PPT-Shared Memory Programming(2)

Parallel Processing CS526 Spring 2012Week 8 Thread Status Synchronization in Shared Memory ProgrammingJava threads Locks Barriars In This Week A Java thread is always in one of several states which could be running sleeping dead etc. 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. 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. Lecture . 7: Lab 3 Recitation. Today. Miscellaneous CUDA syntax. Recap on CUDA and buffers. Shared memory for an N-body simulation. Flocking simulations. Integrators. CUDA Kernels. Launching the kernel:. Lecture . 5: Memory. Today. GPU Memory Overview. CUDA Memory Syntax. Tips and tricks for memory handling. Memory Overview. Very slow access:. Between host and device. Slow access:. Global Memory. Fast access:. Parallel Processing (CS526) . Spring 2012(Week 8) . Shared Memory Architecture . Shared Memory Programming & PLs. Java Threads . Preparing the Environment . In This lecture . Shared Memory Architecture . 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. and Timing Analysis . on Embedded Multicores. Eugene Yip. The University of Auckland. Supervisors: Advisor:. Dr.. . Partha. . Roop. . Dr. .. Alain . Girault. Dr.. . Morteza. . Biglari-Abhari. 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 . 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 of. Split. Performance. . comparison. for NVIDIA CUDA . and. Intel . Xeon. . Phi. May, 2016. Contents. . Introduction. NVIDIA CUDA. Intel . Xeon. . Phi. . Conclusion. . tCSC. 2016. . t. oday’s. th. SMXS/MXDECC. Phone: 405-736-4051. Email: james.stevens@tinker.af.mil. I n t e g r i t y - S e r v i c e - E x c e l l e n c e. TEAM TINKER. OKLAHOMA CITY . AIR LOGISTICS COMPLEX. Accelerating Finite Difference Computations Using General Purpose GPU Computing. Martin Burtscher. Department of Computer Science. High-end CPU-GPU Comparison. . Xeon 8180M. . Titan V. Cores 28 5120 (+ 640). Active threads 2 per core 32 per core. Frequency 2.5 (3.8) GHz 1.2 (1.45) GHz. 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. Lecture 19. April . 4. th. , . 2012. Bus-Based Shared Memory (. Con’t. ). Distributed Shared Memory. Prof John D. Kubiatowicz. http://www.cs.berkeley.edu/~kubitron/cs252. Recall: Natural Extensions of Memory System.