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16. OpenCL Example: Image Rotation
16. OpenCL Example: Image Rotation
This video discusses image rotation and how it can be implemented using OpenCL. Each pixel in an image has coordinates and represents a specific color scale, and the rotation involves moving the pixels to a new location based on a formula that takes into account their original and new coordinates and the rotation angle. The speaker proposes assigning each work item to calculate the new position of a single pixel and uses input decomposition to divide the whole global workspace into smaller work groups, making the operation perform more efficiently. The process of transferring an image from the buffer on the device to the buffer on the host is also explained, with emphasis on checking for errors and calculating elapsed time.
17. OpenCL Example Image Rotation Demo
17. OpenCL Example Image Rotation Demo
The "OpenCL Example Image Rotation Demo" tutorial covers the source code of the demo, which includes different folders containing C code and image files that the program will process. The video walks through creating buffers for input and output images, copying the original image to the device buffer, setting kernel arguments, executing the kernel with a global size defined as the whole image, and reading the output data back to the host. The kernel function takes rotation parameters to calculate the new coordinates of each pixel and copy the pixel information to the new location with boundary checking. The program also includes a function to store the rotated image in BMP format and frees all resources after completion. The demo successfully reads and computes the pixels of the original image to create the rotated image.
18. OpenCL Example: Image Convolution
18. OpenCL Example: Image Convolution
The "OpenCL Example: Image Convolution" video explains image convolution, which modifies each pixel in an image using information from its neighboring pixels by applying a filter like a blurring filter. The video provides a seed implementation of the image convolution function and introduces the "image" data structure in OpenCL, which is designed for image data types, allowing for efficient processing on graphics processors. The video shows how to copy image and filter data to the device for image convolution work using OpenCL and the use of the OpenCL sampler object to access the image. The video also demonstrates how to obtain the work item and iterate through the filter rows and columns to obtain pixel information from the image object, multiply them with the filter pixels, and accumulate them to the sum variable. Lastly, the video shows how to update pixel values using an OpenCL image object.
19. Demo: OpenCL Example - Image Convolution
19. Demo: OpenCL Example - Image Convolution
The video explains an OpenCL image convolution example, defining different filters such as blur, sharpen, edge sharpen, detection, and embrace filter. The presenter demonstrates initializing the filter values and reading BMP image data from the file, creating input and output image objects, and setting up kernel arguments to execute the kernel. The video also teaches creating the sampler, defining how to process pixels outside the boundary, launching the kernel, storing the pixel data into a file, and creating the necessary headers for the BMP format. Finally, verifying the results by comparing the values in two buffers to create a filtered image that should match the golden result with only a slight deviation due to floating computation.
20. Lecture 5 OpenCL Concurrency Model
20. Lecture 5 OpenCL Concurrency Model
This lecture covers the OpenCL runtime and concurrency model, including multiple command queues, queuing model, OpenCL kernels work items, and work groups. Synchronization points are used to manage the execution of commands, and wait events are used to synchronize the commands in a device-side command queue. The lecture emphasizes the importance of asynchronous operations in OpenCL and explains the use of events to specify dependencies between commands. The lecturer also discusses the use of callback functions for event completion and highlights the importance of profiling for performance tuning. Additionally, the lecture covers the OpenCL concurrency model for multiple devices in a system, including the pipeline and parallel execution models. Finally, the lecturer demonstrates the implementation of an execution model using kernel events, which allows for parallel execution of different kernels.
The OpenCL concurrency model allows multiple work items to execute independently to improve performance, using work groups with local synchronization to achieve parallelism in execution, but too many work items can cause resource contention. Work items are responsible for maintaining their own program counters, and understanding the problem dimensions and problem sizes is important to design work items that take advantage of GPU processing elements. OpenCL uses workgroup barriers for advanced synchronization among work items, but no mechanisms support synchronization between work items in different workgroups of the same kernel execution. To synchronize work items within the same work group, the barrier API is used, but for synchronization on a global scale, events and wait events are used. The kernel function uses pointers to memory objects in the global and local memories, and local memory accessible to all processing elements can be used for data sharing within the work group. The lecture also covers native kernels, which allow using C functions as kernels on a device without relying on OpenCL compilers, passing OpenCL memory objects to a user function using the in-queue native kernel API, and built-in kernel functions, such as the motion estimation extension for OpenCL, used for image processing to estimate motion between neighboring frames in a video.
Therefore, it's crucial to properly set up dependencies and use the appropriate queue type to avoid potential issues. Additionally, we learn about multiple command queues and how they can be used to improve concurrency in OpenCL programs.
21. Map Reduce Concept
21. Map Reduce Concept
The concept of MapReduce is explained in this video, which involves breaking down large problems into smaller subproblems using a mapping phase followed by a reduction phase. This approach is used by Google to process vast amounts of data on their computers in data centers. The video provides an example of how processors operate independently, assigning processors some data to work on, which produces key-value pairs upon completion. The key-value pairs are then processed by a group of different processors to get the final result in the reduction phase. This approach allows for efficient processing of large datasets by distributing the workload across multiple machines.
22. Map Reduce Example: WordCount and Weblink
22. Map Reduce Example: WordCount and Weblink
This YouTube video demonstrates how MapReduce can be applied to count the occurrences of each word in a large text file and analyze web page URL relationships. MapReduce allows for each processor to target specific keywords independently in the mapping stage, which involves splitting up the document into smaller sections. The reduction stage involves grouping key-value pairs based on the word key and summing up the values to get the total number of appearances for each word. For web page analysis, the mapping process involves creating key-value pairs with the URL as the key and a list of linked web pages as the values, and the reduction stage builds the final map to show the relationship between web pages.
23. Considerations of MapReduce on OpenCL device
23. Considerations of MapReduce on OpenCL device
The discussion in this YouTube video centers on the use of MapReduce on OpenCL devices, with a focus on memory structure, work organization, and local/global reduction. The speaker notes the advantage of leveraging numerous processing elements on OpenCL devices and emphasizes the use of different memory hierarchies while using MapReduce to process large datasets efficiently. They also detail the five steps involved in the use of MapReduce on OpenCL devices, covering the mapping process, local reduction, synchronization of work items, global barriers, and final result production.
24. MapReduce Example: String Search with Demo
24. MapReduce Example: String Search with Demo
The video demonstrates various aspects of OpenCL programming and MapReduce, with a focus on implementing string search. The speaker explains how to declare and allocate memory using the local qualifier and points out that dynamic memory allocation is not allowed in the kernel function. They also introduce vector data types and demonstrate how they can simplify element-wise addition and memory access. The main focus is on implementing string search using MapReduce, where the input text is divided into work items and assigned to a map function to search for a keyword. Each work item carries out this process while comparing the chunks of text with a pattern vector. Local results are obtained by atomic increment to prevent collisions, and the final result is obtained by aggregating the results from each work item. The speaker also provides a detailed explanation of the kernel function, including arguments required and how it is initialized.
25. OpenCL Example: Radix Sort
25. OpenCL Example: Radix Sort
In this video, the concept of radix sort is introduced, which involves dividing a larger sorting problem into smaller subsets based on numerical representation instead of the actual values of the elements being sorted. The speaker demonstrates through an example of sorting eight numbers, sorted by their least significant digit in hexadecimal representation. The OpenCL shuffle and shuffle2 functions are used to efficiently rearrange elements during the sorting process. The video also explains how to perform a shuffle operation using OpenCL and how to use shuffle instructions in the kernel function for radix sorting. Furthermore, the video explores the kernel function called radix sort eight sort eight, which effectively sorts arrays in OpenCL by splitting the input vector into zeros and ones buckets based on the values in its binary digits.