PPT-CS 179: GPU Programming Lecture 9 / Homework 3

Recap Some algorithms are less obviously parallelizable Reduction Sorts FFT and certain recursive algorithms Parallel FFT structure radix2 Bitreversed access httpstaffustceducncsligraduatealgorithmsbook6chap32htm. Dr A . Sahu. Dept of Comp Sc & . Engg. . . IIT . Guwahati. 1. Outline. Graphics System . GPU Architecture. Memory Model. Vertex Buffer, Texture buffer. GPU Programming Model. DirectX. , OpenGL, . using BU Shared Computing Cluster. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). Host-Device Data Transfer. 1. Moving data is slow. So far we’ve only considered performance when the data is already on the GPU. This neglects the slowest part of GPU programming: getting data on and off of GPU. Host-Device Data Transfer. 1. Moving data is slow. So far we’ve only considered performance when the data is already on the GPU. This neglects the slowest part of GPU programming: getting data on and off of GPU. Lecture 5: GPU Compute . Architecture. 1. Last time.... GPU Memory System. Different kinds of memory pools, caches, . etc. Different optimization techniques. 2. Warp Schedulers. Warp schedulers find a warp that is ready to execute its next instruction and available execution cores and then start execution. Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. Department of Geography and Planning. University at Albany. What is a GPU?. A GPU is a . graphics processing unit. Modern GPUs are composed of multiple processors. Each of these processors can perform operations similar to those of CPUs. Week 3. Goals:. More involved GPU-. accelerable. algorithms. Relevant hardware quirks. CUDA libraries. Outline. GPU-accelerated:. Reduction. Prefix sum. Stream compaction. Sorting (quicksort). Reduction. Lecture 7. Last Week. Memory optimizations using different GPU caches. Atomic operations. Synchronization with __. syncthreads. (). Week 3. Advanced GPU-accelerable algorithms. “Reductions” to parallelize problems that don’t seem intuitively parallelizable. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Lectures. Monday. 6-9pm. Moore 212. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. Image from . Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2013. Lectures. Monday and Wednesday. 6-7:30pm. Towne . 307. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand