PPT-GPU Computing with CUDA

Dan Negrut 2012 UWMadison Dan Negrut SimulationBased Engineering Lab Wisconsin Applied Computing Center Department of Mechanical Engineering Department of Electrical and Computer Engineering. 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 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 . . 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. 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. Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. 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. Proposed Work. This . work aims . to enable efficient dynamic memory management on NVIDIA GPUs by utilizing a sub-allocator between CUDA and the programmer. This work enables Many-Task Computing applications, which need to dynamically allocate parameters for each task, to run efficiently on GPUs.. 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. GPU Computing with CUDA © Dan Negrut, 2012 UW-Madison Dan Negrut Simulation-Based Engineering Lab Wisconsin Applied Computing Center Department of Mechanical Engineering University of Wisconsin-Madison Waters. Introduction to GPU Computing. Brief History of GPU Computing. Technical Issues. Social Impact. Marketing and Ethical . Issues. Project Management. Conclusion. Table of Contents. A . GPU is . 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. 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. Research Computing Services. Boston . University. GPU Programming. Access to the SCC. Login: . tuta#. Password: . VizTut#. GPU Programming. Access to the SCC GPU nodes. # copy tutorial materials: . Cliff Woolley NVIDIADeveloper Technology GroupGPUCPUGPGPU Revolutionizes ComputingLatency Processor Throughput processorLow Latency or High ThroughputCPUOptimized for low-latency access to cached dat