Implement method for listing devices from python

CLUEstering/alpaka/BindingModules/CLUEstering.py

@@ -15,7 +15,7 @@
 from sklearn.preprocessing import StandardScaler
 import CLUE_Convolutional_Kernels as clue_kernels
 import CLUE_CPU_Serial as cpu_serial
-# import CLUE_CPU_TBB as cpu_tbb
+import CLUE_CPU_TBB as cpu_tbb
 import CLUE_GPU_CUDA as gpu_cuda
 
 def test_blobs(n_samples: int, n_dim: int , n_blobs: int = 4, mean: float = 0,
@@ -476,6 +476,36 @@ def choose_kernel(self,
             raise ValueError("Invalid kernel. The allowed choices for the"
                              + " kernels are: flat, exp, gaus and custom.")
 
+    def list_devices(self, backend: str = "all") -> None:
+        """
+        Lists the devices available for the chosen backend.
+
+        Parameters
+        ----------
+        backend : string, optional
+            The backend for which the devices are listed. The allowed values are
+            'all', 'cpu serial', 'cpu tbb' and 'gpu cuda'.
+            The default value is 'all'.
+
+        Raises
+        ------
+        ValueError : If the backend is not valid.
+        """
+
+        if backend == "all":
+            cpu_serial.listDevices('cpu serial')
+            cpu_tbb.listDevices('cpu tbb')
+            gpu_cuda.listDevices('gpu cuda')
+        elif backend == "cpu serial":
+            cpu_serial.listDevices(backend)
+        elif backend == "cpu tbb":
+            cpu_tbb.listDevices(backend)
+        elif backend == "gpu cuda":
+            gpu_cuda.listDevices(backend)
+        else:
+            raise ValueError("Invalid backend. The allowed choices for the"
+                             + " backend are: all, cpu serial, cpu tbb and gpu cuda.")
+
     def run_clue(self,
                  backend: str = "cpu serial",
                  block_size: int = 1024,
@@ -832,8 +862,12 @@ def to_csv(self, output_folder: str, file_name: str) -> None:
 if __name__ == "__main__":
     c = clusterer(0.4,5,1.)
     c.read_data('./sissa.csv')
-    # c.run_clue(backend="cpu serial", verbose=True)
-    c.run_clue(backend="cpu tbb", verbose=True)
-    c.run_clue(backend="gpu cuda", verbose=True)
+    c.run_clue(backend="cpu serial", verbose=True)
+    # c.run_clue(backend="cpu tbb", verbose=True)
+    # c.run_clue(backend="gpu cuda", verbose=True)
     c.cluster_plotter()
-    c.to_csv('./','sissa_output_tbb.csv')
+    # c.to_csv('./','sissa_output_tbb.csv')
+    c.list_devices('cpu serial')
+    c.list_devices('cpu tbb')
+    c.list_devices('gpu cuda')
+    c.list_devices()

CLUEstering/alpaka/BindingModules/binding_cpu.cc

@@ -13,6 +13,20 @@
 #include <stdint.h>
 
 namespace alpaka_serial_sync {
+  void listDevices(const std::string& backend) {
+	const char tab = '\t';
+    const std::vector<Device> devices = alpaka::getDevs<Platform>();
+    if (devices.empty()) {
+      std::cout << "No devices found for the " << backend << " backend." << std::endl;
+      return;
+    } else {
+	  std::cout << backend << " devices found: \n";
+      for (size_t i{}; i < devices.size(); ++i) {
+        std::cout << tab << "device " << i << ": " << alpaka::getName(devices[i]) << '\n';
+      }
+    }
+  }
+
   std::vector<std::vector<int>> mainRun(float dc,
                                         float rhoc,
                                         float outlier,
@@ -202,6 +216,7 @@ namespace alpaka_serial_sync {
   PYBIND11_MODULE(CLUE_CPU_Serial, m) {
     m.doc() = "Binding of the CLUE algorithm running serially on CPU";
 
+	m.def("listDevices", &listDevices, "List the available devices for the CPU serial backend");
     m.def("mainRun",
           pybind11::overload_cast<float,
                                   float,

CLUEstering/alpaka/BindingModules/binding_cpu_tbb.cc

@@ -13,6 +13,20 @@
 #include <stdint.h>
 
 namespace alpaka_tbb_async {
+  void listDevices(const std::string& backend) {
+	const char tab = '\t';
+    const std::vector<Device> devices = alpaka::getDevs<Platform>();
+    if (devices.empty()) {
+      std::cout << "No devices found for the " << backend << " backend." << std::endl;
+      return;
+    } else {
+	  std::cout << backend << " devices found: \n";
+      for (size_t i{}; i < devices.size(); ++i) {
+        std::cout << tab << "Device " << i << ": " << alpaka::getName(devices[i]) << '\n';
+      }
+    }
+  }
+
   std::vector<std::vector<int>> mainRun(float dc,
                                         float rhoc,
                                         float outlier,
@@ -216,6 +230,7 @@ namespace alpaka_tbb_async {
     /* pybind11::class_<CustomKernel, ConvolutionalKernel>(m, "CustomKernel") */
     /*     .def(pybind11::init<kernel_t>()) */
     /*     .def("operator()", &CustomKernel::operator()); */
+	m.def("listDevices", &listDevices, "List the available devices for the TBB backend");
     m.def("mainRun",
           pybind11::overload_cast<float,
                                   float,

CLUEstering/alpaka/BindingModules/binding_gpu_cuda.cc

@@ -15,6 +15,20 @@
 using cms::alpakatools::initialise;
 
 namespace alpaka_cuda_async {
+  void listDevices(const std::string& backend) {
+	const char tab = '\t';
+    const std::vector<Device> devices = alpaka::getDevs<Platform>();
+    if (devices.empty()) {
+      std::cout << "No devices found for the " << backend << " backend." << std::endl;
+      return;
+    } else {
+	  std::cout << backend << " devices found: \n";
+      for (size_t i{}; i < devices.size(); ++i) {
+        std::cout << tab << "device " << i << ": " << alpaka::getName(devices[i]) << '\n';
+      }
+    }
+  }
+
   std::vector<std::vector<int>> mainRun(float dc,
                                         float rhoc,
                                         float outlier,
@@ -208,6 +222,7 @@ namespace alpaka_cuda_async {
   PYBIND11_MODULE(CLUE_GPU_CUDA, m) {
     m.doc() = "Binding of the CLUE algorithm running on CUDA GPUs";
 
+	m.def("listDevices", &listDevices, "List the available devices for the CUDA backend");
     m.def("mainRun",
           pybind11::overload_cast<float,
                                   float,