PPT-CS 179: GPU Programming Lecture 7 Last Week Memory optimizations using different GPU

CS 179 GPU Programming Lecture 7 Last Week Memory optimizations using different GPU caches Atomic operations Synchronization with syncthreads Week 3 Advanced GPUaccelerable algorithms Reductions to parallelize problems that dont seem intuitively parallelizable. Memory Memory is a property relev ant o nly to systems whose input and output signals have the same independent variable A system is said to be memoryless if its output for each value of the independent variable is dependent only on the input signa Justin Hsia. 7/22/2013. Summer 2013 -- Lecture #16. 1. CS 61C: Great Ideas in . Computer Architecture. Amdahl’s Law,. Thread Level Parallelism. 1. st. Half in Review. Write bigger, talk slower. Students afraid/too lost to . Hakim Weatherspoon. CS 3410, Spring 2011. Computer Science. Cornell University. P & H . Chapter. 5.4-5. Announcements. PA3 . available. Due . Tuesday, April 19. th. Work with . pairs. Be responsible with new knowledge. ECE . 751. Brian Coutinho. ,. David Schlais. ,. Gokul Ravi. &. Keshav . Mathur . Summary. Fact. : Accelerators gaining popularity - to improve performance and energy efficiency. Problem. : Accelerators with scratchpads require DMA calls to satisfy memory requests (among other overheads). Cache overview. 4 Hierarchy questions. More on Locality. Please bring these slides to the next lecture!. Projects 2 and 3. Regrade. issues for 3. Please resubmit and come to office hours with a diff.. Research at UNT. . . Krishna . Kavi. Professor. Director of NSF Industry/University Cooperative Center . for Net-Centric Software and Systems (Net-Centric IUCRC). Computer Science and Engineering. The University of North Texas. Tyler Patton. Discussion:. Background. Sequential Optimizations. Parallelization of chess. Backgroun. d. : What is Chess?. Strategic 2 player game. 64 tiles. 16 pieces per player. Objective to capture the. Hakim Weatherspoon. CS 3410, Spring 2013. Computer Science. Cornell University. P & H Chapter . 5.2-3, 5.5. Goals for Today: caches. Writing . to the Cache. Write-through . vs. Write-back. Cache Parameter Tradeoffs. CS 3410, Spring 2011. Computer Science. Cornell University. See P&H . 5.2 (writes), 5.3, 5.5. Announcements. HW3 available due . next. Tuesday . HW3 has been updated. . Use updated version.. Work with . 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) ECE . 751. Brian Coutinho. ,. David Schlais. ,. Gokul Ravi. &. Keshav . Mathur . Summary. Fact. : Accelerators gaining popularity - to improve performance and energy efficiency. Problem. : Accelerators with scratchpads require DMA calls to satisfy memory requests (among other overheads). CS 3410, Spring 2012. Computer Science. Cornell University. P & H Chapter 5.4 . Goals for Today. Virtual Memory. Address Translation. Pages, page tables, and memory . mgmt. unit. Paging. Role of Operating System. T. Chen, T. Moreau, Z. Jiang, L. Zheng, S. Jiao, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. . Ceze. , C. . Guestrin. , and A. Krishnamurthy . Presentation by Grzegorz . Keshav . Harisrikanth. , . Srijan. Chakraborty, Sean Ngo. Architecture Concepts. Instruction Set Architecture (ISA). Performance, Energy, Power. Memory Hierarchy. Caches. Virtual Memory (VM). Pipelines and Hazards.