PPT-Introduction to CUDA Programming

CUDA Programming Introduction Andreas Moshovos Winter 2009 Some slidesmaterial from UIUC course by WenMei Hwu and David Kirk UCSB course by Andrea Di Blas Universitat Jena by Waqar Saleem NVIDIA by Simon Green. Basically a child CUDA Kernel can be called from within a parent CUDA kernel and then optionally synchronize on the completion of that child CUDA Kernel The parent CUDA kernel can consume the output produced from the child CUDA Kernel all withou t . 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. CUDA Platform. CUDA Parallel Computing Platform. . Hardware . . . Capabilities. GPUDirect. SMX. Dynamic Parallelism. HyperQ. Programming . Approaches. Libraries. “Drop-in” Acceleration. 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 . 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. Håkon Kvale . Stensland. Simula Research Laboratory. PC Graphics Timeline. Challenges. :. Render infinitely complex scenes. And extremely high resolution. In 1/60. th. of one second (60 frames per second). 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). K. ainz. Overview. About myself. Motivation. GPU hardware and system architecture. GPU programming languages. GPU programming paradigms. Pitfalls and best practice. Reduction and tiling examples. State-of-the-art . 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. Cliff Woolley NVIDIADeveloper Technology GroupGPUCPUGPGPU Revolutionizes ComputingLatency Processor Throughput processorLow Latency or High ThroughputCPUOptimized for low-latency access to cached dat The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand