PPT-Shared memory

Shared memory Process A Process B Physical Memory Virtual address space A Virtual address space B Share Share Share Instruction memory Data Instruction memory Data Create a shared memory region. †. : Rethinking Hardware for Disciplined Parallelism. Byn Choi, Rakesh Komuravelli, Hyojin Sung, . Rob Bocchino, Sarita Adve, Vikram Adve. Other collaborators:. Languages: Adam Welc, Tatiana Shpeisman, Yang Ni (Intel). Performance considerations. (CUDA best practices) . NVIDIA CUDA C programming best practices guide. ACK: CUDA teaching center Stanford (. Hoberrock. and . Tarjan. ).. Outline. Host to device memory transfer. : 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. AMANO, Hideharu. Textbook pp.60-68. Fork-join: Starting and finishing parallel processes. fork. Usually, these processes (threads). can share variables. fork. join. join. Fork/Join is a way of synchronization. Getting Righteous Performance from your GPU. Optimizing on CPUs. Could I be getting better performance?. Probably a little . bit, but most of it is hidden from the user.. How . much better?. If you compile –O3, you can get faster (maybe 2x). A Complete GPU Compute Architecture by NVIDIA. Tamal. . Saha. , . Abhishek. . Rawat. , Minh Le. {ts4rq, ar8eb, ml4nw}@. virginia.edu. GPU Computing. Unprecedented FP performance. Ideal for data parallel applications. 1. COMET: Code Offload by. Migrating Execution Transparently. OSDI'12. Mark Gordon, Anoushe Jamshidi,. Scott Mahlke, Z. Morley Mao, and Xu Chen. University of Michigan, AT&T Labs - Research. Mark Gordon. What is Multiprocessing?. Enables several programs to run concurrently. Coordinated processing of Programs by more than one processor. Use of 2 or more CPUs within a single computer system. Ability of a system to support more than one processor and to allocate tasks between them. Thread Level Parallelism. Chapter 4, Appendix H. CS448. 2. The Greed for Speed. Two general approaches to making computers faster. Faster uniprocessor. All the techniques we’ve been looking at so far, plus others…. Ramoliya. . . dipak.ramoliya@darshan.ac.in. Computer Engineering Darshan . Institute of Engineering & . Technology. Unit: 6 . Distributed . Shared Memory. Clocks. External clock synchronization (. Cristian. ). Internal clock synchronization (. Gusella. & . Zatti. ). Network Time Protocol (Mills). Decisions. Agreement protocols (Fischer). Data. Distributed file . Eric Kinney. vNUMA, for virtual NUMA, is a virtual machine that presents a cluster as a virtual shared-memory multiprocessor. . It is designed to make the computational power of clusters available to legacy applications and operating systems.. Yao Wang, Andrew . Ferraiuolo. , G. Edward . Suh. Feb 17. th. 2014. Executive Summary. Observation:. Modern computing systems are vulnerable to . timing channel attacks. Problem: . No hardware techniques exist to eliminate timing channels through . Hagersten. , . Landin. , and . Haridi. (1991). Presented by Patrick . Eibl. Outline. Basics of Cache-Only Memory Architectures. The Data Diffusion Machine (DDM). DDM Coherence Protocol. Examples of Replacement, Reading, Writing.