PPT-Introduction to CUDA Programming

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Introduction to Programming Massively Parallel Graphics processors Andreas Moshovos moshovoseecgtorontoedu ECE Univ of Toronto Summer 2010 Some slidesmaterial from

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Introduction to CUDA Programming: Transcript

Introduction to Programming Massively Parallel Graphics processors Andreas Moshovos moshovoseecgtorontoedu ECE Univ of Toronto Summer 2010 Some slidesmaterial from UIUC course by Wen. heterogeneous programming. Katia Oleinik. koleinik@bu.edu. Scientific Computing and Visualization. Boston . University. Architecture. NVIDIA Tesla M2070: . Core clock: 1.15GHz . Single instruction . 448 CUDA cores . 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 . © 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. Martin Burtscher. Department of Computer Science. High-End CPUs and GPUs. Xeon X7550 Tesla C2050. Cores 8 (superscalar) 448 (simple). Active threads 2 per core 48 per core. Frequency 2 GHz 1.15 GHz. 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. 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 . Introduction to CUDA Programming CUDA Programming Introduction Andreas Moshovos Winter 2009 Some slides/material from: UIUC course by Wen-Mei Hwu and David Kirk UCSB course by Andrea Di Blas Universitat Jena by Waqar Saleem 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. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. GeForce 256 (1999) – first GPU. Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources. 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.

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