PPT-CULZSS LZSS Lossless Data Compression on CUDA

Adnan Ozsoy amp Martin Swany DAMSL Distributed and MetaSystems Lab Department of Computer Information and Science University of Delaware September 2011 OUTLINE Introduction Background. Burrows and DJ Wheeler Systems Research Center 130 Lytton Avenue Palo Alto California 94301 brPage 2br Systems Research Center The charter of SRC is to advance both the state of knowledge and the state of the art in computer systems From our establi Basically a child CUDA Kernel can be called from within a parent CUDA kernel and then optionally synchronize on the completion of that child CUDA Kernel The parent CUDA kernel can consume the output produced from the child CUDA Kernel all withou t Ohanes. . Dadian. , Danny . Luong. , Yu Lu. Contents. 7.1 Presentation Formatting. 7.1.1 Taxonomy. 7.1.2 Examples. 7.1.3 Markup Languages. 7.2 Multimedia Data. 7.2.1 Lossless Compression Techniques.
          
     !"# $
 By. Dr. . Rajeev Srivastava. 1. Image File Formats. Dr. Rajeev Srivastava. 2. Header field. . Image data format. Image File Formats. A digital image is often encoded in the form of a binary file for the purpose of storage and transmission. . Håkon Kvale . Stensland. Simula Research Laboratory. PC Graphics Timeline. Challenges. :. Render infinitely complex scenes. And extremely high resolution. In 1/60. th. of one second (60 frames per second). OF H.264 . INTRA FRAME CODING, JPEG, JPEG-LS. , JPEG-2000 . AND JPEG-XR. 1. EE . 5359 . Multimedia Project. Amee Solanki . (1000740226. ). amee.solanki@mavs.uta.edu. Image Compression. 2. Compression is the process of compacting data, reducing the number of . Swati . Singhal. . 1. Alan Sussman . The 2nd International Workshop on Data Reduction for Big Scientific . Data. UMIACS and Department of Computer Science. D. ata. reduction is growing concern for scientific computing. Swati . Singhal. . 1. Alan Sussman . The 2nd International Workshop on Data Reduction for Big Scientific . Data. UMIACS and Department of Computer Science. D. ata. reduction is growing concern for scientific computing. Alex Robertson. Outline. History. Lossless . vs. . Lossy. Basics. Huffman Coding. Getting Advanced. Lossy. Explained. Limitations. Future. History, where this all started. The Problem!. 1940s. Shannon-. What is CUDA?. Data Parallelism. Host-Device model. Thread execution. Matrix-multiplication . GPU revised!. What is CUDA?. C. ompute . D. evice . U. nified . A. rchitecture. Programming interface to GPU. Cliff Woolley NVIDIADeveloper Technology GroupGPUCPUGPGPU Revolutionizes ComputingLatency Processor Throughput processorLow Latency or High ThroughputCPUOptimized for low-latency access to cached dat areaDetector. . Slides: Mark . Rivers . GeoSoilEnviroCARS, Advanced Photon Source. University of . Chicago. EPICS meeting, ITER, June 2019. Presenter: Ulrik Kofoed Pedersen, Head of Beamline Controls. Class Rules and Responsibilities. What will be learning?. . 106-Static and Dynamic Visualization. . 105-Synthesize Data for . SciVis. Video-Real Life applications. 6.01: Recognize Digital Image Format.