Merge pull request #241 from MiloLurati/parallelTuning

Parallel Tuning

Author: isazi

doc/source/optimization.rst

@@ -25,6 +25,7 @@ the ``strategy=`` optional argument of ``tune_kernel()``. Kernel Tuner currently
  * "pso" particle swarm optimization
  * "random_sample" takes a random sample of the search space
  * "simulated_annealing" simulated annealing strategy
+ * "ensemble" ensemble strategy
 
 Most strategies have some mechanism built in to detect when to stop tuning, which may be controlled through specific 
 parameters that can be passed to the strategies using the ``strategy_options=`` optional argument of ``tune_kernel()``. You 

kernel_tuner/interface.py

@@ -34,6 +34,7 @@
 from kernel_tuner.integration import get_objective_defaults
 from kernel_tuner.runners.sequential import SequentialRunner
 from kernel_tuner.runners.simulation import SimulationRunner
+from kernel_tuner.runners.parallel import ParallelRunner
 from kernel_tuner.searchspace import Searchspace
 
 try:
@@ -57,6 +58,7 @@
     pso,
     random_sample,
     simulated_annealing,
+    ensemble
 )
 
 strategy_map = {
@@ -75,6 +77,7 @@
     "simulated_annealing": simulated_annealing,
     "firefly_algorithm": firefly_algorithm,
     "bayes_opt": bayes_opt,
+    "ensemble": ensemble,
 }
 
 
@@ -384,6 +387,7 @@ def __deepcopy__(self, _):
             * "pso" particle swarm optimization
             * "random_sample" takes a random sample of the search space
             * "simulated_annealing" simulated annealing strategy
+            * "ensemble" Ensemble Strategy
 
         Strategy-specific parameters and options are explained under strategy_options.
 
@@ -463,6 +467,7 @@ def __deepcopy__(self, _):
         ),
         ("metrics", ("specifies user-defined metrics, please see :ref:`metrics`.", "dict")),
         ("simulation_mode", ("Simulate an auto-tuning search from an existing cachefile", "bool")),
+        ("parallel_mode", ("Run the auto-tuning on multiple devices (brute-force execution)", "bool")),
         ("observers", ("""A list of Observers to use during tuning, please see :ref:`observers`.""", "list")),
     ]
 )
@@ -574,6 +579,7 @@ def tune_kernel(
     cache=None,
     metrics=None,
     simulation_mode=False,
+    parallel_mode=False,
     observers=None,
     objective=None,
     objective_higher_is_better=None,
@@ -611,6 +617,8 @@ def tune_kernel(
         tuning_options["max_fevals"] = strategy_options["max_fevals"]
     if strategy_options and "time_limit" in strategy_options:
         tuning_options["time_limit"] = strategy_options["time_limit"]
+    if strategy_options and "num_gpus" in strategy_options:
+        tuning_options["num_gpus"] = strategy_options["num_gpus"]
 
     logging.debug("tune_kernel called")
     logging.debug("kernel_options: %s", util.get_config_string(kernel_options))
@@ -650,9 +658,13 @@ def tune_kernel(
         strategy = brute_force
 
     # select the runner for this job based on input
-    selected_runner = SimulationRunner if simulation_mode else SequentialRunner
+    selected_runner = SimulationRunner if simulation_mode else (ParallelRunner if parallel_mode else SequentialRunner)
     tuning_options.simulated_time = 0
-    runner = selected_runner(kernelsource, kernel_options, device_options, iterations, observers)
+    if parallel_mode:
+         num_gpus = tuning_options['num_gpus'] if 'num_gpus' in tuning_options else None
+         runner = selected_runner(kernelsource, kernel_options, device_options, iterations, observers, num_gpus=num_gpus)
+    else:
+        runner = selected_runner(kernelsource, kernel_options, device_options, iterations, observers)
 
     # the user-specified function may or may not have an optional atol argument;
     # we normalize it so that it always accepts atol.

kernel_tuner/observers/nvml.py

@@ -326,6 +326,15 @@ def __init__(
         continuous_duration=1,
     ):
         """Create an NVMLObserver."""
+        # needed for re-initializing observer on ray actor
+        self.init_arguments = {
+            "observables": observables,
+            "device": device,
+            "save_all": save_all,
+            "nvidia_smi_fallback": nvidia_smi_fallback,
+            "use_locked_clocks": use_locked_clocks,
+            "continous_duration": continous_duration
+        }
         if nvidia_smi_fallback:
             self.nvml = nvml(
                 device,

kernel_tuner/observers/pmt.py

@@ -49,6 +49,11 @@ class PMTObserver(BenchmarkObserver):
     def __init__(self, observable=None, use_continuous_observer=False, continuous_duration=1):
         if not pmt:
             raise ImportError("could not import pmt")
+        
+        # needed for re-initializing observer on ray actor
+        self.init_arguments = {
+            "observable": observable
+        }
 
         # User specifices a dictonary of platforms and corresponding device
         if type(observable) is dict:

kernel_tuner/observers/powersensor.py

@@ -27,6 +27,12 @@ class PowerSensorObserver(BenchmarkObserver):
     def __init__(self, observables=None, device=None):
         if not powersensor:
             raise ImportError("could not import powersensor")
+        
+        # needed for re-initializing observer on ray actor
+        self.init_arguments = {
+            "observables": observables,
+            "device": device
+        }
 
         supported = ["ps_energy", "ps_power"]
         for obs in observables: