PPT-Ameliorating Memory Contention of OLAP Operators on GPU Processors

Evangelia A Sitaridi Kenneth A Ross Columbia University DaMoN Workshop 21st May 2012 Introduction 12 Earlier GPU implementations of data processing operators have resulted in significant speedups. ca Abstract Contention for shared resources on multicore processors remains an unsolved problem in existing systems despite signi64257cant re search efforts dedicated to this problem in the past Previous solu tions focused primarily on hardware techn Contents . Vector processor. Vector instructions. Vector pipelines. Scalar pipeline execution. Vector pipeline execution. Symbolic processors. Attributes. Characteristics. Vector Processors. A vector processor is specially designed to perform vector computations.. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Acknowledgements. CPU slides – Varun Sampath, NVIDIA. GPU . slides. Kayvon . Fatahalian. , CMU. Mike Houston, . NVIDIA. CPU and GPU Trends. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). [Think – 2 minutes]. . [Pair – Tell each other what you think]. . . . [share]. [contention]. The . main contention . states the central point or argument of a piece of text or speech. Questions to consider. What is the central message the author is trying to convey to his/her audience?. What is the main point the author is trying to convince me of?. 1. Look at the title.. What is the message it is conveying?. Alma 3. Alma 3. Mosiah 2. Wages. Civil War- Atonement and Contention. 3 Nephi 11. Elder Bednar. Interestingly, the admonition to “be ye therefore perfect” is immediately preceded by counsel about how we should act in response to wrongdoing and offense. Clearly, the rigorous requirements that lead to the perfecting of the Saints include assignments that test and challenge us. . Evangelia A. Sitaridi, Kenneth A. Ross. Columbia University. DaMoN Workshop. 21st May 2012. . Introduction (1/2). Earlier GPU implementations of data processing operators have resulted in significant speedups. 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. Operator. . Meaning. Example. Definition. .. Addition. x = 6 2;. Add the values on either side of . -. Subtraction. x = 6 - 2;. Subtract right value from left value. *. Multiplication. x = 6 * 2;. Department of Geography and Planning. University at Albany. What is a GPU?. A GPU is a . graphics processing unit. Modern GPUs are composed of multiple processors. Each of these processors can perform operations similar to those of CPUs. 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 . Report . in Argos. Agenda. What is the Degree Audit . OLAP Report. ? . OLAP: On-Line . Analytical Processing . Accessing Argos . Running the Degree Audit . OLAP Report. . Interpreting the . Report Data. 2/8/2018. Introduction to Advanced Processors. 1. Outline . Features. Internal Architecture of 80286. Interrupts of . 80286. Signal Description of . 80286. Real And Protected Mode. Instruction set. 2/8/2018.