PPT-CS 179: GPU Programming

Author : tatiana-dople | Published Date : 2018-01-15

Lecture 5 GPU Compute Architecture 1 Last time GPU Memory System Different kinds of memory pools caches etc Different optimization techniques 2 Warp Schedulers

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CS 179: GPU Programming: Transcript

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. . Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin (. Negrut. ), North Carolina Charlotte (. Wikinson. 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. Sathish. . Vadhiyar. Parallel Programming. GPU. Graphical Processing Unit. A single GPU consists of large number of cores – hundreds of cores.. Whereas a single CPU can consist of 2, 4, 8 or 12 cores. 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. 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. 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. Week 3. Goals:. More involved GPU-. accelerable. algorithms. Relevant hardware quirks. CUDA libraries. Outline. GPU-accelerated:. Reduction. Prefix sum. Stream compaction. Sorting (quicksort). Reduction. Martin Burtscher. Department of Computer Science. High-end CPU-GPU Comparison. . Xeon 8180M. . Titan V. Cores 28 5120 (+ 640). Active threads 2 per core 32 per core. Frequency 2.5 (3.8) GHz 1.2 (1.45) GHz. 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 . 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

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