PPT-CS 179: GPU Programming Lecture 7 Week 3 Goals: Advanced GPU-

Author : briana-ranney | Published Date : 2019-11-03

CS 179 GPU Programming Lecture 7 Week 3 Goals Advanced GPU accelerable algorithms CUDA libraries and tools This Lecture GPU accelerable algorithms Reduction Prefix

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CS 179: GPU Programming Lecture 7 Week 3 Goals: Advanced GPU-: Transcript


CS 179 GPU Programming Lecture 7 Week 3 Goals Advanced GPU accelerable algorithms CUDA libraries and tools This Lecture GPU accelerable algorithms Reduction Prefix sum Stream compaction Sorting quicksort. 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 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. 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. 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. Topics. Non-numerical algorithms. Parallel breadth-first search (BFS). Texture memory. GPUs – good for many numerical calculations…. What about “non-numerical” problems?. Graph Algorithms. Graph Algorithms. Topics. Non-numerical algorithms. Parallel breadth-first search (BFS). Texture memory. GPUs – good for many numerical calculations…. What about “non-numerical” problems?. Graph Algorithms. Graph Algorithms. 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. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. GeForce 256 (1999) – first GPU. Research Computing Services. Boston . University. GPU Programming. Access to the SCC. Login: . tuta#. Password: . VizTut#. GPU Programming. Access to the SCC GPU nodes. # copy tutorial materials: . The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Recap. Some algorithms are “less obviously parallelizable”:. Reduction. Sorts. FFT (and certain recursive algorithms). Parallel FFT structure (radix-2). Bit-reversed access. http://staff.ustc.edu.cn/~csli/graduate/algorithms/book6/chap32.htm.

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