Updated the OpenMP matrix multiply.

examples/directives/matrix_multiply_c_openmp.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """This is an example tuning a naive matrix multiplication using the simplified directives interface"""
 
-from kernel_tuner import tune_kernel
+from kernel_tuner import tune_kernel, run_kernel
 from kernel_tuner.utils.directives import Code, OpenMP, Cxx, process_directives
 
 N = 4096
@@ -13,11 +13,11 @@
     #pragma tuner start mm A(float*:NN) B(float*:NN) C(float*:NN)
     float temp_sum = 0.0f;
     #pragma omp target
-    #pragma omp teams collapse(2)
+    #pragma omp teams distribute collapse(2)
     for ( int i = 0; i < N; i++) {
         for ( int j = 0; j < N; j++ ) {
             temp_sum = 0.0f;
-            #pragma omp distribute parallel for num_threads(nthreads) reduction(+:temp_sum)
+            #pragma omp parallel for num_threads(nthreads) reduction(+:temp_sum)
             for ( int k = 0; k < N; k++ ) {
                 temp_sum += A[(i * N) + k] * B[(k * N) + j];
             }
@@ -40,13 +40,20 @@
 metrics["GB/s"] = lambda x: ((N**3 * 2 * 4) + (N**2 * 4)) / x["time_s"] / 10**9
 metrics["GFLOP/s"] = lambda x: (N**3 * 3) / x["time_s"] / 10**9
 
+# compute reference solution from CPU
+results = run_kernel(
+    "mm", kernel_string["mm"], 0, kernel_args["mm"], {"nthreads": 1}, compiler="nvc++", compiler_options=["-fast"]
+)
+answer = [None, None, results[2]]
+
 tune_kernel(
     "mm",
     kernel_string["mm"],
     0,
     kernel_args["mm"],
     tune_params,
     metrics=metrics,
-    compiler_options=["-fast", "-mp=gpu"],
+    answer=answer,
     compiler="nvc++",
+    compiler_options=["-fast", "-mp=gpu"],
 )