PPT-Overview of Ocelot: architecture

Overview GPU Ocelot overview Building configuring and executing Ocelot programs Ocelot Device Interface and CUDA Runtime API Ocelot PTX Internal Representation PTX Pass Manager 2 Ocelot Multiplatform Dynamic Compilation. OCELOT FACT SHEE T KINGDOM: Animalia PHYLUM: Chordata CLASS: Mammalia ORDER: Carnivora FAMILY: Felidae GENUS: Leopardus SPECIES: L. pardalis Felis pardalis, from Latin pardalis , "leopard - like") is BRAZILIAN OCELOT t psuan azmost tsn ysars auo wwtv a swmpzs quss twon: “It ws ars uownu to mawntawn ocszots wn zoos, svouzrn’t tvsy ps rsprsssntatwvs ot tvs wwzr popu zatwonM” Tvs 1. Overview. 2. Ocelot PTX Emulator. Multicore-Backend. NVIDIA GPU Backend. AMD GPU Backend. Execution Model . Parallel thread Execution (PTX). Explicit memory hierarchy. Cooperative thread arrays (CTAs) and ordering constraints. By Michelle Celliers. Contents. Jaguar . Ocelot. Toco Toucan . Poisonous Dart Frog. Jaguar. The jaguar. . is one of the most dangerous animals in the Amazon.. . An excellent hunter. They can climb high trees and run after its prey. This animal is about 6 feet long and can reach up to 250 pounds in weight. It lives on the first layer ( the forest floor ) of the rainforest but hunt on the understory.. Overview. Ocelot PTX . Emulator. Multicore-Backend. NVIDIA GPU Backend. AMD GPU Backend. 2. Overview. Ocelot PTX . Emulator. Multicore-Backend. NVIDIA GPU Backend. AMD GPU Backend. 3. Multicore CPU Backend: Introduction. Sergey Tomin. other co-workers: . I. Agapov, G. . Geloni, I. . Zagorodnov. Motivation. How it works. Recent results of empirical tuning at FLASH (model-free optimization). OCELOT . features in beam dynamics simulations . Created by Natalie Evans. The Ocelot is Amazing For 3 Reasons. What it looks like. It’s speed. What it eats. What it looks like. The ocelot has a pink nose and large clear eyes. It is a medium-sized wild cat that is 4 feet long. It’s fur color ranges from reddish-yellow to smoky white.. Overview. Ocelot PTX . Emulator. Multicore-Backend. NVIDIA GPU Backend. AMD GPU Backend. 2. Multicore CPU Backend: Introduction. Target: Efficient execution of PTX kernels on CPUs. ISA Translation from PTX to LLVM. Sergey Tomin. Machine Learning Applications for Particle Accelerators. SLAC, 28.02.2018. Outline. Introduction . Generic Optimizer. Adaptive Feedback. Machine Learning at the European XFEL. S2e simulations in the control room . Hakim . Weatherspoon. Assistant Professor, . Dept. of Computer Science. CS 5413: High Performance Systems and Networking. September 15, 2014. Slides used . and adapted judiciously . from Computer Networking, A Top-Down Approach. . uppgifter. Architecture notebook. Mycket. . intensivt. elaboration – inception. Mål. : en . stabil. . arkitektur. . i. . slutet. . på. elaboration. Samarbetar. med . alla. under . projektets. Sergey Tomin. other co-workers: . I. Agapov, G. . Geloni, I. . Zagorodnov. Motivation. How it works. Recent results of empirical tuning at FLASH (model-free optimization). OCELOT . features in beam dynamics simulations . 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 . Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2013. Lectures. Monday and Wednesday. 6-7:30pm. Towne . 307. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures.