Dataset generation

autotune/python/optimize.py

@@ -9,31 +9,45 @@ import deap.tools
 
 from genetic import GeneticOperators
 
-def exhaustive(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
-  device = context.devices[0]
-  nvalid = 0
-  current = 0
-  minT = float('inf')
-  for individual in itertools.product(*all_parameters):
-    template = build_template(TemplateType.Parameters(*individual))
-    if not tools.skip(template, statement, device):
-      nvalid = nvalid + 1
-  for individual in itertools.product(*all_parameters):
-    template = build_template(TemplateType.Parameters(*individual))
-    try:
-      T = tools.benchmark(template,statement,device)
-      current = current + 1
-      if T < minT:
-        minT = T
-        best = individual
-      sys.stdout.write('%d / %d , Best is %d %s for %s\r'%(current, nvalid, compute_perf(minT), perf_metric, best))
-      sys.stdout.flush()
-    except:
-      pass
-  sys.stdout.write('\n')
-  sys.stdout.flush()
-    
+#~ def parameter_space(operation):
+  #~ simd = [1, 2, 4, 8]
+  #~ pow2_1D = [2**k for k in range(12)]
+  #~ pow2_2D = [2**i for i in range(8)]
+  #~ pow2_2D_unrolled = [2**i for i in range(8)]
+  #~ FetchingPolicy = vcl.atidlas.FetchingPolicy
+  #~ fetch = [FetchingPolicy.FETCH_FROM_LOCAL, FetchingPolicy.FETCH_FROM_GLOBAL_CONTIGUOUS, FetchingPolicy.FETCH_FROM_GLOBAL_STRIDED]
+  #~ if operation == 'vector-axpy': return [simd, pow2_1D, pow2_1D, fetch]
+  #~ if operation == 'reduction': return [simd, pow2_1D, pow2_1D, fetch]
+  #~ if operation == 'matrix-axpy': return [simd, pow2_2D, pow2_2D, pow2_2D, pow2_2D, fetch]
+  #~ if operation == 'row-wise-reduction': return [simd, pow2_2D, pow2_2D, pow2_1D, fetch]
+  #~ if operation == 'matrix-product': return [simd, pow2_2D, pow2_2D, pow2_2D, pow2_2D_unrolled,  pow2_2D_unrolled,  pow2_2D_unrolled, fetch, fetch, [0] + pow2_2D, [0] + pow2_2D]
+  #~ 
   
-def genetic(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
-  GA = GeneticOperators(context.devices[0], statement, all_parameters, parameter_names, TemplateType, build_template)
-  GA.optimize(maxtime='5m0s', maxgen=1000, compute_perf=compute_perf, perf_metric=perf_metric)
+#~ def exhaustive(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
+  #~ device = context.devices[0]
+  #~ nvalid = 0
+  #~ current = 0
+  #~ minT = float('inf')
+  #~ for individual in itertools.product(*all_parameters):
+    #~ template = build_template(TemplateType.Parameters(*individual))
+    #~ if not tools.skip(template, statement, device):
+      #~ nvalid = nvalid + 1
+  #~ for individual in itertools.product(*all_parameters):
+    #~ template = build_template(TemplateType.Parameters(*individual))
+    #~ try:
+      #~ T = tools.benchmark(template,statement,device)
+      #~ current = current + 1
+      #~ if T < minT:
+        #~ minT = T
+        #~ best = individual
+      #~ sys.stdout.write('%d / %d , Best is %d %s for %s\r'%(current, nvalid, compute_perf(minT), perf_metric, best))
+      #~ sys.stdout.flush()
+    #~ except:
+      #~ pass
+  #~ sys.stdout.write('\n')
+  #~ sys.stdout.flush()
+    #~ 
+  
+def genetic(statement, context, TemplateType, build_template, parameter_names, compute_perf, perf_metric, out):
+  GA = GeneticOperators(context.devices[0], statement, parameter_names, TemplateType, build_template, out)
+  GA.optimize(maxtime='2m30s', maxgen=1000, compute_perf=compute_perf, perf_metric=perf_metric)