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/4_CUDA_Libraries/simpleCUBLASXT/.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/4_CUDA_Libraries/simpleCUBLASXT/.vscode/extensions.json

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

cpp/4_CUDA_Libraries/simpleCUBLASXT/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.20)
+
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules")
+
+project(simpleCUBLASXT LANGUAGES CXX)
+
+find_package(CUDAToolkit REQUIRED)
+
+set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+
+set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120)
+set(CMAKE_CUDA_FLAGS "${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 simpleCUBLASXT
+add_executable(simpleCUBLASXT simpleCUBLASXT.cpp)
+
+target_compile_options(simpleCUBLASXT PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>)
+
+target_compile_features(simpleCUBLASXT PRIVATE cxx_std_17 cuda_std_17)
+
+set_target_properties(simpleCUBLASXT PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
+
+target_include_directories(simpleCUBLASXT PRIVATE
+    ${CUDAToolkit_INCLUDE_DIRS}
+)
+
+target_link_libraries(simpleCUBLASXT PRIVATE
+    CUDA::cudart
+    CUDA::cublas
+)
+
+# Include installation configuration
+include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake)
+setup_samples_install()

cpp/4_CUDA_Libraries/simpleCUBLASXT/README.md

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+# simpleCUBLASXT - Simple CUBLAS XT
+
+## Description
+
+Example of using CUBLAS-XT library which performs GEMM operations over Multiple GPUs.
+
+## Key Concepts
+
+CUBLAS-XT Library
+
+## 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, aarch64
+
+## CUDA APIs involved
+
+### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
+cudaGetDeviceProperties, cudaGetDeviceCount, cudaFree
+
+## Dependencies needed to build/run
+[CUBLAS](../../../README.md#cublas)
+
+## Prerequisites
+
+Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
+Make sure the dependencies mentioned in [Dependencies]() section above are installed.
+
+## References (for more details)

cpp/4_CUDA_Libraries/simpleCUBLASXT/simpleCUBLASXT.cpp

@@ -0,0 +1,308 @@
+/* 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 demonstrates how to use the CUBLAS library
+ * by scaling an array of floating-point values on the device
+ * and comparing the result to the same operation performed
+ * on the host.
+ */
+
+/* Includes, system */
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+/* Includes, cuda */
+#include <cublasXt.h>
+#include <cuda_runtime.h>
+#include <helper_cuda.h>
+
+/* Matrix size */
+// #define N  (275)
+#define N (1024)
+// Restricting the max used GPUs as input matrix is not so large
+#define MAX_NUM_OF_GPUS 2
+
+/* Host implementation of a simple version of sgemm */
+static void simple_sgemm(int n, float alpha, const float *A, const float *B, float beta, float *C)
+{
+    int i;
+    int j;
+    int k;
+
+    for (i = 0; i < n; ++i) {
+        for (j = 0; j < n; ++j) {
+            float prod = 0;
+
+            for (k = 0; k < n; ++k) {
+                prod += A[k * n + i] * B[j * n + k];
+            }
+
+            C[j * n + i] = alpha * prod + beta * C[j * n + i];
+        }
+    }
+}
+
+void findMultipleBestGPUs(int &num_of_devices, int *device_ids)
+{
+    // Find the best CUDA capable GPU device
+    int current_device = 0;
+
+    int device_count;
+    checkCudaErrors(cudaGetDeviceCount(&device_count));
+    typedef struct gpu_perf_t
+    {
+        uint64_t compute_perf;
+        int      device_id;
+    } gpu_perf;
+
+    gpu_perf *gpu_stats = (gpu_perf *)malloc(sizeof(gpu_perf) * device_count);
+
+    cudaDeviceProp deviceProp;
+    int            devices_prohibited = 0;
+    int            computeMode;
+    int            clockRate;
+
+    while (current_device < device_count) {
+        cudaGetDeviceProperties(&deviceProp, current_device);
+        checkCudaErrors(cudaDeviceGetAttribute(&computeMode, cudaDevAttrComputeMode, current_device));
+        checkCudaErrors(cudaDeviceGetAttribute(&clockRate, cudaDevAttrClockRate, current_device));
+        // If this GPU is not running on Compute Mode prohibited,
+        // then we can add it to the list
+        int sm_per_multiproc;
+        if (computeMode != cudaComputeModeProhibited) {
+            if (deviceProp.major == 9999 && deviceProp.minor == 9999) {
+                sm_per_multiproc = 1;
+            }
+            else {
+                sm_per_multiproc = _ConvertSMVer2Cores(deviceProp.major, deviceProp.minor);
+            }
+
+            gpu_stats[current_device].compute_perf =
+                (uint64_t)deviceProp.multiProcessorCount * sm_per_multiproc * clockRate;
+            gpu_stats[current_device].device_id = current_device;
+        }
+        else {
+            devices_prohibited++;
+        }
+
+        ++current_device;
+    }
+    if (devices_prohibited == device_count) {
+        fprintf(stderr,
+                "gpuGetMaxGflopsDeviceId() CUDA error:"
+                " all devices have compute mode prohibited.\n");
+        exit(EXIT_FAILURE);
+    }
+    else {
+        gpu_perf temp_elem;
+        // Sort the GPUs by highest compute perf.
+        for (int i = 0; i < current_device - 1; i++) {
+            for (int j = 0; j < current_device - i - 1; j++) {
+                if (gpu_stats[j].compute_perf < gpu_stats[j + 1].compute_perf) {
+                    temp_elem        = gpu_stats[j];
+                    gpu_stats[j]     = gpu_stats[j + 1];
+                    gpu_stats[j + 1] = temp_elem;
+                }
+            }
+        }
+
+        for (int i = 0; i < num_of_devices; i++) {
+            device_ids[i] = gpu_stats[i].device_id;
+        }
+    }
+    free(gpu_stats);
+}
+
+/* Main */
+int main(int argc, char **argv)
+{
+    cublasStatus_t   status;
+    float           *h_A;
+    float           *h_B;
+    float           *h_C;
+    float           *h_C_ref;
+    float           *d_A   = 0;
+    float           *d_B   = 0;
+    float           *d_C   = 0;
+    float            alpha = 1.0f;
+    float            beta  = 0.0f;
+    int              n2    = N * N;
+    int              i;
+    float            error_norm;
+    float            ref_norm;
+    float            diff;
+    cublasXtHandle_t handle;
+    int             *devices = NULL;
+
+    int num_of_devices = 0;
+
+    checkCudaErrors(cudaGetDeviceCount(&num_of_devices));
+
+    if (num_of_devices > MAX_NUM_OF_GPUS) {
+        num_of_devices = MAX_NUM_OF_GPUS;
+    }
+    devices = (int *)malloc(sizeof(int) * num_of_devices);
+
+    findMultipleBestGPUs(num_of_devices, devices);
+    cudaDeviceProp deviceProp;
+    printf("Using %d GPUs\n", num_of_devices);
+    for (i = 0; i < num_of_devices; i++) {
+        checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devices[i]));
+        printf("GPU ID = %d, Name = %s \n", devices[i], deviceProp.name);
+    }
+
+    /* Initialize CUBLAS */
+    printf("simpleCUBLASXT test running..\n");
+
+    status = cublasXtCreate(&handle);
+
+    if (status != CUBLAS_STATUS_SUCCESS) {
+        fprintf(stderr, "!!!! CUBLASXT initialization error\n");
+        return EXIT_FAILURE;
+    }
+
+    /* Select devices for use in CUBLASXT math functions */
+    status = cublasXtDeviceSelect(handle, num_of_devices, devices);
+
+    if (status != CUBLAS_STATUS_SUCCESS) {
+        fprintf(stderr, "!!!! CUBLASXT device selection error\n");
+        return EXIT_FAILURE;
+    }
+
+    /* Optional: Set a block size for CUBLASXT math functions */
+    status = cublasXtSetBlockDim(handle, 64);
+
+    if (status != CUBLAS_STATUS_SUCCESS) {
+        fprintf(stderr, "!!!! CUBLASXT set block dimension error\n");
+        return EXIT_FAILURE;
+    }
+
+    /* Allocate host memory for the matrices */
+    h_A = (float *)malloc(n2 * sizeof(h_A[0]));
+
+    if (h_A == 0) {
+        fprintf(stderr, "!!!! host memory allocation error (A)\n");
+        return EXIT_FAILURE;
+    }
+
+    h_B = (float *)malloc(n2 * sizeof(h_B[0]));
+
+    if (h_B == 0) {
+        fprintf(stderr, "!!!! host memory allocation error (B)\n");
+        return EXIT_FAILURE;
+    }
+
+    h_C_ref = (float *)malloc(n2 * sizeof(h_C[0]));
+
+    if (h_C_ref == 0) {
+        fprintf(stderr, "!!!! host memory allocation error (C_ref)\n");
+        return EXIT_FAILURE;
+    }
+
+    h_C = (float *)malloc(n2 * sizeof(h_C[0]));
+
+    if (h_C == 0) {
+        fprintf(stderr, "!!!! host memory allocation error (C)\n");
+        return EXIT_FAILURE;
+    }
+
+    /* Fill the matrices with test data */
+    for (i = 0; i < n2; i++) {
+        h_A[i]     = rand() / (float)RAND_MAX;
+        h_B[i]     = rand() / (float)RAND_MAX;
+        h_C[i]     = rand() / (float)RAND_MAX;
+        h_C_ref[i] = h_C[i];
+    }
+
+    /* Performs operation using plain C code */
+    simple_sgemm(N, alpha, h_A, h_B, beta, h_C_ref);
+
+    /* Performs operation using cublas */
+    status = cublasXtSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, N, N, N, &alpha, h_A, N, h_B, N, &beta, h_C, N);
+
+    if (status != CUBLAS_STATUS_SUCCESS) {
+        fprintf(stderr, "!!!! kernel execution error.\n");
+        return EXIT_FAILURE;
+    }
+
+    /* Check result against reference */
+    error_norm = 0;
+    ref_norm   = 0;
+
+    for (i = 0; i < n2; ++i) {
+        diff = h_C_ref[i] - h_C[i];
+        error_norm += diff * diff;
+        ref_norm += h_C_ref[i] * h_C_ref[i];
+    }
+
+    error_norm = (float)sqrt((double)error_norm);
+    ref_norm   = (float)sqrt((double)ref_norm);
+
+    if (fabs(ref_norm) < 1e-7) {
+        fprintf(stderr, "!!!! reference norm is 0\n");
+        return EXIT_FAILURE;
+    }
+
+    /* Memory clean up */
+    free(h_A);
+    free(h_B);
+    free(h_C);
+    free(h_C_ref);
+
+    if (cudaFree(d_A) != cudaSuccess) {
+        fprintf(stderr, "!!!! memory free error (A)\n");
+        return EXIT_FAILURE;
+    }
+
+    if (cudaFree(d_B) != cudaSuccess) {
+        fprintf(stderr, "!!!! memory free error (B)\n");
+        return EXIT_FAILURE;
+    }
+
+    if (cudaFree(d_C) != cudaSuccess) {
+        fprintf(stderr, "!!!! memory free error (C)\n");
+        return EXIT_FAILURE;
+    }
+
+    /* Shutdown */
+    status = cublasXtDestroy(handle);
+
+    if (status != CUBLAS_STATUS_SUCCESS) {
+        fprintf(stderr, "!!!! shutdown error (A)\n");
+        return EXIT_FAILURE;
+    }
+
+    if (error_norm / ref_norm < 1e-6f) {
+        printf("simpleCUBLASXT test passed.\n");
+        exit(EXIT_SUCCESS);
+    }
+    else {
+        printf("simpleCUBLASXT test failed.\n");
+        exit(EXIT_FAILURE);
+    }
+}