PPT-Introduction to CUDA

heterogeneous programming Katia Oleinik koleinikbuedu Scientific Computing and Visualization Boston University Architecture NVIDIA Tesla M2070 Core clock 115GHz Single instruction 448 CUDA cores . CUDA Platform. CUDA Parallel Computing Platform. . Hardware . . . Capabilities. GPUDirect. SMX. Dynamic Parallelism. HyperQ. Programming . Approaches. Libraries. “Drop-in” Acceleration. ITS Research Computing. Mark Reed . Objectives. Learn why computing with accelerators is important. Understand accelerator hardware. Learn what types of problems are suitable for accelerators. Survey the programming models available. CUDA Lecture 4. CUDA Programming Basics. Things we need to consider:. Control. Synchronization. Communication. Parallel programming languages offer different ways of dealing with above. CUDA Programming Basics – Slide . NVIDIA Corporation. Tesla GPU Computing. A Revolution in High Performance Computing. Agenda. CUDA Review. Architecture. Programming Model. Memory Model. CUDA C. CUDA General Optimizations. Fermi. Next Generation Architecture. © Dan Negrut, . 2012. UW-Madison. Dan Negrut. Simulation-Based Engineering Lab. Wisconsin Applied Computing Center. Department of Mechanical Engineering. Department of . Electrical and Computer Engineering. Håkon Kvale . Stensland. iAD-lab, Department for Informatics. Basic 3D Graphics Pipeline. Application. Scene Management. Geometry. Rasterization. Pixel Processing. ROP/FBI/Display. Frame. Buffer. Memory. CUDA Lecture 7. CUDA Threads and Atomics. The Problem: how do you do global communication?. Finish a kernel and start a new one. All writes from all threads complete before a kernel finishes. Would need to decompose kernels into before and after parts. Sathish. . Vadhiyar. Parallel Programming. GPU. Graphical Processing Unit. A single GPU consists of large number of cores – hundreds of cores.. Whereas a single CPU can consist of 2, 4, 8 or 12 cores. Introduction to Programming Massively Parallel Graphics processors. Andreas . Moshovos. moshovos@eecg.toronto.edu. ECE, Univ. of Toronto. Summer 2010. Some slides/material from:. UIUC course by . Wen. Overview. GPU Ocelot overview. Building, configuring, and executing Ocelot programs. Ocelot Device Interface and CUDA Runtime API. Ocelot PTX Internal Representation. PTX Pass Manager. 2. Ocelot: Multiplatform Dynamic Compilation. 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. on . Ubuntu. Cuda. download site. . https://developer.nvidia.com/cuda-downloads. $ . sudo. . dpkg. -. i. cuda-repo-ubuntu1404_7.5-18_amd64.deb . $ . sudo. apt-get update . $ . sudo. apt-get install . Defines much more than an API. A language . Hardware Specifications. PA0. Let’s look into your first assignment and figure some things out.. HELLOCUDA.CU. HELLOCUDA.CU. Pointers to GPU land. dev_a. Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources.