diff --git a/examples/cuda/sepconv_parallel.py b/examples/cuda/sepconv_parallel.py
new file mode 100644
index 00000000..074200e1
--- /dev/null
+++ b/examples/cuda/sepconv_parallel.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+import numpy
+from kernel_tuner import tune_kernel
+from collections import OrderedDict
+
+
+def tune():
+    with open("convolution.cu", "r") as f:
+        kernel_string = f.read()
+
+    # setup tunable parameters
+    tune_params = OrderedDict()
+    tune_params["filter_height"] = [i for i in range(3, 19, 2)]
+    tune_params["filter_width"] = [i for i in range(3, 19, 2)]
+    tune_params["block_size_x"] = [16 * i for i in range(1, 65)]
+    tune_params["block_size_y"] = [2**i for i in range(6)]
+    tune_params["tile_size_x"] = [i for i in range(1, 11)]
+    tune_params["tile_size_y"] = [i for i in range(1, 11)]
+
+    tune_params["use_padding"] = [0, 1]  # toggle the insertion of padding in shared memory
+    tune_params["read_only"] = [0, 1]  # toggle using the read-only cache
+
+    # limit the search to only use padding when its effective, and at least 32 threads in a block
+    restrict = ["use_padding==0 or (block_size_x % 32 != 0)", "block_size_x*block_size_y >= 32"]
+
+    # setup input and output dimensions
+    problem_size = (4096, 4096)
+    size = numpy.prod(problem_size)
+    largest_fh = max(tune_params["filter_height"])
+    largest_fw = max(tune_params["filter_width"])
+    input_size = (problem_size[0] + largest_fw - 1) * (problem_size[1] + largest_fh - 1)
+
+    # create input data
+    output_image = numpy.zeros(size).astype(numpy.float32)
+    input_image = numpy.random.randn(input_size).astype(numpy.float32)
+    filter_weights = numpy.random.randn(largest_fh * largest_fw).astype(numpy.float32)
+
+    # setup kernel arguments
+    cmem_args = {"d_filter": filter_weights}
+    args = [output_image, input_image, filter_weights]
+
+    # tell the Kernel Tuner how to compute grid dimensions
+    grid_div_x = ["block_size_x", "tile_size_x"]
+    grid_div_y = ["block_size_y", "tile_size_y"]
+
+    # start tuning separable convolution (row)
+    tune_params["filter_height"] = [1]
+    tune_params["tile_size_y"] = [1]
+    results_row = tune_kernel(
+        "convolution_kernel",
+        kernel_string,
+        problem_size,
+        args,
+        tune_params,
+        grid_div_y=grid_div_y,
+        grid_div_x=grid_div_x,
+        cmem_args=cmem_args,
+        verbose=False,
+        restrictions=restrict,
+        parallel_runner=1024,
+        cache="convolution_kernel_row",
+    )
+
+    # start tuning separable convolution (col)
+    tune_params["filter_height"] = tune_params["filter_width"][:]
+    tune_params["file_size_y"] = tune_params["tile_size_x"][:]
+    tune_params["filter_width"] = [1]
+    tune_params["tile_size_x"] = [1]
+    results_col = tune_kernel(
+        "convolution_kernel",
+        kernel_string,
+        problem_size,
+        args,
+        tune_params,
+        grid_div_y=grid_div_y,
+        grid_div_x=grid_div_x,
+        cmem_args=cmem_args,
+        verbose=False,
+        restrictions=restrict,
+        parallel_runner=1024,
+        cache="convolution_kernel_col",
+    )
+
+    return results_row, results_col
+
+
+if __name__ == "__main__":
+    results_row, results_col = tune()
diff --git a/examples/cuda/vector_add_parallel.py b/examples/cuda/vector_add_parallel.py
new file mode 100644
index 00000000..8d35ce7c
--- /dev/null
+++ b/examples/cuda/vector_add_parallel.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+
+import numpy
+from kernel_tuner import tune_kernel
+
+
+def tune():
+    kernel_string = """
+    __global__ void vector_add(float *c, float *a, float *b, int n) {
+        int i = (blockIdx.x * block_size_x) + threadIdx.x;
+        if ( i < n ) {
+            c[i] = a[i] + b[i];
+        }
+    }
+    """
+
+    size = 10000000
+
+    a = numpy.random.randn(size).astype(numpy.float32)
+    b = numpy.random.randn(size).astype(numpy.float32)
+    c = numpy.zeros_like(b)
+    n = numpy.int32(size)
+
+    args = [c, a, b, n]
+
+    tune_params = dict()
+    tune_params["block_size_x"] = [32 * i for i in range(1, 33)]
+
+    results, env = tune_kernel("vector_add", kernel_string, size, args, tune_params, parallel_workers=True)
+    print(env)
+    return results
+
+
+if __name__ == "__main__":
+    tune()
diff --git a/kernel_tuner/interface.py b/kernel_tuner/interface.py
index 32e91c86..053f71f2 100644
--- a/kernel_tuner/interface.py
+++ b/kernel_tuner/interface.py
@@ -39,8 +39,6 @@
 import kernel_tuner.util as util
 from kernel_tuner.file_utils import get_input_file, get_t4_metadata, get_t4_results, import_class_from_file
 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.searchspace import Searchspace
 
 try:
@@ -473,6 +471,7 @@ 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_workers", ("Set to `True` or an integer to enable parallel tuning. If set to an integer, this will be the number of parallel workers.", "int|bool")),
         ("observers", ("""A list of Observers to use during tuning, please see :ref:`observers`.""", "list")),
     ]
 )
@@ -584,6 +583,7 @@ def tune_kernel(
     cache=None,
     metrics=None,
     simulation_mode=False,
+    parallel_workers=None,
     observers=None,
     objective=None,
     objective_higher_is_better=None,
@@ -651,9 +651,22 @@ def tune_kernel(
         strategy = brute_force
 
     # select the runner for this job based on input
-    selected_runner = SimulationRunner if simulation_mode else SequentialRunner
+    # TODO: we could use the "match case" syntax when removing support for 3.9
     tuning_options.simulated_time = 0
-    runner = selected_runner(kernelsource, kernel_options, device_options, iterations, observers)
+
+    if parallel_workers and simulation_mode:
+        raise ValueError("Enabling `parallel_workers` and `simulation_mode` together is not supported")
+    elif simulation_mode:
+        from kernel_tuner.runners.simulation import SimulationRunner
+        runner = SimulationRunner(kernelsource, kernel_options, device_options, iterations, observers)
+    elif parallel_workers:
+        from kernel_tuner.runners.parallel import ParallelRunner
+        num_workers = None if parallel_workers is True else parallel_workers
+        runner = ParallelRunner(kernelsource, kernel_options, device_options, tuning_options, iterations, observers, num_workers=num_workers)
+    else:
+        from kernel_tuner.runners.sequential import SequentialRunner
+        runner = SequentialRunner(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.
@@ -669,16 +682,20 @@ def preprocess_cache(filepath):
     # process cache
     if cache:
         cache = preprocess_cache(cache)
-        util.process_cache(cache, kernel_options, tuning_options, runner)
+        tuning_options.cachefile = cache
+        tuning_options.cache = util.process_cache(cache, kernel_options, tuning_options, runner)
     else:
-        tuning_options.cache = {}
         tuning_options.cachefile = None
+        tuning_options.cache = {}
 
     # create search space
     tuning_options.restrictions_unmodified = deepcopy(restrictions)
-    searchspace = Searchspace(tune_params, restrictions, runner.dev.max_threads, **searchspace_construction_options)
+    device_info = runner.get_device_info()
+    searchspace = Searchspace(tune_params, restrictions, device_info.max_threads, **searchspace_construction_options)
+    
     restrictions = searchspace._modified_restrictions
     tuning_options.restrictions = restrictions
+
     if verbose:
         print(f"Searchspace has {searchspace.size} configurations after restrictions.")
 
@@ -696,6 +713,9 @@ def preprocess_cache(filepath):
     results = strategy.tune(searchspace, runner, tuning_options)
     env = runner.get_environment(tuning_options)
 
+    # Shut down the runner
+    runner.shutdown()
+
     # finished iterating over search space
     if results:  # checks if results is not empty
         best_config = util.get_best_config(results, objective, objective_higher_is_better)
diff --git a/kernel_tuner/runners/parallel.py b/kernel_tuner/runners/parallel.py
new file mode 100644
index 00000000..a05fc2fa
--- /dev/null
+++ b/kernel_tuner/runners/parallel.py
@@ -0,0 +1,260 @@
+"""A specialized runner that tunes in parallel the parameter space."""
+import logging
+import socket
+from time import perf_counter
+from kernel_tuner.core import DeviceInterface
+from kernel_tuner.interface import Options
+from kernel_tuner.runners.runner import Runner
+from kernel_tuner.util import ErrorConfig, print_config_output, process_metrics, store_cache
+from datetime import datetime, timezone
+
+logger = logging.getLogger(__name__)
+
+try:
+    import ray
+except ImportError as e:
+    raise ImportError(f"unable to initialize the parallel runner: {e}") from e
+
+
+@ray.remote(num_gpus=1)
+class DeviceActor:
+    def __init__(self, kernel_source, kernel_options, device_options, tuning_options, iterations, observers):
+        # detect language and create high-level device interface
+        self.dev = DeviceInterface(kernel_source, iterations=iterations, observers=observers, **device_options)
+
+        self.units = self.dev.units
+        self.quiet = device_options.quiet
+        self.kernel_source = kernel_source
+        self.warmed_up = False if self.dev.requires_warmup else True
+        self.start_time = perf_counter()
+        self.last_strategy_start_time = self.start_time
+        self.last_strategy_time = 0
+        self.kernel_options = kernel_options
+        self.tuning_options = tuning_options
+
+        # move data to the GPU
+        self.gpu_args = self.dev.ready_argument_list(kernel_options.arguments)
+
+    def shutdown(self):
+        ray.actor.exit_actor()
+
+    def get_environment(self):
+        # Get the device properties
+        env = dict(self.dev.get_environment())
+
+        # Get the host name
+        env["host_name"] = socket.gethostname()
+
+        # Get info about the ray instance
+        ctx = ray.get_runtime_context()
+        env["ray"] = {
+            "node_id": ctx.get_node_id(),
+            "worker_id": ctx.get_worker_id(),
+            "actor_id": ctx.get_actor_id(),
+        }
+
+        return env
+
+    def run(self, element):
+        # TODO: logging.debug("sequential runner started for " + self.kernel_options.kernel_name)
+        objective = self.tuning_options.objective
+        metrics = self.tuning_options.metrics
+
+        params = dict(element)
+        result = None
+        warmup_time = 0
+
+        # attempt to warmup the GPU by running the first config in the parameter space and ignoring the result
+        if not self.warmed_up:
+            warmup_time = perf_counter()
+            self.dev.compile_and_benchmark(
+                self.kernel_source, self.gpu_args, params, self.kernel_options, self.tuning_options
+            )
+            self.warmed_up = True
+            warmup_time = 1e3 * (perf_counter() - warmup_time)
+
+        result = self.dev.compile_and_benchmark(
+            self.kernel_source, self.gpu_args, params, self.kernel_options, self.tuning_options
+        )
+
+        if isinstance(result.get(objective), ErrorConfig):
+            logging.debug("kernel configuration was skipped silently due to compile or runtime failure")
+
+        params.update(result)
+
+        # only compute metrics on configs that have not errored
+        if metrics and not isinstance(params.get(objective), ErrorConfig):
+            params = process_metrics(params, metrics)
+
+        # get the framework time by estimating based on other times
+        total_time = 1000 * ((perf_counter() - self.start_time) - warmup_time)
+        params["strategy_time"] = self.last_strategy_time
+        params["framework_time"] = max(
+            total_time
+            - (
+                params["compile_time"]
+                + params["verification_time"]
+                + params["benchmark_time"]
+                + params["strategy_time"]
+            ),
+            0,
+        )
+
+        params["timestamp"] = datetime.now(timezone.utc).isoformat()
+        params["ray_actor_id"] = ray.get_runtime_context().get_actor_id()
+        params["host_name"] = socket.gethostname()
+
+        self.start_time = perf_counter()
+
+        # all visited configurations are added to results to provide a trace for optimization strategies
+        return params
+
+
+class DeviceActorState:
+    def __init__(self, actor):
+        self.actor = actor
+        self.running_jobs = []
+        self.maximum_running_jobs = 1
+        self.is_running = True
+        self.env = ray.get(actor.get_environment.remote())
+
+    def __repr__(self):
+        actor_id = self.env["ray"]["actor_id"]
+        host_name = self.env["host_name"]
+        return f"{actor_id} ({host_name})"
+
+    def shutdown(self):
+        if not self.is_running:
+            return
+
+        self.is_running = False
+
+        try:
+            self.actor.shutdown.remote()
+        except Exception:
+            logger.exception("Failed to request actor shutdown: %s", self)
+
+    def submit(self, config):
+        logger.info(f"jobs submitted to worker {self}: {config}")
+        job = self.actor.run.remote(config)
+        self.running_jobs.append(job)
+        return job
+    
+    def is_available(self):
+        if not self.is_running:
+            return False
+
+        # Check for ready jobs, but do not block
+        ready_jobs, self.running_jobs = ray.wait(self.running_jobs, timeout=0)
+        ray.get(ready_jobs)
+
+        # Available if this actor can now run another job
+        return len(self.running_jobs) < self.maximum_running_jobs
+
+
+class ParallelRunner(Runner):
+    def __init__(self, kernel_source, kernel_options, device_options, tuning_options, iterations, observers, num_workers=None):
+        if not ray.is_initialized():
+            ray.init()
+
+        if num_workers is None:
+            num_workers = int(ray.cluster_resources().get("GPU", 0))
+
+        if num_workers == 0:
+            raise RuntimeError("failed to initialize parallel runner: no GPUs found")
+
+        if num_workers < 1:
+            raise RuntimeError(f"failed to initialize parallel runner: invalid number of GPUs specified: {num_workers}")
+
+        self.workers = []
+
+        try:
+            for index in range(num_workers):
+                actor = DeviceActor.remote(kernel_source, kernel_options, device_options, tuning_options, iterations, observers)
+                worker = DeviceActorState(actor)
+                self.workers.append(worker)
+
+                logger.info(f"launched worker {index}: {worker}")
+        except:
+            # If an exception occurs, shut down the worker
+            self.shutdown()
+            raise
+
+        # Check if all workers have the same device
+        device_names = {w.env.get("device_name") for w in self.workers}
+        if len(device_names) != 1:
+            self.shutdown()
+            raise RuntimeError(
+                f"failed to initialize parallel runner: workers have different devices: {sorted(device_names)}"
+            )
+
+        self.device_name = device_names.pop()
+
+        # TODO: Get this from the device
+        self.units = {"time": "ms"}
+        self.quiet = device_options.quiet
+
+    def get_device_info(self):
+        return Options({"max_threads": 1024})
+
+    def get_environment(self, tuning_options):
+        return {
+            "device_name": self.device_name, 
+            "workers": [w.env for w in self.workers]
+        }
+
+    def shutdown(self):
+        for worker in self.workers:
+            try:
+                worker.shutdown()
+            except Exception as err:
+                logger.exception("error while shutting down worker {worker}")
+
+    def submit_job(self, *args):
+        while True:
+            # Round-robin: first available worker gets the job and goes to the back of the list
+            for i, worker in enumerate(list(self.workers)):
+                if worker.is_available():
+                    self.workers.pop(i)
+                    self.workers.append(worker)
+                    return worker.submit(*args)
+
+            # Gather all running jobs
+            running_jobs = [job for w in self.workers for job in w.running_jobs]
+
+            # If there are no running jobs, then something must be wrong.
+            # Maybe a worker has crashed or gotten into an invalid state.
+            if not running_jobs:
+                raise RuntimeError("invalid state: no Ray workers are available to run job")
+
+            # Wait until any running job completes
+            ray.wait(running_jobs, num_returns=1)
+
+    def run(self, parameter_space, tuning_options):
+        running_jobs = dict()
+        completed_jobs = dict()
+
+        # Submit jobs which are not in the cache
+        for config in parameter_space:
+            params = dict(zip(tuning_options.tune_params.keys(), config))
+            key = ",".join([str(i) for i in config])
+
+            if key in tuning_options.cache:
+                completed_jobs[key] = tuning_options.cache[key]
+            else:
+                assert key not in running_jobs
+                running_jobs[key] = self.submit_job(params)
+                completed_jobs[key] = None
+
+        # Wait for the running jobs to finish
+        for key, job in running_jobs.items():
+            result = ray.get(job)
+            completed_jobs[key] = result
+
+            # print configuration to the console
+            print_config_output(tuning_options.tune_params, result, self.quiet, tuning_options.metrics, self.units)
+
+            # add configuration to cache
+            store_cache(key, result, tuning_options.cachefile, tuning_options.cache)
+
+        return list(completed_jobs.values())
diff --git a/kernel_tuner/runners/runner.py b/kernel_tuner/runners/runner.py
index 80ab3214..3a886ad1 100644
--- a/kernel_tuner/runners/runner.py
+++ b/kernel_tuner/runners/runner.py
@@ -13,8 +13,15 @@ def __init__(
     ):
         pass
 
+    def shutdown(self):
+        pass
+
+    @abstractmethod
+    def get_device_info(self):
+        pass
+
     @abstractmethod
-    def get_environment(self):
+    def get_environment(self, tuning_options):
         pass
 
     @abstractmethod
diff --git a/kernel_tuner/runners/sequential.py b/kernel_tuner/runners/sequential.py
index 5e53093b..2bd554bf 100644
--- a/kernel_tuner/runners/sequential.py
+++ b/kernel_tuner/runners/sequential.py
@@ -20,15 +20,13 @@ def __init__(self, kernel_source, kernel_options, device_options, iterations, ob
         :param kernel_options: A dictionary with all options for the kernel.
         :type kernel_options: kernel_tuner.interface.Options
 
-        :param device_options: A dictionary with all options for the device
-            on which the kernel should be tuned.
+        :param device_options: A dictionary with all options for the device on which the kernel should be tuned.
         :type device_options: kernel_tuner.interface.Options
 
-        :param iterations: The number of iterations used for benchmarking
-            each kernel instance.
+        :param iterations: The number of iterations used for benchmarking each kernel instance.
         :type iterations: int
         """
-        #detect language and create high-level device interface
+        # detect language and create high-level device interface
         self.dev = DeviceInterface(kernel_source, iterations=iterations, observers=observers, **device_options)
 
         self.units = self.dev.units
@@ -41,9 +39,12 @@ def __init__(self, kernel_source, kernel_options, device_options, iterations, ob
         self.last_strategy_time = 0
         self.kernel_options = kernel_options
 
-        #move data to the GPU
+        # move data to the GPU
         self.gpu_args = self.dev.ready_argument_list(kernel_options.arguments)
 
+    def get_device_info(self):
+        return self.dev
+
     def get_environment(self, tuning_options):
         return self.dev.get_environment()
 
@@ -53,16 +54,14 @@ def run(self, parameter_space, tuning_options):
         :param parameter_space: The parameter space as an iterable.
         :type parameter_space: iterable
 
-        :param tuning_options: A dictionary with all options regarding the tuning
-            process.
-        :type tuning_options: kernel_tuner.iterface.Options
+        :param tuning_options: A dictionary with all options regarding the tuning process.
+        :type tuning_options: kernel_tuner.interface.Options
 
-        :returns: A list of dictionaries for executed kernel configurations and their
-            execution times.
-        :rtype: dict())
+        :returns: A list of dictionaries for executed kernel configurations and their execution times.
+        :rtype: dict()
 
         """
-        logging.debug('sequential runner started for ' + self.kernel_options.kernel_name)
+        logging.debug("sequential runner started for " + self.kernel_options.kernel_name)
 
         results = []
 
@@ -77,33 +76,46 @@ def run(self, parameter_space, tuning_options):
             x_int = ",".join([str(i) for i in element])
             if tuning_options.cache and x_int in tuning_options.cache:
                 params.update(tuning_options.cache[x_int])
-                params['compile_time'] = 0
-                params['verification_time'] = 0
-                params['benchmark_time'] = 0
+                params["compile_time"] = 0
+                params["verification_time"] = 0
+                params["benchmark_time"] = 0
             else:
                 # attempt to warmup the GPU by running the first config in the parameter space and ignoring the result
                 if not self.warmed_up:
                     warmup_time = perf_counter()
-                    self.dev.compile_and_benchmark(self.kernel_source, self.gpu_args, params, self.kernel_options, tuning_options)
+                    self.dev.compile_and_benchmark(
+                        self.kernel_source, self.gpu_args, params, self.kernel_options, tuning_options
+                    )
                     self.warmed_up = True
                     warmup_time = 1e3 * (perf_counter() - warmup_time)
 
-                result = self.dev.compile_and_benchmark(self.kernel_source, self.gpu_args, params, self.kernel_options, tuning_options)
+                result = self.dev.compile_and_benchmark(
+                    self.kernel_source, self.gpu_args, params, self.kernel_options, tuning_options
+                )
 
                 params.update(result)
 
                 if tuning_options.objective in result and isinstance(result[tuning_options.objective], ErrorConfig):
-                    logging.debug('kernel configuration was skipped silently due to compile or runtime failure')
+                    logging.debug("kernel configuration was skipped silently due to compile or runtime failure")
 
             # only compute metrics on configs that have not errored
             if tuning_options.metrics and not isinstance(params.get(tuning_options.objective), ErrorConfig):
                 params = process_metrics(params, tuning_options.metrics)
 
             # get the framework time by estimating based on other times
-            total_time = 1000 * ((perf_counter() - self.start_time) - warmup_time) 
-            params['strategy_time'] = self.last_strategy_time
-            params['framework_time'] = max(total_time - (params['compile_time'] + params['verification_time'] + params['benchmark_time'] + params['strategy_time']), 0)
-            params['timestamp'] = str(datetime.now(timezone.utc))
+            total_time = 1000 * ((perf_counter() - self.start_time) - warmup_time)
+            params["strategy_time"] = self.last_strategy_time
+            params["framework_time"] = max(
+                total_time
+                - (
+                    params["compile_time"]
+                    + params["verification_time"]
+                    + params["benchmark_time"]
+                    + params["strategy_time"]
+                ),
+                0,
+            )
+            params["timestamp"] = str(datetime.now(timezone.utc))
             self.start_time = perf_counter()
 
             if result:
@@ -111,7 +123,7 @@ def run(self, parameter_space, tuning_options):
                 print_config_output(tuning_options.tune_params, params, self.quiet, tuning_options.metrics, self.units)
 
                 # add configuration to cache
-                store_cache(x_int, params, tuning_options)
+                store_cache(x_int, params, tuning_options.cachefile, tuning_options.cache)
 
             # all visited configurations are added to results to provide a trace for optimization strategies
             results.append(params)
diff --git a/kernel_tuner/runners/simulation.py b/kernel_tuner/runners/simulation.py
index 9695879d..b369b85a 100644
--- a/kernel_tuner/runners/simulation.py
+++ b/kernel_tuner/runners/simulation.py
@@ -16,11 +16,11 @@ class SimulationDevice(_SimulationDevice):
 
     @property
     def name(self):
-        return self.env['device_name']
+        return self.env["device_name"]
 
     @name.setter
     def name(self, value):
-        self.env['device_name'] = value
+        self.env["device_name"] = value
         if not self.quiet:
             print("Simulating: " + value)
 
@@ -40,12 +40,10 @@ def __init__(self, kernel_source, kernel_options, device_options, iterations, ob
         :param kernel_options: A dictionary with all options for the kernel.
         :type kernel_options: kernel_tuner.interface.Options
 
-        :param device_options: A dictionary with all options for the device
-            on which the kernel should be tuned.
+        :param device_options: A dictionary with all options for the device on which the kernel should be tuned.
         :type device_options: kernel_tuner.interface.Options
 
-        :param iterations: The number of iterations used for benchmarking
-            each kernel instance.
+        :param iterations: The number of iterations used for benchmarking each kernel instance.
         :type iterations: int
         """
         self.quiet = device_options.quiet
@@ -60,6 +58,9 @@ def __init__(self, kernel_source, kernel_options, device_options, iterations, ob
         self.last_strategy_time = 0
         self.units = {}
 
+    def get_device_info(self):
+        return self.dev
+    
     def get_environment(self, tuning_options):
         env = self.dev.get_environment()
         env["simulation"] = True
@@ -72,21 +73,18 @@ def run(self, parameter_space, tuning_options):
         :param parameter_space: The parameter space as an iterable.
         :type parameter_space: iterable
 
-        :param tuning_options: A dictionary with all options regarding the tuning
-            process.
+        :param tuning_options: A dictionary with all options regarding the tuning process.
         :type tuning_options: kernel_tuner.iterface.Options
 
-        :returns: A list of dictionaries for executed kernel configurations and their
-            execution times.
+        :returns: A list of dictionaries for executed kernel configurations and their execution times.
         :rtype: dict()
         """
-        logging.debug('simulation runner started for ' + self.kernel_options.kernel_name)
+        logging.debug("simulation runner started for " + self.kernel_options.kernel_name)
 
         results = []
 
-        # iterate over parameter space 
+        # iterate over parameter space
         for element in parameter_space:
-
             # check if element is in the cache
             x_int = ",".join([str(i) for i in element])
             if tuning_options.cache and x_int in tuning_options.cache:
@@ -105,21 +103,22 @@ def run(self, parameter_space, tuning_options):
                 # configuration is already counted towards the unique_results.
                 # It is the responsibility of cost_func to add configs to unique_results.
                 if x_int in tuning_options.unique_results:
-
-                    result['compile_time'] = 0
-                    result['verification_time'] = 0
-                    result['benchmark_time'] = 0
+                    result["compile_time"] = 0
+                    result["verification_time"] = 0
+                    result["benchmark_time"] = 0
 
                 else:
                     # configuration is evaluated for the first time, print to the console
-                    util.print_config_output(tuning_options.tune_params, result, self.quiet, tuning_options.metrics, self.units)
+                    util.print_config_output(
+                        tuning_options.tune_params, result, self.quiet, tuning_options.metrics, self.units
+                    )
 
                 # Everything but the strategy time and framework time are simulated,
                 # self.last_strategy_time is set by cost_func
-                result['strategy_time'] = self.last_strategy_time
+                result["strategy_time"] = self.last_strategy_time
 
                 try:
-                    simulated_time = result['compile_time'] + result['verification_time'] + result['benchmark_time']
+                    simulated_time = result["compile_time"] + result["verification_time"] + result["benchmark_time"]
                     tuning_options.simulated_time += simulated_time
                 except KeyError:
                     if "time_limit" in tuning_options:
@@ -129,7 +128,7 @@ def run(self, parameter_space, tuning_options):
 
                 total_time = 1000 * (perf_counter() - self.start_time)
                 self.start_time = perf_counter()
-                result['framework_time'] = total_time - self.last_strategy_time
+                result["framework_time"] = total_time - self.last_strategy_time
 
                 results.append(result)
                 continue
diff --git a/kernel_tuner/strategies/bayes_opt.py b/kernel_tuner/strategies/bayes_opt.py
index a814e7ce..31a68ca8 100644
--- a/kernel_tuner/strategies/bayes_opt.py
+++ b/kernel_tuner/strategies/bayes_opt.py
@@ -455,6 +455,33 @@ def fit_observations_to_model(self):
         """Update the model based on the current list of observations."""
         self.__model.fit(self.__valid_params, self.__valid_observations)
 
+    def evaluate_parallel_objective_function(self, param_configs: list[tuple]) -> list[float]:
+        """Evaluates the objective function for multiple configurations in parallel."""
+        results = []
+        valid_param_configs = []
+        valid_indices = []
+
+        # Extract the valid configurations
+        for param_config in param_configs:
+            param_config = self.unprune_param_config(param_config)
+            denormalized_param_config = self.denormalize_param_config(param_config)
+            if not self.__searchspace_obj.is_param_config_valid(denormalized_param_config):
+                results.append(self.invalid_value)
+            else:
+                valid_indices.append(len(results))
+                results.append(None)
+                valid_param_configs.append(param_config)
+
+        # Run valid configurations in parallel
+        scores = self.cost_func.run_all(valid_param_configs)
+
+        # Put the scores at the right location in the result
+        for idx, score in zip(valid_indices, scores):
+            results[idx] = score
+            
+        self.fevals += len(scores)
+        return results
+
     def evaluate_objective_function(self, param_config: tuple) -> float:
         """Evaluates the objective function."""
         param_config = self.unprune_param_config(param_config)
diff --git a/kernel_tuner/strategies/common.py b/kernel_tuner/strategies/common.py
index 9ffe999b..e567d998 100644
--- a/kernel_tuner/strategies/common.py
+++ b/kernel_tuner/strategies/common.py
@@ -103,6 +103,12 @@ def __init__(
 
 
     def __call__(self, x, check_restrictions=True):
+        return self.run_one(x, check_restrictions=check_restrictions)
+
+    def run_all(self, xs, check_restrictions=True):
+        return [self.run_one(x, check_restrictions=check_restrictions) for x in xs]
+
+    def run_one(self, x, check_restrictions=True):
         """Cost function used by almost all strategies."""
         self.runner.last_strategy_time = 1000 * (perf_counter() - self.runner.last_strategy_start_time)
 
diff --git a/kernel_tuner/strategies/diff_evo.py b/kernel_tuner/strategies/diff_evo.py
index d80b6e8e..6350b7d9 100644
--- a/kernel_tuner/strategies/diff_evo.py
+++ b/kernel_tuner/strategies/diff_evo.py
@@ -140,7 +140,7 @@ def differential_evolution(searchspace, cost_func, bounds, popsize, maxiter, F,
     population[0] = cost_func.get_start_pos()
 
     # Calculate the initial cost for each individual in the population
-    population_cost = np.array([cost_func(ind) for ind in population])
+    population_cost = np.array(cost_func.run_all(population))
 
     # Keep track of the best solution found so far
     best_idx = np.argmin(population_cost)
@@ -209,7 +209,7 @@ def differential_evolution(searchspace, cost_func, bounds, popsize, maxiter, F,
         # --- c. Selection ---
 
         # Calculate the cost of the new trial vectors
-        trial_population_cost = np.array([cost_func(ind) for ind in trial_population])
+        trial_population_cost = np.array(cost_func.run_all(trial_population))
 
         # Keep track of whether population changes over time
         no_change = True
diff --git a/kernel_tuner/strategies/firefly_algorithm.py b/kernel_tuner/strategies/firefly_algorithm.py
index a732d404..861c5f86 100644
--- a/kernel_tuner/strategies/firefly_algorithm.py
+++ b/kernel_tuner/strategies/firefly_algorithm.py
@@ -44,13 +44,14 @@ def tune(searchspace: Searchspace, runner, tuning_options):
     swarm[0].position = x0
 
     # compute initial intensities
-    for j in range(num_particles):
-        try:
+    try:
+        for j in range(num_particles):
             swarm[j].compute_intensity(cost_func)
-        except StopCriterionReached as e:
-            if tuning_options.verbose:
-                print(e)
-            return cost_func.results
+    except StopCriterionReached as e:
+        if tuning_options.verbose:
+            print(e)
+        return cost_func.results
+    for j in range(num_particles):
         if swarm[j].score <= best_score_global:
             best_position_global = swarm[j].position
             best_score_global = swarm[j].score
diff --git a/kernel_tuner/strategies/genetic_algorithm.py b/kernel_tuner/strategies/genetic_algorithm.py
index 804758ee..230cfd49 100644
--- a/kernel_tuner/strategies/genetic_algorithm.py
+++ b/kernel_tuner/strategies/genetic_algorithm.py
@@ -43,19 +43,17 @@ def tune(searchspace: Searchspace, runner, tuning_options):
 
         # determine fitness of population members
         weighted_population = []
-        for dna in population:
-            try:
-                # if we are not constraint-aware we should check restrictions upon evaluation
-                time = cost_func(dna, check_restrictions=not constraint_aware)
-                num_evaluated += 1
-            except StopCriterionReached as e:
-                if tuning_options.verbose:
-                    print(e)
-                return cost_func.results
-
-            weighted_population.append((dna, time))
+        try:
+            # if we are not constraint-aware we should check restrictions upon evaluation
+            times = cost_func.run_all(population, check_restrictions=not constraint_aware)
+            num_evaluated += len(population)
+        except StopCriterionReached as e:
+            if tuning_options.verbose:
+                print(e)
+            return cost_func.results
 
         # population is sorted such that better configs have higher chance of reproducing
+        weighted_population = list(zip(population, times))
         weighted_population.sort(key=lambda x: x[1])
 
         # 'best_score' is used only for printing
diff --git a/kernel_tuner/strategies/hillclimbers.py b/kernel_tuner/strategies/hillclimbers.py
index ccd4eebf..cc53d7db 100644
--- a/kernel_tuner/strategies/hillclimbers.py
+++ b/kernel_tuner/strategies/hillclimbers.py
@@ -72,33 +72,39 @@ def base_hillclimb(base_sol: tuple, neighbor_method: str, max_fevals: int, searc
         if randomize:
             random.shuffle(indices)
 
+        children = []
+
         # in each dimension see the possible values
         for index in indices:
             neighbors = searchspace.get_param_neighbors(tuple(child), index, neighbor_method, randomize)
 
             # for each value in this dimension
             for val in neighbors:
-                orig_val = child[index]
+                child = list(child)
                 child[index] = val
+                children.append(child)
 
+        if restart:
+            for child in children:
                 # get score for this position
                 score = cost_func(child, check_restrictions=False)
 
-                # generalize this to other tuning objectives
                 if score < best_score:
                     best_score = score
                     base_sol = child[:]
                     found_improved = True
-                    if restart:
-                        break
-                else:
-                    child[index] = orig_val
+                    break
+        else:
+            # get score for all positions in parallel
+            scores = cost_func.run_all(children, check_restrictions=False)
 
-                fevals = len(tuning_options.unique_results)
-                if fevals >= max_fevals:
-                    return base_sol
+            for child, score in zip(children, scores):
+                if score < best_score:
+                    best_score = score
+                    base_sol = child[:]
+                    found_improved = True
 
-            if found_improved and restart:
-                break
+        if found_improved and restart:
+            break
 
     return base_sol
diff --git a/kernel_tuner/strategies/pso.py b/kernel_tuner/strategies/pso.py
index e8489d12..4e38aa31 100644
--- a/kernel_tuner/strategies/pso.py
+++ b/kernel_tuner/strategies/pso.py
@@ -51,24 +51,26 @@ def tune(searchspace: Searchspace, runner, tuning_options):
         if tuning_options.verbose:
             print("start iteration ", i, "best time global", best_score_global)
 
+        try:
+            scores = cost_func.run_all([p.position for p in swarm])
+        except StopCriterionReached as e:
+            if tuning_options.verbose:
+                print(e)
+            return cost_func.results
+
         # evaluate particle positions
-        for j in range(num_particles):
-            try:
-                swarm[j].evaluate(cost_func)
-            except StopCriterionReached as e:
-                if tuning_options.verbose:
-                    print(e)
-                return cost_func.results
+        for p, score in zip(swarm, scores):
+            p.set_score(score)
 
             # update global best if needed
-            if swarm[j].score <= best_score_global:
-                best_position_global = swarm[j].position
-                best_score_global = swarm[j].score
+            if score <= best_score_global:
+                best_position_global = p.position
+                best_score_global = score
 
         # update particle velocities and positions
-        for j in range(0, num_particles):
-            swarm[j].update_velocity(best_position_global, w, c1, c2)
-            swarm[j].update_position(bounds)
+        for p in swarm:
+            p.update_velocity(best_position_global, w, c1, c2)
+            p.update_position(bounds)
 
     if tuning_options.verbose:
         print("Final result:")
@@ -92,7 +94,10 @@ def __init__(self, bounds):
         self.score = sys.float_info.max
 
     def evaluate(self, cost_func):
-        self.score = cost_func(self.position)
+        self.set_score(cost_func(self.position))
+
+    def set_score(self, score):
+        self.score = score
         # update best_pos if needed
         if self.score < self.best_score:
             self.best_pos = self.position
diff --git a/kernel_tuner/strategies/random_sample.py b/kernel_tuner/strategies/random_sample.py
index 33b5075d..4efe8615 100644
--- a/kernel_tuner/strategies/random_sample.py
+++ b/kernel_tuner/strategies/random_sample.py
@@ -20,16 +20,13 @@ def tune(searchspace: Searchspace, runner, tuning_options):
         num_samples = min(tuning_options.max_fevals, searchspace.size)
 
     samples = searchspace.get_random_sample(num_samples)
-
     cost_func = CostFunc(searchspace, tuning_options, runner)
 
-    for sample in samples:
-        try:
-            cost_func(sample, check_restrictions=False)
-        except StopCriterionReached as e:
-            if tuning_options.verbose:
-                print(e)
-            return cost_func.results
+    try:
+        cost_func.run_all(samples, check_restrictions=False)
+    except StopCriterionReached as e:
+        if tuning_options.verbose:
+            print(e)
 
     return cost_func.results
 
diff --git a/kernel_tuner/util.py b/kernel_tuner/util.py
index 2d9e3f1b..635c6de7 100644
--- a/kernel_tuner/util.py
+++ b/kernel_tuner/util.py
@@ -1152,7 +1152,7 @@ def check_matching_problem_size(cached_problem_size, problem_size):
     if cached_problem_size_arr.size != problem_size_arr.size or not (cached_problem_size_arr == problem_size_arr).all():
         raise ValueError(f"Cannot load cache which contains results for different problem_size, cache: {cached_problem_size}, requested: {problem_size}")
 
-def process_cache(cache, kernel_options, tuning_options, runner):
+def process_cache(cachefile, kernel_options, tuning_options, runner):
     """Cache file for storing tuned configurations.
 
     the cache file is stored using JSON and uses the following format:
@@ -1181,9 +1181,9 @@ def process_cache(cache, kernel_options, tuning_options, runner):
         raise ValueError("Caching only works correctly when tunable parameters are stored in a dictionary")
 
     # if file does not exist, create new cache
-    if not os.path.isfile(cache):
+    if not os.path.isfile(cachefile):
         if tuning_options.simulation_mode:
-            raise ValueError(f"Simulation mode requires an existing cachefile: file {cache} does not exist")
+            raise ValueError(f"Simulation mode requires an existing cachefile: file {cachefile} does not exist")
 
         c = dict()
         c["device_name"] = runner.dev.name
@@ -1197,15 +1197,14 @@ def process_cache(cache, kernel_options, tuning_options, runner):
         contents = json.dumps(c, cls=NpEncoder, indent="")[:-3]  # except the last "}\n}"
 
         # write the header to the cachefile
-        with open(cache, "w") as cachefile:
-            cachefile.write(contents)
+        with open(cachefile, "w") as f:
+            f.write(contents)
 
-        tuning_options.cachefile = cache
-        tuning_options.cache = {}
+        return {}
 
     # if file exists
     else:
-        cached_data = read_cache(cache, open_cache=not tuning_options.simulation_mode)
+        cached_data = read_cache(cachefile, open_cache=not tuning_options.simulation_mode)
 
         # if in simulation mode, use the device name from the cache file as the runner device name
         if runner.simulation_mode:
@@ -1231,17 +1230,16 @@ def process_cache(cache, kernel_options, tuning_options, runner):
                 )
             raise ValueError(
                 f"Cannot load cache which contains results obtained with different tunable parameters. \
-                Cache at '{cache}' has: {cached_data['tune_params_keys']}, tuning_options has: {list(tuning_options.tune_params.keys())}"
+                Cache at '{cachefile}' has: {cached_data['tune_params_keys']}, tuning_options has: {list(tuning_options.tune_params.keys())}"
             )
 
-        tuning_options.cachefile = cache
-        tuning_options.cache = cached_data["cache"]
+        return cached_data["cache"]
 
 
-def correct_open_cache(cache, open_cache=True):
+def correct_open_cache(cachefile, open_cache=True):
     """If cache file was not properly closed, pretend it was properly closed."""
-    with open(cache, "r") as cachefile:
-        filestr = cachefile.read().strip()
+    with open(cachefile, "r") as f:
+        filestr = f.read().strip()
 
     # if file was not properly closed, pretend it was properly closed
     if len(filestr) > 0 and filestr[-3:] not in ["}\n}", "}}}"]:
@@ -1253,15 +1251,15 @@ def correct_open_cache(cache, open_cache=True):
     else:
         if open_cache:
             # if it was properly closed, open it for appending new entries
-            with open(cache, "w") as cachefile:
-                cachefile.write(filestr[:-3] + ",")
+            with open(cachefile, "w") as f:
+                f.write(filestr[:-3] + ",")
 
     return filestr
 
 
-def read_cache(cache, open_cache=True):
+def read_cache(cachefile, open_cache=True):
     """Read the cachefile into a dictionary, if open_cache=True prepare the cachefile for appending."""
-    filestr = correct_open_cache(cache, open_cache)
+    filestr = correct_open_cache(cachefile, open_cache)
 
     error_configs = {
         "InvalidConfig": InvalidConfig(),
@@ -1279,25 +1277,25 @@ def read_cache(cache, open_cache=True):
     return cache_data
 
 
-def close_cache(cache):
-    if not os.path.isfile(cache):
+def close_cache(cachefile):
+    if not os.path.isfile(cachefile):
         raise ValueError("close_cache expects cache file to exist")
 
-    with open(cache, "r") as fh:
+    with open(cachefile, "r") as fh:
         contents = fh.read()
 
     # close to file to make sure it can be read by JSON parsers
     if contents[-1] == ",":
-        with open(cache, "w") as fh:
+        with open(cachefile, "w") as fh:
             fh.write(contents[:-1] + "}\n}")
 
 
-def store_cache(key, params, tuning_options):
+def store_cache(key, params, cachefile, cache):
     """Stores a new entry (key, params) to the cachefile."""
     # logging.debug('store_cache called, cache=%s, cachefile=%s' % (tuning_options.cache, tuning_options.cachefile))
-    if isinstance(tuning_options.cache, dict):
-        if key not in tuning_options.cache:
-            tuning_options.cache[key] = params
+    if isinstance(cache, dict):
+        if key not in cache:
+            cache[key] = params
 
             # Convert ErrorConfig objects to string, wanted to do this inside the JSONconverter but couldn't get it to work
             output_params = params.copy()
@@ -1305,9 +1303,9 @@ def store_cache(key, params, tuning_options):
                 if isinstance(v, ErrorConfig):
                     output_params[k] = str(v)
 
-            if tuning_options.cachefile:
-                with open(tuning_options.cachefile, "a") as cachefile:
-                    cachefile.write("\n" + json.dumps({key: output_params}, cls=NpEncoder)[1:-1] + ",")
+            if cachefile:
+                with open(cachefile, "a") as f:
+                    f.write("\n" + json.dumps({key: output_params}, cls=NpEncoder)[1:-1] + ",")
 
 
 def dump_cache(obj: str, tuning_options):
diff --git a/test/test_util_functions.py b/test/test_util_functions.py
index 4a1858f3..56a5a761 100644
--- a/test/test_util_functions.py
+++ b/test/test_util_functions.py
@@ -621,25 +621,25 @@ def assert_open_cachefile_is_correctly_parsed(cache):
 
     try:
         # call process_cache without pre-existing cache
-        process_cache(cache, kernel_options, tuning_options, runner)
+        tuning_options.cachefile = cache
+        tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
 
         # check if file has been created
         assert os.path.isfile(cache)
         assert_open_cachefile_is_correctly_parsed(cache)
-        assert tuning_options.cachefile == cache
         assert isinstance(tuning_options.cache, dict)
         assert len(tuning_options.cache) == 0
 
         # store one entry in the cache
         params = {"x": 4, "time": np.float32(0.1234)}
-        store_cache("4", params, tuning_options)
+        store_cache("4", params, cache, tuning_options.cache)
         assert len(tuning_options.cache) == 1
 
         # close the cache
         close_cache(cache)
 
         # now test process cache with a pre-existing cache file
-        process_cache(cache, kernel_options, tuning_options, runner)
+        tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
         assert_open_cachefile_is_correctly_parsed(cache)
 
         assert tuning_options.cache["4"]["time"] == params["time"]
@@ -648,7 +648,7 @@ def assert_open_cachefile_is_correctly_parsed(cache):
         # a different kernel, device, or parameter set
         with pytest.raises(ValueError) as excep:
             kernel_options.kernel_name = "wrong_kernel"
-            process_cache(cache, kernel_options, tuning_options, runner)
+            tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
         assert "kernel" in str(excep.value)
 
         # correct the kernel name from last test
@@ -656,7 +656,7 @@ def assert_open_cachefile_is_correctly_parsed(cache):
 
         with pytest.raises(ValueError) as excep:
             runner.dev.name = "wrong_device"
-            process_cache(cache, kernel_options, tuning_options, runner)
+            tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
         assert "device" in str(excep.value)
 
         # correct the device from last test
@@ -664,7 +664,7 @@ def assert_open_cachefile_is_correctly_parsed(cache):
 
         with pytest.raises(ValueError) as excep:
             tuning_options.tune_params["y"] = ["a", "b"]
-            process_cache(cache, kernel_options, tuning_options, runner)
+            tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
         assert "parameter" in str(excep.value)
 
     finally: