examples use store output file functions

Author: benvanwerkhoven

examples/cuda/vector_add.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python
 """This is the minimal example from the README"""
 
-import json
 import numpy
 from kernel_tuner import tune_kernel
+from kernel_tuner.file_utils import store_output_file, store_metadata_file
 
 def tune():
 
@@ -28,12 +28,15 @@ def tune():
     tune_params = dict()
     tune_params["block_size_x"] = [128+64*i for i in range(15)]
 
-    result = tune_kernel("vector_add", kernel_string, size, args, tune_params)
+    results, env = tune_kernel("vector_add", kernel_string, size, args, tune_params)
 
-    with open("vector_add.json", 'w') as fp:
-        json.dump(result, fp)
+    # Store the tuning results in an output file
+    store_output_file("vector_add.json", results, tune_params)
 
-    return result
+    # Store the metadata of this run
+    store_metadata_file("vector_add-metadata.json")
+
+    return results
 
 
 if __name__ == "__main__":

examples/opencl/vector_add.py

@@ -1,8 +1,13 @@
 #!/usr/bin/env python
+"""This is the minimal example from the README"""
+
 import numpy
 from kernel_tuner import tune_kernel
+from kernel_tuner.file_utils import store_output_file, store_metadata_file
+
+def tune():
 
-kernel_string = """
+    kernel_string = """
 __kernel void vector_add(__global float *c, __global const float *a, __global const float *b, int n) {
     int i = get_global_id(0);
     if (i<n) {
@@ -11,17 +16,28 @@
 }
 """
 
-size = 10000000
+    size = 10000000
+
+    a = numpy.random.rand(size).astype(numpy.float32)
+    b = numpy.random.rand(size).astype(numpy.float32)
+    c = numpy.zeros_like(a)
+    n = numpy.int32(size)
+
+    args = [c, a, b, n]
+
+    tune_params = dict()
+    tune_params["block_size_x"] = [128+64*i for i in range(15)]
+
+    results, env = tune_kernel("vector_add", kernel_string, size, args, tune_params)
 
-a = numpy.random.rand(size).astype(numpy.float32)
-b = numpy.random.rand(size).astype(numpy.float32)
-c = numpy.zeros_like(a)
-n = numpy.int32(size)
+    # Store the tuning results in an output file
+    store_output_file("vector_add.json", results, tune_params)
 
-args = [c, a, b, n]
+    # Store the metadata of this run
+    store_metadata_file("vector_add-metadata.json")
 
-tune_params = dict()
-tune_params["block_size_x"] = [128+64*i for i in range(15)]
+    return results
 
-tune_kernel("vector_add", kernel_string, size, args, tune_params)
 
+if __name__ == "__main__":
+    tune()