PPT-© David Kirk/NVIDIA and Wen-

mei W Hwu University of Illinois 20072012 1 CSEE 217 GPU Architecture and Parallel Programming Lecture 12 Parallel Computation Patterns Parallel Prefix Sum Scan Part2 2 Recall a. Why NVIDIA GeForce Motherboard GPUs? NVIDIA DOE Review of MAP (FNAL August 29-31, 2012). 1. The Front End. Harold Kirk. Brookhaven National Lab. August 30, 2012. Outline. Define Front End. Major Sub-systems. Key Challenges. Future R&D Activities. mei. W. . Hwu. , 2007-2012 . University . of Illinois, Urbana-Champaign. 1. CS/EE 217. GPU Architecture and Parallel . Programming. Project . Kickoff. Two flavors. Application. Implement/optimize an realistic application on GPGPUs. University . of Illinois, 2007-2012. CS/EE 217. GPU Architecture and Parallel Programming. Lecture . 6: . DRAM Bandwidth. 1. Objective. To understand DRAM bandwidth. Cause of the DRAM bandwidth problem. and Scholar. s. Symposium to . h. onour. our great friend and colleague on his retirement . from Princeton. AJS Smith. June 17, 2016. Early days: Kirk in Tucson, class of ‘66 . Grad School at Caltech 1966-71. II. GPGPUs. Instructor:Mikko. H . Lipasti. Spring 2015. University of Wisconsin-Madison. Lecture notes based on slides created by John . Shen. , Mark Hill, David Wood, . Guri. . Sohi. , Jim Smith, Natalie Enright . ECE 498AL, University of Illinois, Urbana-Champaign. 1. GPU Programming with CUDA . © David Kirk/NVIDIA and Wen-mei W. Hwu, 2007-2009. ECE 498AL, University of Illinois, Urbana-Champaign. 2. A quiet revolution and potential build-up. 1697 - 1758. 1697. – Kirk, . Obediah. of Stanly County Albemarle Township. 1713. – Kirk, William of Craven County. 1716. – Kirk, Thomas of Chowan County. 1755. – Kirk, Joseph of Granville County (then to Orange and Chatham in1763). 1697 - 1758. 1697. – Kirk, . Obediah. of Stanly County Albemarle Township. 1713. – Kirk, William of Craven County. 1716. – Kirk, Thomas of Chowan County. 1755. – Kirk, Joseph of Granville County (then to Orange and Chatham in1763). and CUDA. Lecture for CPSC 5155. Edward Bosworth, Ph.D.. Computer Science Department. Columbus State University. The Graphics Coprocessor. From the earliest VGA designs, the graphics unit has been designed as a special purpose processor, attached to the CPU using a. heterogeneous programming. Brian Gregor. bgregor@bu.edu. Research Computing Services. Boston University. CUDA C/C BASICS. NVIDIA . Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. Retain performance. CUDA C/C . Based on industry-standard C/C . Small set of extensions to enable heterogeneous programming. mei. W. . Hwu. University of Illinois, 2007-2012. 1. CS/EE 217. GPU Architecture and Parallel Programming. Lecture 15:. Atomic Operations and . Histogramming. - Part 2. 2. Objective. To learn practical histogram programming techniques. CUDA C/C++ Basics Supercomputing 2011 Tutorial Cyril Zeller, NVIDIA Corporation