Changes

   === Assignment 1 ===

<big><presource lang="cpp">

=== Initial Profile (on OpenSUSE Linux at Seneca): ===<big><pre>

</bigpre> === Modified Makefile for Profiling === <source lang="bash"> CIMG_FILES = CImg_demo# Files which requires external libraries to run. CIMG_EXTRA_FILES = use_jpeg_buffer \  gmic_gimp \  gmic CIMG_VERSION = 1.5.4 X11PATH = /usr/X11R6 CC = g++ EXEPFX = CCVER = $(CC) ifeq ($(CC),g++) CCVER = `$(CC) -v 2>&1 | tail -n 1` endif ifeq ($(CC),clang++) CCVER = `$(CC) -v 2>&1 | head -n 1` endif ifeq ($(CC),icc) CCVER = "icc \( `$(CC) -v 2>&1`\)" CFLAGS = -I.. LDFLAGS = else # ADDED PROFILING CALLS HERE # '''CFLAGS = -I.. -Wall -W -O2 -g -pg'''  '''LDFLAGS = -lm -pg''' endif # Flags to enable strict code standards ifeq ($(CC),icc) CIMG_ANSI_CFLAGS = -ansi else CIMG_ANSI_CFLAGS = -ansi -pedantic endif# Flags to enable code debugging. CIMG_DEBUG_CFLAGS = -Dcimg_verbosity=3 -Dcimg_strict_warnings -g  # Flags to enable color output messages. # (requires a VT100 compatible terminal) CIMG_VT100_CFLAGS = -Dcimg_use_vt100  # Flags to enable code optimization by the compiler. ifeq ($(CC),icc) CIMG_OPT_CFLAGS = -O3 -ipo -no-prec-div else CIMG_OPT_CFLAGS = -O3 -fno-tree-pre endif  # Flags to enable OpenMP support. ifeq ($(CC),icc) CIMG_OPENMP_CFLAGS = -Dcimg_use_openmp -openmp -i-static else CIMG_OPENMP_CFLAGS = -Dcimg_use_openmp -fopenmp endif  # Flags to enable OpenCV support. CIMG_OPENCV_CFLAGS = -Dcimg_use_opencv -I/usr/include/opencv CIMG_OPENCV_LDFLAGS = -lcv -lhighgui  # Flags used to disable display capablities of CImg CIMG_NODISPLAY_CFLAGS = -Dcimg_display=0  # Flags to enable the use of the X11 library. # (X11 is used by CImg to handle display windows) # !!! For 64bits systems : replace -L$(X11PATH)/lib by -L$(X11PATH)/lib64 !!! CIMG_X11_CFLAGS = -I$(X11PATH)/include CIMG_X11_LDFLAGS = -L$(X11PATH)/lib -lpthread -lX11  # Flags to enable fast image display, using the XSHM library (when using X11). # !!! Seems to randomly crash when used on MacOSX and 64bits systems, so use it only when necessary !!! CIMG_XSHM_CFLAGS = # -Dcimg_use_xshm CIMG_XSHM_LDFLAGS = # -lXext # Flags to enable GDI32 display (Windows native). CIMG_GDI32_CFLAGS = -mwindows CIMG_GDI32_LDFLAGS = -lgdi32  # Flags to enable screen mode switching, using the XRandr library (when using X11). # ( http://www.x.org/wiki/Projects/XRandR ) # !!! Not supported by the X11 server on MacOSX, so do not use it on MacOSX !!! CIMG_XRANDR_CFLAGS = -Dcimg_use_xrandr CIMG_XRANDR_LDFLAGS = -lXrandr  # Flags to enable native support for PNG image files, using the PNG library. # ( http://www.libpng.org/ ) CIMG_PNG_CFLAGS = -Dcimg_use_png CIMG_PNG_LDFLAGS = -lpng -lz  # Flags to enable native support for JPEG image files, using the JPEG library. # ( http://www.ijg.org/ ) CIMG_JPEG_CFLAGS = -Dcimg_use_jpeg CIMG_JPEG_LDFLAGS = -ljpeg  # Flags to enable native support for TIFF image files, using the TIFF library. # ( http://www.libtiff.org/ ) CIMG_TIFF_CFLAGS = -Dcimg_use_tiff CIMG_TIFF_LDFLAGS = -ltiff  # Flags to enable native support for MINC2 image files, using the MINC2 library. # ( http://en.wikibooks.org/wiki/MINC/Reference/MINC2.0_Users_Guide ) CIMG_MINC2_CFLAGS = -Dcimg_use_minc2 -I${HOME}/local/include CIMG_MINC2_LDFLAGS = -lminc_io -lvolume_io2 -lminc2 -lnetcdf -lhdf5 -lz -L${HOME}/local/lib  # Flags to enable native support for EXR image files, using the OpenEXR library. # ( http://www.openexr.com/ ) CIMG_EXR_CFLAGS = -Dcimg_use_openexr -I/usr/include/OpenEXR CIMG_EXR_LDFLAGS = -lIlmImf -lHalf  # Flags to enable native support for various video files, using the FFMPEG library. # ( http://www.ffmpeg.org/ ) CIMG_FFMPEG_CFLAGS = -Dcimg_use_ffmpeg -D__STDC_CONSTANT_MACROS -I/usr/include/libavcodec -I/usr/include/libavformat -I/usr/include/libswscale -I/usr/include/ffmpeg CIMG_FFMPEG_LDFLAGS = -lavcodec -lavformat -lswscale  # Flags to enable native support for compressed .cimgz files, using the Zlib library. # ( http://www.zlib.net/ ) CIMG_ZLIB_CFLAGS = -Dcimg_use_zlib CIMG_ZLIB_LDFLAGS = -lz  # Flags to enable native support of most classical image file formats, using the Magick++ library. # ( http://www.imagemagick.org/Magick++/ ) CIMG_MAGICK_CFLAGS = -Dcimg_use_magick `Magick++-config --cppflags` `Magick++-config --cxxflags` CIMG_MAGICK_LDFLAGS = `Magick++-config --ldflags` `Magick++-config --libs`  # Flags to enable faster Discrete Fourier Transform computation, using the FFTW3 library # ( http://www.fftw.org/ ) CIMG_FFTW3_CFLAGS = -Dcimg_use_fftw3 ifeq ($(MSYSTEM),MINGW32) CIMG_FFTW3_LDFLAGS = -lfftw3-3 else CIMG_FFTW3_LDFLAGS = -lfftw3 endif  # Flags to enable the use of LAPACK routines for matrix computation # ( http://www.netlib.org/lapack/ ) CIMG_LAPACK_CFLAGS = -Dcimg_use_lapack CIMG_LAPACK_LDFLAGS = -lblas -lg2c -llapack  # Flags to enable the use of the Board library # ( http://libboard.sourceforge.net/ ) CIMG_BOARD_CFLAGS = -Dcimg_use_board -I/usr/include/board CIMG_BOARD_LDFLAGS = -lboard  # Flags to compile on Sun Solaris CIMG_SOLARIS_LDFLAGS = -R$(X11PATH)/lib -lrt -lnsl -lsocket  # Flags to compile GIMP plug-ins. ifeq ($(MSYSTEM),MINGW32) CIMG_GIMP_CFLAGS = -mwindows endif .cpp:  @echo  @echo "** Compiling '$* ($(CIMG_VERSION))' with '$(CCVER)'"  @echo  $(CC) -o $(EXEPFX)$* $< $(CFLAGS) $(CONF_CFLAGS) $(LDFLAGS) $(CONF_LDFLAGS) ifeq ($(STRIP_EXE),true) ifeq ($(MSYSTEM),MINGW32)  strip $(EXEPFX)$*.exe else  strip $(EXEPFX)$* endif endif clean:  rm -rf *.exe *.o *~ \#* $(CIMG_FILES) $(CIMG_EXTRA_FILES) ifneq ($(EXEPFX),)  rm -f $(EXEPFX)* endif # Linux/BSD/Mac OSX targets, with X11 display. linux:  @$(MAKE) \ "CONF_CFLAGS = \ $(CIMG_ANSI_CFLAGS) \ $(CIMG_VT100_CFLAGS) \ $(CIMG_X11_CFLAGS) \ $(CIMG_XSHM_CFLAGS)" \ "CONF_LDFLAGS = \ $(CIMG_X11_LDFLAGS) \ $(CIMG_XSHM_LDFLAGS)" \ all # MacOsX targets, with X11 display. macosx:  @$(MAKE) \ "CONF_CFLAGS = \ $(PROFILE) \ $(PROFILE2) \ $(CIMG_ANSI_CFLAGS) \ $(CIMG_VT100_CFLAGS) \ $(CIMG_X11_CFLAGS)" \ "CONF_LDFLAGS = \ $(CIMG_X11_LDFLAGS)" \ all # Windows targets, with GDI32 display. windows:  @$(MAKE) \ "CONF_CFLAGS = " \ "CONF_LDFLAGS = \ $(CIMG_GDI32_LDFLAGS)" \all</source> === Makefile changes ===  I have made changes to the original Makefile that was provided with this library. I have customized it to compile for profiling by default. A new build can be generate by cleaning the old build (eg: make clean). The source can be compiled by running make <platform> eg: macosx, linux. You will need to download a third party program to allow you to run the 'make' command on Windows but the execution is the same. it can also be run on windows using visual Studio or on the command line("cl.exe must be in the environnent PATH variable"). === Build Instructions === This Makefile and application is cross-platform. make <platform> eg: macosx, linux etc. I have successfully built this application on the following platforms: Windows 7, OpenSuse linux and Mac OSX Lion.  === Summary of Findings === The execution of the program takes roughly 3.10 - 20 seconds (depending on how long you are measuring the calculations of triangle animations). it should be noted that this application initially was an application that relied upon user input for execution and for termination. I have modified this initial behavior by ensuring the while loop (which generates the triangles) executes only for a maximum of 1000 iterations. The time measured in this assignment is for every 1000 iterations of this loop.  === Profiling Results ===The results if the initial profile shows that the execution time is most greatly consumed when drawing the triangles out to the screen one at a time. It seems like this can be optimized by offloading this drawing to n threads based on n triangles to be drawn. But this is subject to change because of any additional complexity that may be introduced that may include interoperability with both the GPU and CPU. There is another for loop which sets the dimensions for each triangle one by one in linear time O(n ). This process can also be out-sourced to the GPU in n threads for n triangles. I would need to determine if this process also involves interoperability between the CPU and GPU. The complexity of the entire program is O(n^3). There is a for loop for setup, a while loop for accepting user input and another for loop for drawing the triangles. Also the times recorded can be increase if the maximum loop iterations increase ie: 10000,100000,1000000. This will identify the same relationship but with higher task time.  === Potential Parallelization Candidates === Upon analyzing this function I discovered two possible areas where I could optimize the code using threads sent to the GPU. The first is a for loop which sets the attributes for 100 triangles in serial. This task can be done in parallel using 100 threads on the GPU.<source lang="cpp"> for (int k = 0; k<100; ++k) {  posx[k] = (float)(cimg::rand()*img0.width());  posy[k] = (float)(cimg::rand()*img0.height());  rayon[k] = (float)(10 + cimg::rand()*50);  angle[k] = (float)(cimg::rand()*360);  veloc[k] = (float)(cimg::rand()*20 - 10);  color[k][0] = (unsigned char)(cimg::rand()*255);  color[k][1] = (unsigned char)(cimg::rand()*255);  color[k][2] = (unsigned char)(cimg::rand()*255);  opacity[k] = (float)(0.3 + 1.5*cimg::rand());  } </pre>   The second instance where this is possible is a bit tricky. It involves another serial for loop. The purpose of this loop is to draw each of the triangles on the screen and manipulate them later on. I am not 100 percent sure this can be done in parallel in practice but in theory it should be possible because the application is drawing out each triangle one by one. <pre> // Draw each triangle on the background image.  for (int k = 0; k<num; ++k) {  const int  x0 = (int)(posx[k] + rayon[k]*std::cos(angle[k]*cimg::PI/180)),  y0 = (int)(posy[k] + rayon[k]*std::sin(angle[k]*cimg::PI/180)),  x1 = (int)(posx[k] + rayon[k]*std::cos((angle[k] + 120)*cimg::PI/180)),  y1 = (int)(posy[k] + rayon[k]*std::sin((angle[k] + 120)*cimg::PI/180)),  x2 = (int)(posx[k] + rayon[k]*std::cos((angle[k] + 240)*cimg::PI/180)),  y2 = (int)(posy[k] + rayon[k]*std::sin((angle[k] + 240)*cimg::PI/180));  if (k%10) img.draw_triangle(x0,y0,x1,y1,x2,y2,color[k],opacity[k]);  else img.draw_triangle(x0,y0,x1,y1,x2,y2,img0,0,0,img0.width()-1,0,0,img.height()-1,opacity[k]);  img.draw_triangle(x0,y0,x1,y1,x2,y2,white,opacity[k],~0U);    // Make the triangles rotate, and check for mouse click event.  // (to make triangles collapse or join).  angle[k]+=veloc[k];  if (disp.mouse_x()>0 && disp.mouse_y()>0) {  float u = disp.mouse_x() - posx[k], v = disp.mouse_y() - posy[k];  if (disp.button()) { u = -u; v = -v; }  posx[k]-=0.03f*u, posy[k]-=0.03f*v;  if (posx[k]<0 || posx[k]>=img.width()) posx[k] = (float)(cimg::rand()*img.width());  if (posy[k]<0 || posy[k]>=img.height()) posy[k] = (float)(cimg::rand()*img.height());  }  }   </source> === Amdahls Law Calculations === Since there are 100 Triangles generated then we can theoretically create 100 threads for each triangle. The draw_line, draw_triangle, and draw_image functions take up 16 percent(0.38 + 0.08 + 0.06 / 3.10) of the execution time of the application in seconds. Plugging that into the equation using 100 cores we get: S100 = 1/ 1 - 0.16 + 0.16 / 100 = 1.18 or 1.2 speedup is theoretically achievable rounded up PER 1000 iterations of the while loop to draw these triangles. Will I work on this Project?If I can optimize this function or any other function within the CImg library I will continue with this project. If it is not possible to optimize this project within the given time of the course then it will be difficult to continue on with this project and I will have to work with someone else's project. But my initial plan is to continue with this project unless I am told otherwise. === Issues Encountered === '''Gprof on MacOSx with Intel processor'''   The profiling tool gprog does not work on the macbooks with an Intel processor installed (I have Intel Core i5). This was verified by numerous internet resources and annoying personal experience.  '''Makefile builds EVERYTHING''' The Makefile shipped with this open source library is programmed to build all of the modules located in CImg_demo.cpp. I had to modify the Makefile to only build the specified function within the library of Image processing functions == Assignment 2 == This assignment involved GPU programming the functions I had speculated I could process concurrently. After further analysis I determined that the second for loop cannot be processed in parallel without converting some underlying API functions to device functions. These functions are used in multiple areas in the framework so I did not pursue changing the framework too much. The first for loop however contained independent data that could be executed in parallel so I went ahead and created kernels for that code. I also noticed that the function made a call to cimg::rand(). This is obviously a function that returns random numbers. Luckily CUDA also has multiple libraries that perform these functions. Off the top of my head: Thrust and CURAND do. I went with CURAND because it has the word "rand" in it and that's what I need. hehe=== Kernels ===<source lang="cpp">  /*  * Setup and initialize curand with a seed  */ __global__ void initCurand(curandState* state){  int idx = blockIdx.x * blockDim.x + threadIdx.x;  curand_init(100, idx, 0, &state[idx]);  __syncthreads(); }   /*  * CUDA kernel that will execute 100 threads in parallel  * and will populate these parallel arrays with 100 random numbers  * array size = 100. */   __global__ void initializeArrays(float* posx, float* posy,float* rayon, float* veloc,  float* opacity ,float* angle, unsigned char* color, int height,  int width, curandState* state, size_t pitch){    int idx = blockIdx.x * blockDim.x + threadIdx.x;  curandState localState = state[idx];    posx[idx] = (float)(curand_normal(&localState)*width);  posy[idx] = (float)(curand_normal(&localState)*height);  rayon[idx] = (float)(10 + curand_normal(&localState)*50);  angle[idx] = (float)(curand_normal(&localState)*360);  veloc[idx] = (float)(curand_uniform(&localState)*20 - 10);  color[idx*pitch] = (unsigned char)(curand_normal(&localState)*255);  color[(idx*pitch)+1] = (unsigned char)(curand_normal(&localState)*255);  color[(idx*pitch)+2] = (unsigned char)(curand_normal(&localState)*255);  opacity[idx] = (float)(0.3f + 1.5f *curand_normal(&localState));    __syncthreads(); } // check for any errors returned by CUDA API functions. void errCheck(cudaError_t err, const char* msg){  if (err != cudaSuccess)  std::cout<< msg << ": " << cudaGetErrorString(err) << std::endl; } </source> === Body Code ===<source lang="cpp">  // check for any errors returned by CUDA API functions. void errCheck(cudaError_t err, const char* msg){  if (err != cudaSuccess)  std::cout<< msg << ": " << cudaGetErrorString(err) << std::endl; }   /*---------------------------    Main procedure    --------------------------*/ int main() {    // Create a colored 640x480 background image which consists of different color shades.  CImg<float> background(640,480,1,3);  cimg_forXY(background,x,y) background.fillC(x,y,0,  x*std::cos(6.0*y/background.height()) + y*std::sin(9.0*x/background.width()),  x*std::sin(8.0*y/background.height()) - y*std::cos(11.0*x/background.width()),  x*std::cos(13.0*y/background.height()) - y*std::sin(8.0*x/background.width()));  background.normalize(0,180);    // Init images and create display window.  CImg<unsigned char> img0(background), img;  unsigned char white[] = { 255, 255, 255 }, color[100][3];  CImgDisplay disp(img0,"[#6] - Filled Triangles (Click to shrink)");  // error handling  cudaError_t err;  // Define random properties (pos, size, colors, ..) for all triangles that will be displayed.  float posx[100];  float posy[100];  float rayon[100];  float angle[100];  float veloc[100];  float opacity[100];  // Define the same properties but for the device  float* d_posx;  float* d_posy;  float* d_rayon;  float* d_angle;  float* d_veloc;  float* d_opacity;  unsigned char* d_color;    // CURAND state  curandState* devState;    // allocate memory on the device for the device arrays  err = cudaMalloc((void**)&d_posx, 100 * sizeof(float));  errCheck(err, "cudaMalloc((void**)&d_posx, 100 * sizeof(float))");  err = cudaMalloc((void**)&d_posy, 100 * sizeof(float));  errCheck(err,"cudaMalloc((void**)&d_posy, 100 * sizeof(float))");  err = cudaMalloc((void**)&d_rayon, 100 * sizeof(float));  errCheck(err,"cudaMalloc((void**)&d_rayon, 100 * sizeof(float))");  err = cudaMalloc((void**)&d_angle, 100 * sizeof(float));  errCheck(err,"cudaMalloc((void**)&d_angle, 100 * sizeof(float))");  err = cudaMalloc((void**)&d_veloc, 100 * sizeof(float));  errCheck(err,"cudaMalloc((void**)&d_veloc, 100 * sizeof(float))");  err = cudaMalloc((void**)&d_opacity, 100 * sizeof(float));  errCheck(err,"cudaMalloc((void**)&d_opacity, 100 * sizeof(float))");  err = cudaMalloc((void**)&devState, 100*sizeof(curandState));  errCheck(err,"cudaMalloc((void**)&devState, 100*sizeof(curandState))");  size_t pitch;  //allocated the device memory for source array   err = cudaMallocPitch(&d_color, &pitch, 3 * sizeof(unsigned char),100);  errCheck(err,"cudaMallocPitch(&d_color, &pitch, 3 * sizeof(unsigned char),100)");  // launch grid of threads  dim3 dimBlock(100);  dim3 dimGrid(1);    /* Kernel for initializing CURAND */  initCurand<<<1,100>>>(devState);    // synchronize the device and the host  cudaDeviceSynchronize();    /*Kernel for initializing Arrays */  initializeArrays<<<1, 100>>>(d_posx, d_posy, d_rayon, d_veloc, d_opacity, d_angle,  d_color, img0.height(), img0.width(), devState, pitch);  // synchronize the device and the host  cudaDeviceSynchronize();    // get the populated arrays back to the host for use  err = cudaMemcpy(posx,d_posx, 100 * sizeof(float), cudaMemcpyDeviceToHost);  errCheck(err,"cudaMemcpy(posx,d_posx, 100 * sizeof(float), cudaMemcpyDeviceToHost)");  err = cudaMemcpy(posy,d_posy, 100 * sizeof(float), cudaMemcpyDeviceToHost);  errCheck(err,"cudaMemcpy(posy,d_posy, 100 * sizeof(float), cudaMemcpyDeviceToHost)");  err = cudaMemcpy(rayon,d_rayon, 100 * sizeof(float), cudaMemcpyDeviceToHost);  errCheck(err,"cudaMemcpy(rayon,d_rayon, 100 * sizeof(float), cudaMemcpyDeviceToHost)");  err = cudaMemcpy(veloc,d_veloc, 100 * sizeof(float), cudaMemcpyDeviceToHost);  errCheck(err,"cudaMemcpy(veloc,d_veloc, 100 * sizeof(float), cudaMemcpyDeviceToHost)");  err = cudaMemcpy(opacity,d_opacity, 100 * sizeof(float), cudaMemcpyDeviceToHost);  errCheck(err,"cudaMemcpy(opacity,d_opacity, 100 * sizeof(float), cudaMemcpyDeviceToHost)");  err = cudaMemcpy(angle,d_angle, 100 * sizeof(float), cudaMemcpyDeviceToHost);  errCheck(err,"cudaMemcpy(angle,d_angle, 100 * sizeof(float), cudaMemcpyDeviceToHost)");  // pitch of color array is 3+1 padded  err = cudaMemcpy2D(color,4,d_color,pitch,3 *sizeof(unsigned char),3, cudaMemcpyDeviceToHost);  errCheck(err,"cudaMemcpy2D(color,pitch,d_color,100*3,3 *sizeof(unsigned char),100* sizeof(unsigned char), cudaMemcpyDeviceToHost)");  // measuring time it takes for triangle animations in 1000 iterations  int i = 0, num = 1;    // Start animation loop.  while (!disp.is_closed() && !disp.is_keyQ() && !disp.is_keyESC() && i < 1000) {  img = img0;    i++;  // Draw each triangle on the background image.  for (int k = 0; k<num; ++k) {  const int  x0 = (int)(posx[k] + rayon[k]*std::cos(angle[k]*cimg::PI/180)),  y0 = (int)(posy[k] + rayon[k]*std::sin(angle[k]*cimg::PI/180)),  x1 = (int)(posx[k] + rayon[k]*std::cos((angle[k] + 120)*cimg::PI/180)),  y1 = (int)(posy[k] + rayon[k]*std::sin((angle[k] + 120)*cimg::PI/180)),  x2 = (int)(posx[k] + rayon[k]*std::cos((angle[k] + 240)*cimg::PI/180)),  y2 = (int)(posy[k] + rayon[k]*std::sin((angle[k] + 240)*cimg::PI/180));  if (k%10) img.draw_triangle(x0,y0,x1,y1,x2,y2,color[k],opacity[k]);  else img.draw_triangle(x0,y0,x1,y1,x2,y2,img0,0,0,img0.width()-1,0,0,img.height()-1,opacity[k]);  img.draw_triangle(x0,y0,x1,y1,x2,y2,white,opacity[k],~0U);    // Make the triangles rotate, and check for mouse click event.  // (to make triangles collapse or join).  angle[k]+=veloc[k];  if (disp.mouse_x()>0 && disp.mouse_y()>0) {  float u = disp.mouse_x() - posx[k], v = disp.mouse_y() - posy[k];  if (disp.button()) { u = -u; v = -v; }  posx[k]-=0.03f*u, posy[k]-=0.03f*v;  if (posx[k]<0 || posx[k]>=img.width()) posx[k] = (float)(cimg::rand()*img.width());  if (posy[k]<0 || posy[k]>=img.height()) posy[k] = (float)(cimg::rand()*img.height());  }  }    // Display current animation framerate, and refresh display window.  img.draw_text(5,5,"%u frames/s",white,0,0.5f,13,(unsigned int)disp.frames_per_second());  img0.resize(disp.display(img).resize(false).wait(20));  if (++num>100) num = 100;    // Allow the user to toggle fullscreen mode, by pressing CTRL+F.  if (disp.is_keyCTRLLEFT() && disp.is_keyF()) disp.resize(640,480,false).toggle_fullscreen(false);  }    // free allocated device memory  cudaFree(d_posy);  cudaFree(d_posx);  cudaFree(d_rayon);  cudaFree(d_veloc);  cudaFree(d_opacity);  cudaFree(d_color);  cudaFree(d_angle);  cudaFree(devState);  return 0; }  </source> === Profiling Results ===The entirety of the GPU code executes in 0.15 - 0.16 seconds. I profiled the application using nvvp and NSight on Visual Studio 2010.=== Issues Encountered === Makefile does not work on the Windows platform. Tried adding the -lcurand linker flag but to no avail. The code does compile cleanly without error on Visual Studio 2010 IDE. Coding issues: Stackoverflow Questions I made: [http://stackoverflow.com/questions/15245723/using-arrayij-in-a-cuda-kernel-memcpy-call Using 2D Arrays] [http://stackoverflow.com/questions/15238009/concurrently-initializing-many-arrays-with-random-numbers-using-curand-and-cuda Memory Issues]    With the help of the open-source community I was able to solve my problems and gain a better understanding of CUDA and CURAND.

=== Assignment 2 ====== Assignment 3 ===