cudaNvSci: Update README.md fixing typo (#337)

Fixes #193

Samples/2_Concepts_and_Techniques/boxFilter/boxFilter.cpp

@@ -77,7 +77,6 @@ int filter_radius = 14;
 int nthreads = 64;
 unsigned int width, height;
 unsigned int *h_img = NULL;
-unsigned int *d_img = NULL;
 unsigned int *d_temp = NULL;
 
 GLuint pbo;                                      // OpenGL pixel buffer object
@@ -108,11 +107,11 @@ extern "C" void computeGold(float *id, float *od, int w, int h, int n);
 // These are CUDA functions to handle allocation and launching the kernels
 extern "C" void initTexture(int width, int height, void *pImage, bool useRGBA);
 extern "C" void freeTextures();
-extern "C" double boxFilter(float *d_src, float *d_temp, float *d_dest,
+extern "C" double boxFilter(float *d_temp, float *d_dest,
                             int width, int height, int radius, int iterations,
                             int nthreads, StopWatchInterface *timer);
 
-extern "C" double boxFilterRGBA(unsigned int *d_src, unsigned int *d_temp,
+extern "C" double boxFilterRGBA(unsigned int *d_temp,
                                 unsigned int *d_dest, int width, int height,
                                 int radius, int iterations, int nthreads,
                                 StopWatchInterface *timer);
@@ -165,7 +164,7 @@ void display() {
   size_t num_bytes;
   checkCudaErrors(cudaGraphicsResourceGetMappedPointer(
       (void **)&d_result, &num_bytes, cuda_pbo_resource));
-  boxFilterRGBA(d_img, d_temp, d_result, width, height, filter_radius,
+  boxFilterRGBA(d_temp, d_result, width, height, filter_radius,
                 iterations, nthreads, kernel_timer);
 
   checkCudaErrors(cudaGraphicsUnmapResources(1, &cuda_pbo_resource, 0));
@@ -282,11 +281,7 @@ void reshape(int x, int y) {
 }
 
 void initCuda(bool useRGBA) {
-  // allocate device memory
-  checkCudaErrors(
-      cudaMalloc((void **)&d_img, (width * height * sizeof(unsigned int))));
-  checkCudaErrors(
-      cudaMalloc((void **)&d_temp, (width * height * sizeof(unsigned int))));
+  checkCudaErrors(cudaMalloc((void **)&d_temp, (width * height * sizeof(unsigned int))));
 
   // Refer to boxFilter_kernel.cu for implementation
   initTexture(width, height, h_img, useRGBA);
@@ -304,11 +299,6 @@ void cleanup() {
     h_img = NULL;
   }
 
-  if (d_img) {
-    cudaFree(d_img);
-    d_img = NULL;
-  }
-
   if (d_temp) {
     cudaFree(d_temp);
     d_temp = NULL;
@@ -413,7 +403,7 @@ int runBenchmark() {
       cudaMalloc((void **)&d_result, width * height * sizeof(unsigned int)));
 
   // warm-up
-  boxFilterRGBA(d_img, d_temp, d_temp, width, height, filter_radius, iterations,
+  boxFilterRGBA(d_temp, d_temp, width, height, filter_radius, iterations,
                 nthreads, kernel_timer);
   checkCudaErrors(cudaDeviceSynchronize());
 
@@ -426,7 +416,7 @@ int runBenchmark() {
 
   for (int i = 0; i < iCycles; i++) {
     dProcessingTime +=
-        boxFilterRGBA(d_img, d_temp, d_img, width, height, filter_radius,
+        boxFilterRGBA(d_temp, d_temp, width, height, filter_radius,
                       iterations, nthreads, kernel_timer);
   }
 
@@ -469,7 +459,7 @@ int runSingleTest(char *ref_file, char *exec_path) {
   {
     printf("%s (radius=%d) (passes=%d) ", sSDKsample, filter_radius,
            iterations);
-    boxFilterRGBA(d_img, d_temp, d_result, width, height, filter_radius,
+    boxFilterRGBA(d_temp, d_result, width, height, filter_radius,
                   iterations, nthreads, kernel_timer);
 
     // check if kernel execution generated an error

Samples/2_Concepts_and_Techniques/boxFilter/boxFilter_kernel.cu

@@ -399,7 +399,6 @@ extern "C" void freeTextures() {
     Perform 2D box filter on image using CUDA
 
     Parameters:
-    d_src  - pointer to input image in device memory
     d_temp - pointer to temporary storage in device memory
     d_dest - pointer to destination image in device memory
     width  - image width
@@ -408,7 +407,7 @@ extern "C" void freeTextures() {
     iterations - number of iterations
 
 */
-extern "C" double boxFilter(float *d_src, float *d_temp, float *d_dest,
+extern "C" double boxFilter(float *d_temp, float *d_dest,
                             int width, int height, int radius, int iterations,
                             int nthreads, StopWatchInterface *timer) {
   // var for kernel timing
@@ -447,7 +446,7 @@ extern "C" double boxFilter(float *d_src, float *d_temp, float *d_dest,
 }
 
 // RGBA version
-extern "C" double boxFilterRGBA(unsigned int *d_src, unsigned int *d_temp,
+extern "C" double boxFilterRGBA(unsigned int *d_temp,
                                 unsigned int *d_dest, int width, int height,
                                 int radius, int iterations, int nthreads,
                                 StopWatchInterface *timer) {

Samples/2_Concepts_and_Techniques/sortingNetworks/README.md

@@ -2,7 +2,7 @@
 
 ## Description
 
-This sample implements bitonic sort and odd-even merge sort (also known as Batcher's sort), algorithms belonging to the class of sorting networks. While generally subefficient, for large sequences compared to algorithms with better asymptotic algorithmic complexity (i.e. merge sort or radix sort), this may be the preferred algorithms of choice for sorting batches of short-sized to mid-sized (key, value) array pairs. Refer to an excellent tutorial by H. W. Lang http://www.iti.fh-flensburg.de/lang/algorithmen/sortieren/networks/indexen.htm
+This sample implements bitonic sort and odd-even merge sort (also known as Batcher's sort), algorithms belonging to the class of sorting networks. While generally subefficient, for large sequences compared to algorithms with better asymptotic algorithmic complexity (i.e. merge sort or radix sort), this may be the preferred algorithms of choice for sorting batches of short-sized to mid-sized (key, value) array pairs. Refer to an excellent tutorial by H. W. Lang https://hwlang.de/algorithmen/sortieren/bitonic/bitonicen.htm
 
 ## Key Concepts
 

Samples/3_CUDA_Features/memMapIPCDrv/memMapIpc.cpp

@@ -493,12 +493,14 @@ static void parentProcess(char *app) {
       continue;
     }
 
-    for (int j = 0; j < nprocesses; j++) {
+    for (int j = 0; j < selectedDevices.size(); j++) {
       int canAccessPeerIJ, canAccessPeerJI;
-      checkCudaErrors(
-          cuDeviceCanAccessPeer(&canAccessPeerJI, devices[j], devices[i]));
-      checkCudaErrors(
-          cuDeviceCanAccessPeer(&canAccessPeerIJ, devices[i], devices[j]));
+      checkCudaErrors(cuDeviceCanAccessPeer(&canAccessPeerJI, 
+                                            devices[selectedDevices[j]], 
+                                            devices[i]));
+      checkCudaErrors(cuDeviceCanAccessPeer(&canAccessPeerIJ, 
+                                            devices[i], 
+                                            devices[selectedDevices[j]]));
       if (!canAccessPeerIJ || !canAccessPeerJI) {
         allPeers = false;
         break;
@@ -513,10 +515,10 @@ static void parentProcess(char *app) {
       // setup the peers for the device.  For systems that only allow 8
       // peers per GPU at a time, this acts to remove devices from CanAccessPeer
       for (int j = 0; j < nprocesses; j++) {
-        checkCudaErrors(cuCtxSetCurrent(ctxs[i]));
+        checkCudaErrors(cuCtxSetCurrent(ctxs.back()));
         checkCudaErrors(cuCtxEnablePeerAccess(ctxs[j], 0));
         checkCudaErrors(cuCtxSetCurrent(ctxs[j]));
-        checkCudaErrors(cuCtxEnablePeerAccess(ctxs[i], 0));
+        checkCudaErrors(cuCtxEnablePeerAccess(ctxs.back(), 0));
       }
       selectedDevices.push_back(i);
       nprocesses++;

Samples/4_CUDA_Libraries/conjugateGradient/main.cpp

@@ -231,6 +231,10 @@ int main(int argc, char **argv) {
     }
   }
 
+  if (buffer) {
+    checkCudaErrors(cudaFree(buffer));
+  }
+  
   cusparseDestroy(cusparseHandle);
   cublasDestroy(cublasHandle);
   if (matA) {

Samples/4_CUDA_Libraries/cudaNvSci/README.md

@@ -2,7 +2,7 @@
 
 ## Description
 
-This sample demonstrates CUDA-NvSciBuf/NvSciSync Interop. Two CPU threads import the NvSciBuf and NvSciSync into CUDA to perform two image processing algorithms on a ppm image - image rotation in 1st thread & rgba to grayscale conversion of rotated image in 2nd thread. Currently only supported on Ubuntu 18.04
+This sample demonstrates CUDA-NvSciBuf/NvSciSync Interop. Two CPU threads import the NvSciBuf and NvSciSync into CUDA to perform two image processing algorithms on a ppm image - image rotation in 1st thread & rgba to grayscale conversion of rotated image in 2nd thread. Currently only supported on Ubuntu 18.04
 
 ## Key Concepts