PPT-GPU and CPU Parallelization of Honest-but-Curious Secure Tw

Nathaniel Husted Steve Myers abhi shelat Paul Grubbs Alice and Bob want to compute a public function of their private inputs Secure Twoparty Computation Disease Database Alice. Avg Access Time 2 Tokens Number of Controllers Average Access Time clock cyles brPage 16br Number of Tokens vs Avg Access Time 9 Controllers Number of Tokens Average Access Time clock cycles brPage 17br brPage 18br Dr A . Sahu. Dept of Comp Sc & . Engg. . . IIT . Guwahati. 1. Outline. Graphics System . GPU Architecture. Memory Model. Vertex Buffer, Texture buffer. GPU Programming Model. DirectX. , OpenGL, . Rajat Phull, . Srihari. Cadambi, Nishkam Ravi and Srimat Chakradhar. NEC Laboratories America. Princeton, New Jersey, USA.. www.nec-labs.com. OpenFOAM Overview. OpenFOAM stands for:. ‘. O. pen . F. By Devin Riley. The Sneaky Cat-. Strengths- . Executes . under pressure, good liar, quiet . Weaknesses- non-confrontational, immature, . Defensive Dog-. Strengths- keen sense of smell, optimist. Weaknesses- optimist, gullible, lacks self-confidence. H.A. Rey. Curious. Interested in knowing about something. The cat was curious about the butterfly.. Which goes with curious?. Curious. What would a curious student say about some new tools in a work station?. Konstantinos Theodorakos. January 2015. Modern Processor Design. “Free lunch is over”. Lower Power consumption is favored on multi-core/processor architectures. CBD Parallelization . intention. Why Parallelize CBD models?. ECE 751, Fall 2015. Peng . Liu. 1. Overview. What? JavaScript . Engine optimization. How? Light-weight . software speculation mechanism. 2. [1] Heine. , David, et al. Software and hardware for exploiting speculative parallelism with a multiprocessor. Computer Systems Laboratory, Stanford University, 1997. Interested in knowing about something. The cat was curious about the butterfly.. Which goes with curious?. Curious. What would a curious student say about some new tools in a work station?. Turn and Talk; share out. Alex Wade. CAP6938 Final Project. Introduction. GPU based implementation of . A Computational Approach to Edge Detection. by John Canny. Paper presents an accurate, localized edge detection method. Purpose. 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. Prediction markets & real-world data feeds. Assertions about the outside world . Idea. : add a mechanism to assert facts. election outcomes. sports results. commodity prices. Bet or hedge results using smart contracts. 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) Paris, 2016-01-26. 2. Contents. Introduction . Brief review of ongoing IAC Adaptive Optics projects. Summary of control technologies used . Technologies comparison . C. onclusions. 3. Contents. Introduction. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos.