  3.06   5.883  5.996
 6.31   9.69   9.42   6.46   2.363  2.664  1.711  4.227  9.73   6.875
 4.43   3.652  7.91   5.875  2.154  8.77   3.064  7.76   5.254  2.986
 5.453  3.344  3.256  7.566  7.336  7.62   6.61   5.94   6.547  9.3
 4.418  9.21   3.518  7.53   7.766  9.37   4.125  2.275  6.355  9.07
 2.633  2.15   5.363  2.148  8.84   7.918  1.124  2.107  9.695  2.475
 3.168  4.336  3.639  6.76   7.625  5.375  4.35   9.11   2.66   3.082
 3.156  6.574  1.6875 5.285  5.984  3.71   7.324  8.7    1.902  6.883
 3.38   2.812  5.52   4.355  7.883  2.424  2.033  1.163  3.502  9.7
 4.53   4.086  1.8955 2.42   6.695  8.72   7.32   5.477  4.99   4.715
 9.78   3.45   5.73   8.73   8.38   1.751  1.987  8.41   4.984  1.489
 3.73   7.613  8.44   4.027  9.97   3.303  3.438  2.475  6.27   6.742
 3.492  7.152  9.87   3.135  3.658  2.887  6.55   7.6    9.695  1.997
 3.959  9.85   3.79   7.938  7.97   3.17   9.78   5.688  8.15   8.22
 1.746  4.633  4.06   9.71   9.695  4.     3.314  7.56   8.56   3.45
 8.52   5.39   6.332  6.883  1.269  1.232  3.148  3.582  8.33   1.179
 1.37   5.297  4.66   7.285  1.086  2.473  3.51   7.28   4.13   8.37
 2.441  5.73   7.496  5.31   8.76   2.38   7.348  7.453  2.664  2.328
 9.93   1.119  8.766  6.395  5.965  5.99   4.6    2.154  1.278  4.074
 3.883  6.617  6.05   2.447  2.256  8.63   6.348  7.816  1.547  1.743
 8.94   9.414  9.49   9.625  8.21   1.641  1.308  5.79   3.178  6.17
 4.094  4.812  6.434  1.946  5.64   7.957  7.75   1.073  8.33   7.105
 4.39   5.98   7.53   6.05   1.823  2.086  5.5    6.71   8.33   8.29
 3.584  7.684  5.766  2.354  6.78   1.824  9.97   8.51   6.58   6.43
 6.21   6.4    4.367  4.406  2.604  4.33   1.739  8.     1.828  9.14
 6.32   9.2    3.469  8.586  9.01   3.854  9.49   4.133  6.266  5.08
 2.426  7.574  1.077  3.453  3.975  9.58   8.7    8.48   8.82   8.92
 3.809  7.355  7.758  9.336  6.734  2.578  9.23   7.406  9.28   2.688
 9.82   4.816  1.821  4.99   4.26   3.223  7.277  4.25   8.3    9.734
 4.65   6.535  1.145  7.367  3.615  7.36   8.33   7.58   9.336  5.17
 6.52   9.41   9.98   2.766  9.42   6.85   2.258  5.3    6.85   1.848
 5.83   4.863  6.875  2.215  5.13   5.836  8.01   4.56   7.89   5.273
 7.51   6.938  9.42   2.69   2.434  9.586  9.375  4.48   3.656  1.709
 6.43   7.363  2.744  6.316  1.648  8.62   9.61   3.787  2.877  9.09
 3.76   1.255  9.84   2.592  1.932  5.68   1.545  5.27   5.758  2.615
 1.832  4.492  4.258  8.64   1.39   1.534  4.465  4.832  5.62   2.893
 3.928  3.438  3.84   2.105  5.355  4.402  9.54   8.98   5.723  5.91
 4.97   3.984  5.707  8.82   7.71   1.297  3.387  7.04   2.494  3.83
 6.375  7.28   2.805  3.244  4.97   3.736  5.363  9.64   3.41   6.297
 9.83   5.832  3.182  1.314  9.02   5.95   6.215  5.043  7.984  5.75
 6.29   4.297  3.11   9.11   2.44   9.42   6.27   3.5    7.652  7.043
 7.36   3.336  5.938  7.88   8.414  9.445  3.121  8.57   6.848  8.375
 4.395  3.344  7.2    7.188  3.502  1.3955 7.113  8.17   7.625  7.375
 9.43   4.996  8.82   7.47   9.01   9.914  6.05   3.867  8.87   2.713
 1.194  7.246  1.3    6.07   3.338  9.37   8.98   4.402  8.414  9.91
 4.273  5.07   6.832  8.1    5.79   4.207  7.098  6.89   4.875  8.1
 5.562  1.795  1.216  6.06   7.05   8.46   8.6    4.18   9.55   9.17
 4.832  4.348  5.11   1.57   3.262  2.871  7.586  6.89   1.491  5.07
 8.516  5.453  7.027  8.75   2.98   8.14   1.939  3.496  9.13   6.695
 9.88   6.918  8.11   2.334  3.172  2.023  5.71   5.73   8.93   7.59
 7.676  6.156  4.63   9.3    9.85   7.64   3.037  7.844  1.864  8.86
 8.95   3.492  5.094  3.98   8.734  5.7    8.83   4.83   8.77   3.256
 1.446  9.57   7.24   1.619  4.305  2.613  8.52   1.942  4.51   1.763
 7.008  2.906  3.297  2.9    6.     7.266  1.484  9.82   9.49   4.29
 5.184  9.23   5.32   4.977  8.46   5.01   8.83   8.125  6.703  5.76
 2.81   5.477  9.21   5.965  1.945  7.785  5.402  2.926  4.125  8.66
 3.064  7.67   5.617  1.917  5.652  6.71   6.016  1.414  3.623  5.543
 5.496  1.709  5.63   9.8    4.074  8.45   8.69   3.287  7.598  4.82
 9.34   6.863  3.615  9.57   6.914  1.097  5.77   3.168  4.13   8.805
 9.11   6.074  6.94   4.207  8.87   3.771  6.723  6.18   5.035  5.168
 2.54   6.5    1.165  8.27   8.34   6.55   5.48   2.916  5.227  7.355
 6.773  8.93   8.03   7.016  9.055  9.38   5.96   7.605  1.135  2.719
 5.67   8.47   8.586  1.516  5.88   2.809  3.754  5.08   4.523  4.11
 7.37   8.27   7.13   7.375  6.21   8.27   6.258  7.2    9.875  2.72
 8.836  2.295  3.596  6.4    6.664  2.426  2.326  2.234  9.13   1.09
 9.31   7.383  6.848  9.77   3.455  1.8955 6.52   7.934  3.096  2.916
 4.414  7.7    6.53   7.883  5.312  3.621  4.26   2.764  7.105  2.695
 8.88   3.555  8.23   2.025  3.723  1.196  9.31   6.984  5.156  7.996
 7.68   2.73   5.074  5.566  6.027  8.49   2.867  8.15   2.607  4.12
 8.26   2.084  5.19   2.662  2.92   6.574  9.516  4.066  3.162  4.785
 6.754  1.17   3.25   9.29   6.49   1.221  7.5    7.5    7.176  7.355
 4.605  7.17   3.082  4.1    4.17   7.3    2.621  5.188  7.848  9.62
 6.586  4.727  8.49   2.406  5.637  2.627  2.666  1.433  4.594  4.88
 4.914  3.025  8.05   9.22   9.14   7.965  9.93   5.695  1.479  4.594
 3.604  7.51   7.13   7.61   4.164  8.8    3.176  4.48   5.414  4.88
 2.848  7.9    5.734  2.412  6.234  6.13   2.422  7.     6.46   5.28
 2.537  9.26   5.508  4.15   6.965  9.984  2.588  1.44   9.27   9.48
 1.508  4.164  4.6    4.78   2.553  7.42   8.19   2.09   9.17   6.39
 5.117  4.316  2.928  1.542  6.156  5.367  7.465  3.67   2.71   8.56
 1.676  9.74   1.035  4.35   7.5    9.06   5.242  3.38   9.02   9.74
 3.441  2.215  7.453  6.547  8.77   1.679  7.656  1.884  9.86   7.883
 2.838  7.453  2.102  4.016  6.887  7.74   7.04   8.195  5.957  5.348
 6.99   5.723  3.357  7.945  6.863  5.895  8.24   1.139  4.688  7.727
 5.473  8.38   7.953  1.94   7.387  4.152  9.664  5.984  3.938  1.157
 9.37   7.023  9.26   7.47   6.973  2.006  2.646  7.94   8.695  4.49
 7.99   3.072  7.39   9.15   1.879  8.97   8.125  4.613  1.028  2.877
 9.15   2.771  9.11   2.422  2.613  5.12   1.508  5.746  2.5    3.857
 7.28   8.836  3.615  6.316  2.506  7.938  2.576  5.2    1.335  7.88
 3.838  8.8    5.723  9.836  6.35   3.557  5.08   2.344  2.633  5.46
 8.39   1.893  8.164  5.836  1.698  1.498  9.33   3.895  4.137  6.684
 7.793  2.14   9.055  3.16  ]
输出数据(dst_gm):
[1.092e+00 7.153e-06 1.124e+00 2.861e-06 1.073e+00 4.828e-06 1.145e+00
 1.669e-06 1.194e+00 2.861e-06 1.097e+00 3.874e-06 1.090e+00 5.960e-08
 1.028e+00 4.888e-06]

• 完整样例三