[oneAPI samples][Fourier correlation] Using std::sqrt(float) and std::log(float) in Fourier correlation samples

Author: raphael-egan

Libraries/oneMKL/fourier_correlation/fcorr_1d_buffers.cpp

@@ -148,8 +148,8 @@ int main(int argc, char **argv) {
   // - nrm2(input data) = norm_sig1 * norm_sig2 * N
   // - O(log(N)) ~ 2 * log(N) [arbitrary choice; implementation-dependent behavior]
   max_err_threshold +=
-    2.0f * logf(N) * std::numeric_limits<float>::epsilon()
-         * norm_sig1 * norm_sig2;
+    2.0f * std::log(static_cast<float>(N))
+         * std::numeric_limits<float>::epsilon() * norm_sig1 * norm_sig2;
   // Verify results by comparing DFT-based and naive calculations to each other,
   // and fetch optimal shift maximizing correlation (DFT-based calculation).
   auto naive_corr_acc = naive_corr.get_host_access(sycl::read_only);
@@ -184,9 +184,9 @@ int main(int argc, char **argv) {
     const float avg_sig1 = sig1.get_host_access(sycl::read_only)[0] / N;
     const float avg_sig2 = sig2.get_host_access(sycl::read_only)[0] / N;
     const float std_dev_sig1 =
-          sqrt((norm_sig1 * norm_sig1 - N * avg_sig1 * avg_sig1) / N);
+          std::sqrt((norm_sig1 * norm_sig1 - N * avg_sig1 * avg_sig1) / N);
     const float std_dev_sig2 =
-          sqrt((norm_sig2 * norm_sig2 - N * avg_sig2 * avg_sig2) / N);
+          std::sqrt((norm_sig2 * norm_sig2 - N * avg_sig2 * avg_sig2) / N);
     const float normalized_corr =
       (max_corr / N - avg_sig1 * avg_sig2) / (std_dev_sig1 * std_dev_sig2);
     std::cout << "Right-shift the second signal " << optimal_shift

Libraries/oneMKL/fourier_correlation/fcorr_1d_usm.cpp

@@ -142,8 +142,8 @@ int main(int argc, char** argv) {
   // - nrm2(input data) = norm_sig1[0] * norm_sig2[0] * N
   // - O(log(N)) ~ 2 * log(N) [arbitrary choice; implementation-dependent behavior]
   max_err_threshold +=
-    2.0f * logf(N) * std::numeric_limits<float>::epsilon()
-         * norm_sig1[0] * norm_sig2[0];
+    2.0f * std::log(static_cast<float>(N))
+         * std::numeric_limits<float>::epsilon() * norm_sig1[0] * norm_sig2[0];
   // Verify results by comparing DFT-based and naive calculations to each other,
   // and fetch optimal shift maximizing correlation (DFT-based calculation).
   float max_err = 0.0f;
@@ -176,9 +176,9 @@ int main(int argc, char** argv) {
     const float avg_sig1 = sig1[0] / N;
     const float avg_sig2 = sig2[0] / N;
     const float std_dev_sig1 =
-          sqrt((norm_sig1[0] * norm_sig1[0] - N * avg_sig1 * avg_sig1) / N);
+          std::sqrt((norm_sig1[0] * norm_sig1[0] - N * avg_sig1 * avg_sig1) / N);
     const float std_dev_sig2 =
-          sqrt((norm_sig2[0] * norm_sig2[0] - N * avg_sig2 * avg_sig2) / N);
+          std::sqrt((norm_sig2[0] * norm_sig2[0] - N * avg_sig2 * avg_sig2) / N);
     const float normalized_corr =
       (max_corr / N - avg_sig1 * avg_sig2) / (std_dev_sig1 * std_dev_sig2);
     std::cout << "Right-shift the second signal " << optimal_shift

Libraries/oneMKL/fourier_correlation/fcorr_2d_usm.cpp

@@ -67,7 +67,7 @@ calc_frobenius_norm(const float* img, sycl::queue& Q,
       sum += img[row * col_stride + col] * img[row * col_stride + col];
     });
   }).wait();
-  const float frobenius_norm = sqrtf(temp[0]);
+  const float frobenius_norm = std::sqrt(temp[0]);
   sycl::free(temp, Q);
   return frobenius_norm;
 }
@@ -234,8 +234,8 @@ int main(int argc, char **argv) {
   // - O(log(num_elem)) ~ 2 * log(num_elem)
   //   [arbitrary choice; implementation-dependent behavior]
   max_err_threshold +=
-    2.0f * logf(num_elem) * std::numeric_limits<float>::epsilon()
-         * norm_img1 * norm_img2;
+    2.0f * std::log(static_cast<float>(num_elem))
+         * std::numeric_limits<float>::epsilon() * norm_img1 * norm_img2;
   // Verify results by comparing DFT-based and naive calculations to each other,
   // and fetch optimal shift maximizing correlation (DFT-based calculation).
   float max_err = 0.0f;
@@ -272,9 +272,9 @@ int main(int argc, char **argv) {
     const float avg_sig1 = img1[0] / num_elem;
     const float avg_sig2 = img2[0] / num_elem;
     const float std_dev_sig1 =
-          sqrt((norm_img1 * norm_img1 - num_elem * avg_sig1 * avg_sig1) / num_elem);
+          std::sqrt((norm_img1 * norm_img1 - num_elem * avg_sig1 * avg_sig1) / num_elem);
     const float std_dev_sig2 =
-          sqrt((norm_img2 * norm_img2 - num_elem * avg_sig2 * avg_sig2) / num_elem);
+          std::sqrt((norm_img2 * norm_img2 - num_elem * avg_sig2 * avg_sig2) / num_elem);
     const float normalized_corr =
       (max_corr / num_elem - avg_sig1 * avg_sig2) / (std_dev_sig1 * std_dev_sig2);
     std::cout << "Shift the second signal by translation vector ("