PPT-CUDA C/C++ BASICS

NVIDIA Corporation NVIDIA 2013 What is CUDA CUDA Architecture Expose GPU parallelism for generalpurpose computing Retain performance CUDA CC Based on industrystandard CC Small set of extensions to enable heterogeneous programming. . Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin (. Negrut. ), North Carolina Charlotte (. Wikinson. GPGPU Programming in CUDA. Supada . Laosooksathit. NVIDIA Hardware Architecture. Host. memory. Recall. 5 steps for CUDA Programming. Initialize device. Allocate. device memory. Copy. data to device memory. 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 . Applications:. NAMD. Parallel Framework for Unstructured Meshing (. ParFUM. ). Features:. Profile snapshots:. Captures the runtime of the application by segregating it into user specified intervals. CUDA Profiling. Martin Burtscher. Department of Computer Science. CUDA Optimization Tutorial. Martin Burtscher. burtscher@txstate.edu. http://www.cs.txstate.edu/~burtscher/. Tutorial slides. http://www.cs.txstate.edu/~burtscher/tutorials/COT5/slides.pptx. 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. and Debugging. Shehzan. ArrayFire. Summary. ArrayFire. GPU Programming and CUDA. Debugging and Profiling using CUDA Tools. Memory Coalescing. Shared Memory and Bank Conflicts. Transpose. Reduction. ArrayFire. 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. 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. introduce the use of multiple CUDA streams to overlap memory transfers with kernel computations.. Also introduced is paged-locked memory. 2. Page-locked host memory. (also called pinned host memory). heterogeneous programming. Brian Gregor. bgregor@bu.edu. Research Computing Services. Boston University. CUDA C/C BASICS. NVIDIA . Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. What is CUDA?. Data Parallelism. Host-Device model. Thread execution. Matrix-multiplication . GPU revised!. What is CUDA?. C. ompute . D. evice . U. nified . A. rchitecture. Programming interface to GPU. 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.