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/simpleCUBLAS_LU/.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/simpleCUBLAS_LU/.vscode/extensions.json

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

cpp/4_CUDA_Libraries/simpleCUBLAS_LU/CMakeLists.txt

@@ -0,0 +1,43 @@
+cmake_minimum_required(VERSION 3.20)
+
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules")
+
+project(simpleCUBLAS_LU LANGUAGES CXX)
+
+find_package(CUDAToolkit REQUIRED)
+
+set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+
+set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120)
+set(CMAKE_CUDA_FLAGS "${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 simpleCUBLAS_LU
+add_executable(simpleCUBLAS_LU simpleCUBLAS_LU.cpp)
+
+target_compile_options(simpleCUBLAS_LU PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>)
+
+target_compile_features(simpleCUBLAS_LU PRIVATE cxx_std_17 cuda_std_17)
+
+set_target_properties(simpleCUBLAS_LU PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
+
+target_include_directories(simpleCUBLAS_LU PRIVATE
+    ${CUDAToolkit_INCLUDE_DIRS}
+)
+
+target_link_libraries(simpleCUBLAS_LU PRIVATE
+    CUDA::cudart
+    CUDA::cublas
+)
+
+# Include installation configuration
+include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake)
+setup_samples_install()

cpp/4_CUDA_Libraries/simpleCUBLAS_LU/README.md

@@ -0,0 +1,36 @@
+# simpleCUBLAS_LU - Simple CUBLAS LU
+
+## Description
+
+CUDA sample demonstrating cuBLAS API cublasDgetrfBatched() for lower-upper (LU) decomposition of a matrix.
+
+## Key Concepts
+
+CUBLAS Library, LU decomposition
+
+## 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)
+cudaGetErrorEnum, cudaMalloc, cudaMemcpy, 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/simpleCUBLAS_LU/simpleCUBLAS_LU.cpp

@@ -0,0 +1,418 @@
+/* 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 API
+ * for lower-upper (LU) decomposition of a matrix. LU decomposition
+ * factors a matrix as the product of upper triangular matrix and
+ * lower trianglular matrix.
+ *
+ * https://en.wikipedia.org/wiki/LU_decomposition
+ *
+ * This sample uses 10000 matrices of size 4x4 and performs
+ * LU decomposition of them using batched decomposition API
+ * of cuBLAS library. To test the correctness of upper and lower
+ * matrices generated, they are multiplied and compared with the
+ * original input matrix.
+ *
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+
+// cuda libraries and helpers
+#include <cublas_v2.h>
+#include <cuda_runtime.h>
+#include <helper_cuda.h>
+
+// configurable parameters
+// dimension of matrix
+#define N          4
+#define BATCH_SIZE 10000
+
+// use double precision data type
+#define DOUBLE_PRECISION /* comment this to use single precision */
+#ifdef DOUBLE_PRECISION
+#define DATA_TYPE double
+#define MAX_ERROR 1e-15
+#else
+#define DATA_TYPE float
+#define MAX_ERROR 1e-6
+#endif /* DOUBLE_PRCISION */
+
+// use pivot vector while decomposing
+#define PIVOT /* comment this to disable pivot use */
+
+// helper functions
+
+// wrapper around cublas<t>getrfBatched()
+cublasStatus_t
+cublasXgetrfBatched(cublasHandle_t handle, int n, DATA_TYPE *const A[], int lda, int *P, int *info, int batchSize)
+{
+#ifdef DOUBLE_PRECISION
+    return cublasDgetrfBatched(handle, n, A, lda, P, info, batchSize);
+#else
+    return cublasSgetrfBatched(handle, n, A, lda, P, info, batchSize);
+#endif
+}
+
+// wrapper around malloc
+// clears the allocated memory to 0
+// terminates the program if malloc fails
+void *xmalloc(size_t size)
+{
+    void *ptr = malloc(size);
+    if (ptr == NULL) {
+        printf("> ERROR: malloc for size %zu failed..\n", size);
+        exit(EXIT_FAILURE);
+    }
+    memset(ptr, 0, size);
+    return ptr;
+}
+
+// initalize identity matrix
+void initIdentityMatrix(DATA_TYPE *mat)
+{
+    // clear the matrix
+    memset(mat, 0, N * N * sizeof(DATA_TYPE));
+
+    // set all diagonals to 1
+    for (int i = 0; i < N; i++) {
+        mat[(i * N) + i] = 1.0;
+    }
+}
+
+// initialize matrix with all elements as 0
+void initZeroMatrix(DATA_TYPE *mat) { memset(mat, 0, N * N * sizeof(DATA_TYPE)); }
+
+// fill random value in column-major matrix
+void initRandomMatrix(DATA_TYPE *mat)
+{
+    for (int i = 0; i < N; i++) {
+        for (int j = 0; j < N; j++) {
+            mat[(j * N) + i] = (DATA_TYPE)1.0 + ((DATA_TYPE)rand() / (DATA_TYPE)RAND_MAX);
+        }
+    }
+
+    // diagonal dominant matrix to insure it is invertible matrix
+    for (int i = 0; i < N; i++) {
+        mat[(i * N) + i] += (DATA_TYPE)N;
+    }
+}
+
+// print column-major matrix
+void printMatrix(DATA_TYPE *mat)
+{
+    for (int i = 0; i < N; i++) {
+        for (int j = 0; j < N; j++) {
+            printf("%20.16f ", mat[(j * N) + i]);
+        }
+        printf("\n");
+    }
+    printf("\n");
+}
+
+// matrix mulitplication
+void matrixMultiply(DATA_TYPE *res, DATA_TYPE *mat1, DATA_TYPE *mat2)
+{
+    initZeroMatrix(res);
+
+    for (int i = 0; i < N; i++) {
+        for (int j = 0; j < N; j++) {
+            for (int k = 0; k < N; k++) {
+                res[(j * N) + i] += mat1[(k * N) + i] * mat2[(j * N) + k];
+            }
+        }
+    }
+}
+
+// check matrix equality
+bool checkRelativeError(DATA_TYPE *mat1, DATA_TYPE *mat2, DATA_TYPE maxError)
+{
+    DATA_TYPE err         = (DATA_TYPE)0.0;
+    DATA_TYPE refNorm     = (DATA_TYPE)0.0;
+    DATA_TYPE relError    = (DATA_TYPE)0.0;
+    DATA_TYPE relMaxError = (DATA_TYPE)0.0;
+
+    for (int i = 0; i < N * N; i++) {
+        refNorm = abs(mat1[i]);
+        err     = abs(mat1[i] - mat2[i]);
+
+        if (refNorm != 0.0 && err > 0.0) {
+            relError    = err / refNorm;
+            relMaxError = MAX(relMaxError, relError);
+        }
+
+        if (relMaxError > maxError)
+            return false;
+    }
+    return true;
+}
+
+// decode lower and upper matrix from single matrix
+// returned by getrfBatched()
+void getLUdecoded(DATA_TYPE *mat, DATA_TYPE *L, DATA_TYPE *U)
+{
+    // init L as identity matrix
+    initIdentityMatrix(L);
+
+    // copy lower triangular values from mat to L (skip diagonal)
+    for (int i = 0; i < N; i++) {
+        for (int j = 0; j < i; j++) {
+            L[(j * N) + i] = mat[(j * N) + i];
+        }
+    }
+
+    // init U as all zero
+    initZeroMatrix(U);
+
+    // copy upper triangular values from mat to U
+    for (int i = 0; i < N; i++) {
+        for (int j = i; j < N; j++) {
+            U[(j * N) + i] = mat[(j * N) + i];
+        }
+    }
+}
+
+// generate permutation matrix from pivot vector
+void getPmatFromPivot(DATA_TYPE *Pmat, int *P)
+{
+    int pivot[N];
+
+    // pivot vector in base-1
+    // convert it to base-0
+    for (int i = 0; i < N; i++) {
+        P[i]--;
+    }
+
+    // generate permutation vector from pivot
+    // initialize pivot with identity sequence
+    for (int k = 0; k < N; k++) {
+        pivot[k] = k;
+    }
+
+    // swap the indices according to pivot vector
+    for (int k = 0; k < N; k++) {
+        int q = P[k];
+
+        // swap pivot(k) and pivot(q)
+        int s    = pivot[k];
+        int t    = pivot[q];
+        pivot[k] = t;
+        pivot[q] = s;
+    }
+
+    // generate permutation matrix from pivot vector
+    initZeroMatrix(Pmat);
+    for (int i = 0; i < N; i++) {
+        int j             = pivot[i];
+        Pmat[(j * N) + i] = (DATA_TYPE)1.0;
+    }
+}
+
+int main(int argc, char **argv)
+{
+    // cuBLAS variables
+    cublasStatus_t status;
+    cublasHandle_t handle;
+
+    // host variables
+    size_t matSize = N * N * sizeof(DATA_TYPE);
+
+    DATA_TYPE *h_AarrayInput;
+    DATA_TYPE *h_AarrayOutput;
+    DATA_TYPE *h_ptr_array[BATCH_SIZE];
+
+    int *h_pivotArray;
+    int *h_infoArray;
+
+    // device variables
+    DATA_TYPE  *d_Aarray;
+    DATA_TYPE **d_ptr_array;
+
+    int *d_pivotArray;
+    int *d_infoArray;
+
+    int err_count = 0;
+
+    // seed the rand() function with time
+    srand(12345);
+
+    // find cuda device
+    printf("> initializing..\n");
+    int dev = findCudaDevice(argc, (const char **)argv);
+    if (dev == -1) {
+        return (EXIT_FAILURE);
+    }
+
+    // initialize cuBLAS
+    status = cublasCreate(&handle);
+    if (status != CUBLAS_STATUS_SUCCESS) {
+        printf("> ERROR: cuBLAS initialization failed..\n");
+        return (EXIT_FAILURE);
+    }
+
+#ifdef DOUBLE_PRECISION
+    printf("> using DOUBLE precision..\n");
+#else
+    printf("> using SINGLE precision..\n");
+#endif
+
+#ifdef PIVOT
+    printf("> pivot ENABLED..\n");
+#else
+    printf("> pivot DISABLED..\n");
+#endif
+
+    // allocate memory for host variables
+    h_AarrayInput  = (DATA_TYPE *)xmalloc(BATCH_SIZE * matSize);
+    h_AarrayOutput = (DATA_TYPE *)xmalloc(BATCH_SIZE * matSize);
+
+    h_pivotArray = (int *)xmalloc(N * BATCH_SIZE * sizeof(int));
+    h_infoArray  = (int *)xmalloc(BATCH_SIZE * sizeof(int));
+
+    // allocate memory for device variables
+    checkCudaErrors(cudaMalloc((void **)&d_Aarray, BATCH_SIZE * matSize));
+    checkCudaErrors(cudaMalloc((void **)&d_pivotArray, N * BATCH_SIZE * sizeof(int)));
+    checkCudaErrors(cudaMalloc((void **)&d_infoArray, BATCH_SIZE * sizeof(int)));
+    checkCudaErrors(cudaMalloc((void **)&d_ptr_array, BATCH_SIZE * sizeof(DATA_TYPE *)));
+
+    // fill matrix with random data
+    printf("> generating random matrices..\n");
+    for (int i = 0; i < BATCH_SIZE; i++) {
+        initRandomMatrix(h_AarrayInput + (i * N * N));
+    }
+
+    // copy data to device from host
+    printf("> copying data from host memory to GPU memory..\n");
+    checkCudaErrors(cudaMemcpy(d_Aarray, h_AarrayInput, BATCH_SIZE * matSize, cudaMemcpyHostToDevice));
+
+    // create pointer array for matrices
+    for (int i = 0; i < BATCH_SIZE; i++)
+        h_ptr_array[i] = d_Aarray + (i * N * N);
+
+    // copy pointer array to device memory
+    checkCudaErrors(cudaMemcpy(d_ptr_array, h_ptr_array, BATCH_SIZE * sizeof(DATA_TYPE *), cudaMemcpyHostToDevice));
+
+    // perform LU decomposition
+    printf("> performing LU decomposition..\n");
+#ifdef PIVOT
+    status = cublasXgetrfBatched(handle, N, d_ptr_array, N, d_pivotArray, d_infoArray, BATCH_SIZE);
+#else
+    status = cublasXgetrfBatched(handle, N, d_ptr_array, N, NULL, d_infoArray, BATCH_SIZE);
+#endif /* PIVOT */
+    if (status != CUBLAS_STATUS_SUCCESS) {
+        printf("> ERROR: cublasDgetrfBatched() failed with error %s..\n", _cudaGetErrorEnum(status));
+        return (EXIT_FAILURE);
+    }
+
+    // copy data to host from device
+    printf("> copying data from GPU memory to host memory..\n");
+    checkCudaErrors(cudaMemcpy(h_AarrayOutput, d_Aarray, BATCH_SIZE * matSize, cudaMemcpyDeviceToHost));
+    checkCudaErrors(cudaMemcpy(h_infoArray, d_infoArray, BATCH_SIZE * sizeof(int), cudaMemcpyDeviceToHost));
+#ifdef PIVOT
+    checkCudaErrors(cudaMemcpy(h_pivotArray, d_pivotArray, N * BATCH_SIZE * sizeof(int), cudaMemcpyDeviceToHost));
+#endif /* PIVOT */
+
+    // verify the result
+    printf("> verifying the result..\n");
+    for (int i = 0; i < BATCH_SIZE; i++) {
+        if (h_infoArray[i] == 0) {
+            DATA_TYPE *A  = h_AarrayInput + (i * N * N);
+            DATA_TYPE *LU = h_AarrayOutput + (i * N * N);
+            DATA_TYPE  L[N * N];
+            DATA_TYPE  U[N * N];
+            getLUdecoded(LU, L, U);
+
+            // test P * A = L * U
+            int      *P = h_pivotArray + (i * N);
+            DATA_TYPE Pmat[N * N];
+#ifdef PIVOT
+            getPmatFromPivot(Pmat, P);
+#else
+            initIdentityMatrix(Pmat);
+#endif /* PIVOT */
+
+            // perform matrix multiplication
+            DATA_TYPE PxA[N * N];
+            DATA_TYPE LxU[N * N];
+            matrixMultiply(PxA, Pmat, A);
+            matrixMultiply(LxU, L, U);
+
+            // check for equality of matrices
+            if (!checkRelativeError(PxA, LxU, (DATA_TYPE)MAX_ERROR)) {
+                printf("> ERROR: accuracy check failed for matrix number %05d..\n", i + 1);
+                err_count++;
+            }
+        }
+        else if (h_infoArray[i] > 0) {
+            printf("> execution for matrix %05d is successful, but U is singular and "
+                   "U(%d,%d) = 0..\n",
+                   i + 1,
+                   h_infoArray[i] - 1,
+                   h_infoArray[i] - 1);
+        }
+        else // (h_infoArray[i] < 0)
+        {
+            printf("> ERROR: matrix %05d have an illegal value at index %d = %lf..\n",
+                   i + 1,
+                   -h_infoArray[i],
+                   *(h_AarrayInput + (i * N * N) + (-h_infoArray[i])));
+        }
+    }
+
+    // free device variables
+    checkCudaErrors(cudaFree(d_ptr_array));
+    checkCudaErrors(cudaFree(d_infoArray));
+    checkCudaErrors(cudaFree(d_pivotArray));
+    checkCudaErrors(cudaFree(d_Aarray));
+
+    // free host variables
+    if (h_infoArray)
+        free(h_infoArray);
+    if (h_pivotArray)
+        free(h_pivotArray);
+    if (h_AarrayOutput)
+        free(h_AarrayOutput);
+    if (h_AarrayInput)
+        free(h_AarrayInput);
+
+    // destroy cuBLAS handle
+    status = cublasDestroy(handle);
+    if (status != CUBLAS_STATUS_SUCCESS) {
+        printf("> ERROR: cuBLAS uninitialization failed..\n");
+        return (EXIT_FAILURE);
+    }
+
+    if (err_count > 0) {
+        printf("> TEST FAILED for %d matrices, with precision: %g\n", err_count, MAX_ERROR);
+        return (EXIT_FAILURE);
+    }
+
+    printf("> TEST SUCCESSFUL, with precision: %g\n", MAX_ERROR);
+    return (EXIT_SUCCESS);
+}