PPT-GPU Computing with CUDA

GPU Computing with CUDA Dan Negrut 2012 UWMadison Dan Negrut SimulationBased Engineering Lab Wisconsin Applied Computing Center Department of Mechanical Engineering University of WisconsinMadison. 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 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. 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. 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. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. 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 . 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 . 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. 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: . Current Goal(s):. Generate . stacktraces. of GPU executions and associate GPU call chains with CPU call graphs. Particular interest on how to determine call chains when in-lined GPU functions are used. Cliff Woolley NVIDIADeveloper Technology GroupGPUCPUGPGPU Revolutionizes ComputingLatency Processor Throughput processorLow Latency or High ThroughputCPUOptimized for low-latency access to cached dat