CUDA 13.2 samples update (#432)

- Added Python samples for CUDA Python 1.0 release
- Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.

cpp/0_Introduction/clock/.vscode/c_cpp_properties.json

@@ -0,0 +1,18 @@
+{
+    "configurations": [
+        {
+            "name": "Linux",
+            "includePath": [
+                "${workspaceFolder}/**",
+                "${workspaceFolder}/../../../Common"
+            ],
+            "defines": [],
+            "compilerPath": "/usr/local/cuda/bin/nvcc",
+            "cStandard": "gnu17",
+            "cppStandard": "gnu++14",
+            "intelliSenseMode": "linux-gcc-x64",
+            "configurationProvider": "ms-vscode.makefile-tools"
+        }
+    ],
+    "version": 4
+}

cpp/0_Introduction/clock/.vscode/extensions.json

@@ -0,0 +1,7 @@
+{
+    "recommendations": [
+        "nvidia.nsight-vscode-edition",
+        "ms-vscode.cpptools",
+        "ms-vscode.makefile-tools"
+    ]
+}

cpp/0_Introduction/clock/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.20)
+
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules")
+
+project(clock LANGUAGES C CXX CUDA)
+
+find_package(CUDAToolkit REQUIRED)
+
+set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+
+set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120)
+set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets")
+if(ENABLE_CUDA_DEBUG)
+    set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G")        # enable cuda-gdb (may significantly affect performance on some targets)
+else()
+    set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option)
+endif()
+
+# Include directories and libraries
+include_directories(../../../Common)
+
+# Source file
+# Add target for asyncAPI
+add_executable(clock clock.cu)
+
+target_compile_options(clock PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>)
+
+target_compile_features(clock PRIVATE cxx_std_17 cuda_std_17)
+
+set_target_properties(clock PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
+
+# Include installation configuration
+include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake)
+setup_samples_install()

cpp/0_Introduction/clock/README.md

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+# clock - Clock
+
+## Description
+
+This example shows how to use the clock function to measure the performance of block of threads of a kernel accurately.
+
+## Key Concepts
+
+Performance Strategies
+
+## Supported SM Architectures
+
+[SM 5.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 5.2 ](https://developer.nvidia.com/cuda-gpus)  [SM 5.3 ](https://developer.nvidia.com/cuda-gpus)  [SM 6.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 6.1 ](https://developer.nvidia.com/cuda-gpus)  [SM 7.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 7.2 ](https://developer.nvidia.com/cuda-gpus)  [SM 7.5 ](https://developer.nvidia.com/cuda-gpus)  [SM 8.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 8.6 ](https://developer.nvidia.com/cuda-gpus)  [SM 8.7 ](https://developer.nvidia.com/cuda-gpus)  [SM 8.9 ](https://developer.nvidia.com/cuda-gpus)  [SM 9.0 ](https://developer.nvidia.com/cuda-gpus)
+
+## Supported OSes
+
+Linux, Windows
+
+## Supported CPU Architecture
+
+x86_64, armv7l
+
+## CUDA APIs involved
+
+### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
+cudaMalloc, cudaMemcpy, cudaFree
+
+## Prerequisites
+
+Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
+
+## References (for more details)

cpp/0_Introduction/clock/clock.cu

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+/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *  * Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ *  * Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *  * Neither the name of NVIDIA CORPORATION nor the names of its
+ *    contributors may be used to endorse or promote products derived
+ *    from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+ * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+/*
+ * This example shows how to use the clock function to measure the performance
+ * of block of threads of a kernel accurately. Blocks are executed in parallel
+ * and out of order. Since there's no synchronization mechanism between blocks,
+ * we measure the clock once for each block. The clock samples are written to
+ * device memory.
+ */
+
+// System includes
+#include <assert.h>
+#include <stdint.h>
+#include <stdio.h>
+
+// CUDA runtime
+#include <cuda_runtime.h>
+
+// helper functions and utilities to work with CUDA
+#include <helper_cuda.h>
+#include <helper_functions.h>
+
+// This kernel computes a standard parallel reduction and evaluates the
+// time it takes to do that for each block. The timing results are stored
+// in device memory.
+__global__ static void timedReduction(const float *input, float *output, clock_t *timer)
+{
+    // __shared__ float shared[2 * blockDim.x];
+    extern __shared__ float shared[];
+
+    const int tid = threadIdx.x;
+    const int bid = blockIdx.x;
+
+    if (tid == 0)
+        timer[bid] = clock();
+
+    // Copy input.
+    shared[tid]              = input[tid];
+    shared[tid + blockDim.x] = input[tid + blockDim.x];
+
+    // Perform reduction to find minimum.
+    for (int d = blockDim.x; d > 0; d /= 2) {
+        __syncthreads();
+
+        if (tid < d) {
+            float f0 = shared[tid];
+            float f1 = shared[tid + d];
+
+            if (f1 < f0) {
+                shared[tid] = f1;
+            }
+        }
+    }
+
+    // Write result.
+    if (tid == 0)
+        output[bid] = shared[0];
+
+    __syncthreads();
+
+    if (tid == 0)
+        timer[bid + gridDim.x] = clock();
+}
+
+#define NUM_BLOCKS  64
+#define NUM_THREADS 256
+
+// It's interesting to change the number of blocks and the number of threads to
+// understand how to keep the hardware busy.
+//
+// Here are some numbers I get on my G80:
+//    blocks - clocks
+//    1 - 3096
+//    8 - 3232
+//    16 - 3364
+//    32 - 4615
+//    64 - 9981
+//
+// With less than 16 blocks some of the multiprocessors of the device are idle.
+// With more than 16 you are using all the multiprocessors, but there's only one
+// block per multiprocessor and that doesn't allow you to hide the latency of
+// the memory. With more than 32 the speed scales linearly.
+
+// Start the main CUDA Sample here
+int main(int argc, char **argv)
+{
+    printf("CUDA Clock sample\n");
+
+    // This will pick the best possible CUDA capable device
+    int dev = findCudaDevice(argc, (const char **)argv);
+
+    float   *dinput  = NULL;
+    float   *doutput = NULL;
+    clock_t *dtimer  = NULL;
+
+    clock_t timer[NUM_BLOCKS * 2];
+    float   input[NUM_THREADS * 2];
+
+    for (int i = 0; i < NUM_THREADS * 2; i++) {
+        input[i] = (float)i;
+    }
+
+    checkCudaErrors(cudaMalloc((void **)&dinput, sizeof(float) * NUM_THREADS * 2));
+    checkCudaErrors(cudaMalloc((void **)&doutput, sizeof(float) * NUM_BLOCKS));
+    checkCudaErrors(cudaMalloc((void **)&dtimer, sizeof(clock_t) * NUM_BLOCKS * 2));
+
+    checkCudaErrors(cudaMemcpy(dinput, input, sizeof(float) * NUM_THREADS * 2, cudaMemcpyHostToDevice));
+
+    timedReduction<<<NUM_BLOCKS, NUM_THREADS, sizeof(float) * 2 * NUM_THREADS>>>(dinput, doutput, dtimer);
+
+    checkCudaErrors(cudaMemcpy(timer, dtimer, sizeof(clock_t) * NUM_BLOCKS * 2, cudaMemcpyDeviceToHost));
+
+    checkCudaErrors(cudaFree(dinput));
+    checkCudaErrors(cudaFree(doutput));
+    checkCudaErrors(cudaFree(dtimer));
+
+    long double avgElapsedClocks = 0;
+
+    for (int i = 0; i < NUM_BLOCKS; i++) {
+        avgElapsedClocks += (long double)(timer[i + NUM_BLOCKS] - timer[i]);
+    }
+
+    avgElapsedClocks = avgElapsedClocks / NUM_BLOCKS;
+    printf("Average clocks/block = %Lf\n", avgElapsedClocks);
+
+    return EXIT_SUCCESS;
+}