caffe - understanding SoftmaxLayer::Backward_cpu function

I'm new to caffe and trying to understand the implementation of softmax layer backward function

template <typename Dtype>
void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {
  const Dtype* top_diff = top[0]->cpu_diff();
  const Dtype* top_data = top[0]->cpu_data();
  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
  Dtype* scale_data = scale_.mutable_cpu_data();
  int channels = top[0]->shape(softmax_axis_);
  int dim = top[0]->count() / outer_num_;
  caffe_copy(top[0]->count(), top_diff, bottom_diff);
  for (int i = 0; i < outer_num_; ++i) {
    // compute dot(top_diff, top_data) and subtract them from the bottom diff
    for (int k = 0; k < inner_num_; ++k) {
      scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,
          bottom_diff + i * dim + k, inner_num_,
          top_data + i * dim + k, inner_num_);
    }
    // subtraction
    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, inner_num_, 1,
        -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);
  }
  // elementwise multiplication
  caffe_mul(top[0]->count(), bottom_diff, top_data, bottom_diff);
}

(the full file can be found here: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_layer.cpp)

I have a few questions:

1. What is the data in bottom_diff?
2. If I understand correctly, top is where the output goes so what is the data in top_data when calling caffe_cpu_strided_dot?
3. What is substructed in caffe_cpu_gemm?

Thanks!