PPT-JPEG-GPU: a GPGPU Implementation of JPEG Core Coding System

Ang Li University of WisconsinMadison Outline Brief Introduction of Background Implementation Evaluation Conclusion 3202013 2 NVIDIA GTC 2013 Background JPEG Encoding Parallelism Seeking. A CUDA Approach. Gary . Resnick. Scott . Badenhorst. Department of Computer Science. University of Cape Town. 17 March, 2010. Introduction. Approach. Plan. Outcomes. Overview. Radio Astronomy. ITK v4 . . summer . meeting. June 28, 2011. Won-. Ki. . Jeong. Harvard University. Overview. Introduction. Current status. Examples. Future work. 2. GPU Acceleration. GPU as a fast co-processor. Massively parallel. ITS Research Computing. Lani. Clough, Mark Reed. markreed@unc.edu. . Objectives. Introductory. level MATLAB course for people who want to learn . parallel and GPU computing . in MATLAB.. Help participants . Youngho Kim. CIS665: GPU Programming. Building a Million Particle System: Lutz Latta. UberFlow - A GPU-based Particle Engine: Peter Kipfer et al.. Real-Time Particle Systems on the GPU in Dynamic Environments: Shannon Drone. Condor Week 2012. Bob Nordlund. Grid Computing @The Hartford…. Using Condor in our production environment since 2004. Computing Environment. Two pools (Hartford, CT and Boulder, CO). Linux central managers and schedulers. Jared . Coplin. and Martin . Burtscher. Department of Computer Science. 1. Introduction. GPU-based accelerators. Used in high-performance computing. Spreading in PCs and handheld . devices. 2. Power Characteristics of Irregular GPGPU Programs . Graphics Processing Unit (GPU). GPU is the . chip . in computer video cards, PS3, Xbox, . etc. Designed to realize the 3D graphics pipeline. Application .  Geometry  Rasterizer image. GPU development:. 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. . Tor . Aamodt. (UBC, . GPGPUSim. ). Additional material from . NViDia. whitepaper, and others. GPU Computing Architecture. NVIDIA . Tegra. X1 die photo. /. The GPU is Ubiquitous. 2. . [APU13 keynote]. Hui. Li. Geoffrey Fox. Research Goal. provide . a uniform . MapReduce programming . model that works . on HPC . Clusters or . Virtual Clusters cores . on traditional Intel architecture chip, cores on . Coding is what is used to create computer software, apps, and websites.. Examples:. Your operating system (OS). Apps on your phone. Facebook, . Snapchat. , . Instagram. Benefits of Coding. It empowers you to do many things you wouldn’t otherwise be able to do.. On the Noise Level Estimation. INTERIM. . PRESENTATION. SPRING 2015. ADVISOR: Dr. . K.R.Rao. Presented by, . . . Komandla. . Sai. . Venkat. ,. UTA id: 1001115386. 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) Cooperative Computing. Di Wu, Fan Zhang, . Naiyong. . Ao. , Gang Wang, . Xiaoguang. Liu, Jing Liu. Nankai. -Baidu Joint Lab, . Nankai. University. Introduction. Cooperative Model. GPU Batching Algorithm.