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. 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 heterogeneous programming. Katia Oleinik. koleinik@bu.edu. Scientific Computing and Visualization. Boston . University. Architecture. NVIDIA Tesla M2070: . Core clock: 1.15GHz . Single instruction . 448 CUDA cores . . 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. Microwave Engineering . Lecture 12. . Power Dividers and Directional Couplers. 2. 2. Power dividers and directional couplers . . Passive components that are used for power division or combining. . The coupler may be a three-port or a four-port component. © Dan Negrut, . 2012. UW-Madison. Dan Negrut. Simulation-Based Engineering Lab. Wisconsin Applied Computing Center. Department of Mechanical Engineering. Department of . Electrical and Computer Engineering. at 75 Gb/s on a GPU. Molly A. O’Neil and Martin Burtscher. Department of Computer Science. Introduction. Scientific simulations on HPC clusters. Run on interconnected compute nodes. P. roduce . and transfer lots of . Martin Burtscher. Department of Computer Science. High-End CPUs and GPUs. Xeon X7550 Tesla C2050. Cores 8 (superscalar) 448 (simple). Active threads 2 per core 48 per core. Frequency 2 GHz 1.15 GHz. Normal Forms. Given . a design, how do we know it is good or not? . What is the . best. design?. Can a bad design be transformed into a good one? . Conceptual design. Schemas. ICs. Normalization. A relation is said to be in a particular normal form if it satisfies a certain set of constraints.. introduce the use of multiple CUDA streams to overlap memory transfers with kernel computations.. Also introduced is paged-locked memory. 2. Page-locked host memory. (also called pinned host memory). 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. Gennady Pekhimenko. § . Vivek Seshadri. §. Onur Mutlu. . §. . Michael A. Kozuch. †. Phillip B. Gibbons. † . Todd C. Mowry. . §. § . Carnegie Mellon University . † . Intel Labs Pittsburgh. 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. Ryan . LeFebre. Terminated Lossless Line. The ratio of voltage to current at z=0 must be Z. L. , to satisfy this waves may be reflected. Terminated Lossless Line. Total voltage on the line:. Total current on the line:.