我一直在为某个特定应用程序的矢量化而苦苦挣扎,而且我已经尝试了一切。从自动向量化到手动编码的SSE内在函数。但不知何故,我无法在基于模板的应用程序上获得加速。
以下是我当前代码的片段,我使用SSE内在函数进行了矢量化。当我使用-vec-report3编译(Intel icc)时,我不断获得此消息:
备注:循环未向量化:语句无法向量化。
#pragma ivdep
for ( i = STENCIL; i < z - STENCIL; i+=4 )
{
it = it2 + i;
__m128 tmp2i = _mm_mul_ps(_mm_add_ps(_mm_load_ps(&p2[i+j*it_j-it_j4+k*it_k]),_mm_load_ps(&p2[i+j*it_j+it_j4+k*it_k])),X4_i); //loop was not vectorized: statement cannot be vectorized
__m128 tmp3 = _mm_mul_ps(_mm_add_ps(_mm_load_ps(&p2[i+j*it_j-it_j3+k*it_k]),_mm_load_ps(&p2[i+j*it_j+it_j3+k*it_k])),X3_i);
__m128 tmp4 = _mm_mul_ps(_mm_add_ps(_mm_load_ps(&p2[i+j*it_j-it_j2+k*it_k]),_mm_load_ps(&p2[i+j*it_j+it_j2+k*it_k])),X2_i);
__m128 tmp5 = _mm_mul_ps(_mm_add_ps(_mm_load_ps(&p2[i+j*it_j-it_j +k*it_k]),_mm_load_ps(&p2[i+j*it_j+it_j +k*it_k])),X1_i);
__m128 tmp6 = _mm_add_ps(_mm_add_ps(_mm_add_ps(tmp2i,tmp3),_mm_add_ps(tmp4,tmp5)), _mm_mul_ps(_mm_load_ps(&p2[it]),C00_i));
_mm_store_ps(&tmp2[i],tmp6);
}
我错过了一些关键的东西吗?由于该消息没有说明为什么它不能被矢量化,我发现很难确定瓶颈。
更新 在仔细考虑了这些建议后,我按照以下方式调整了代码。我认为最好将其进一步分解,以确定实际上导致向量依赖的语句。
//#pragma ivdep
for ( i = STENCIL; i < z - STENCIL; i+=4 )
{
it = it2 + i;
__m128 center = _mm_mul_ps(_mm_load_ps(&p2[it]),C00_i);
u_j4 = _mm_load_ps(&p2[i+j*it_j-it_j4+k*it_k]); //Line 180
u_j3 = _mm_load_ps(&p2[i+j*it_j-it_j3+k*it_k]);
u_j2 = _mm_load_ps(&p2[i+j*it_j-it_j2+k*it_k]);
u_j1 = _mm_load_ps(&p2[i+j*it_j-it_j +k*it_k]);
u_j8 = _mm_load_ps(&p2[i+j*it_j+it_j4+k*it_k]);
u_j7 = _mm_load_ps(&p2[i+j*it_j+it_j3+k*it_k]);
u_j6 = _mm_load_ps(&p2[i+j*it_j+it_j2+k*it_k]);
u_j5 = _mm_load_ps(&p2[i+j*it_j+it_j +k*it_k]);
__m128 tmp2i = _mm_mul_ps(_mm_add_ps(u_j4,u_j8),X4_i);
__m128 tmp3 = _mm_mul_ps(_mm_add_ps(u_j3,u_j7),X3_i);
__m128 tmp4 = _mm_mul_ps(_mm_add_ps(u_j2,u_j6),X2_i);
__m128 tmp5 = _mm_mul_ps(_mm_add_ps(u_j1,u_j5),X1_i);
__m128 tmp6 = _mm_add_ps(_mm_add_ps(tmp2i,tmp3),_mm_add_ps(tmp4,tmp5));
__m128 tmp7 = _mm_add_ps(tmp6,center);
_mm_store_ps(&tmp2[i],tmp7); //Line 196
}
当我在没有#pragma ivdep
的情况下编译(icc)上面的代码时,我得到以下消息:
remark: loop was not vectorized: existence of vector dependence.
vector dependence: assumed FLOW dependence between tmp2 line 196 and tmp2 line 196.
vector dependence: assumed ANTI dependence between tmp2 line 196 and tmp2 line 196.
当我使用#pragma ivdep
编译(icc)时,我收到以下消息:
remark: loop was not vectorized: unsupported data type. //Line 180
为什么第196行会建议依赖?如何消除建议的向量依赖性?
答案 0 :(得分:2)
问题在于您尝试将自动矢量化与手动矢量化代码结合使用。编译器说该行不能进行矢量化,因为你无法对矢量函数进行矢量化。
让编译器自动向量化它,或禁用自动向量化并手动向量化代码。正如已经评论过的那样,自动矢量化器将计算矢量化盈利能力:它会检查是否值得对您的代码进行矢量化。