PPT-Memory Optimizations

Research at UNT Krishna Kavi Professor Director of NSF IndustryUniversity Cooperative Center for NetCentric Software and Systems NetCentric IUCRC Computer Science and Engineering The University of North Texas. 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 Intel. ® . performance tools.. Andrei Anufrienko. Intel Compiler Group. The objectives of this course. :. Get a basic understanding of. : . the main factors of the processor performance,. base performance improvement techniques,. : Rethinking the Multicore Memory Hierarchy. for Disciplined Parallelism. Byn Choi, Rakesh Komuravelli, . Hyojin Sung. , . Robert Smolinski, . Nima. . Honarmand. , . Sarita. V. Adve, . Vikram. S. Adve, Nicholas P. Carter, Ching-Tsun Chou. The material in these slides has been taken from the NVIDIA manuals (Best Practices Guide & Optimizing Matrix Transpose in CUDA), and from a paper by . Ryoo. et al [Ryoo12]. See "A bit of History" in the last slide. Chapter . 2. Memory Hierarchy Design. Computer Architecture. A Quantitative Approach, Fifth Edition. Copyright © 2012, Elsevier Inc. All rights reserved.. Introduction. Programmers want unlimited amounts of memory with low latency. Nuno Lopes . and. José Monteiro. Deriving preconditions by hand is hard; WPs are often non-trivial. WPs derived by hand are often wrong!. Weaker preconditions expose more optimization opportunities. in . Dyninst. Andrew . Bernat. , Matthew Legendre. Instrumentation is Complicated. User perspective: . “Insert some new code here, here, and here.”. Dyninst’s. perspective:. Relocation. – Move code to make space for instrumentation. and High-Coverage Tests. Presented by Yoni Leibowitz. EXE & KLEE. Automatically Generating Inputs of Death. David Dill. Vijay . Ganesh. Cristian Cadar. Dawson Engler. Peter Pawlowski. KLEE. EXE. Unassisted & Automatic Generation of High-Coverage Tests for Complex System Programs. Chapter . 2. Memory Hierarchy Design. Computer Architecture. A Quantitative Approach. , Sixth Edition. Copyright © 2019, Elsevier Inc. All rights Reserved. Introduction. Programmers want unlimited amounts of memory with low latency. Linchuan. . Chen. Advisor: Dr. . Gagan. Agrawal. Motivation - Platforms. Accelerators are Evolving. General Purpose Graphics Processing Units (GPGPU). Extreme-scale, cost-effective, power efficient. CS 179: GPU Programming Lecture 7 Last Week Memory optimizations using different GPU caches Atomic operations Synchronization with __ syncthreads () Week 3 Advanced GPU-accelerable algorithms “Reductions” to parallelize problems that don’t seem intuitively parallelizable 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 . Nuno Lopes . and. José Monteiro. Deriving preconditions by hand is hard; WPs are often non-trivial. WPs derived by hand are often wrong!. Weaker preconditions expose more optimization opportunities. and High-Coverage Tests. Presented by Yoni Leibowitz. EXE & KLEE. Automatically Generating Inputs of Death. David Dill. Vijay . Ganesh. Cristian Cadar. Dawson Engler. Peter Pawlowski. KLEE. EXE. Unassisted & Automatic Generation of High-Coverage Tests for Complex System Programs.