PPT-CUDA Performance Considerations

Author : stefany-barnette | Published Date : 2016-08-05

2 of 2 Varun Sampath Original Slides by Patrick Cozzi University of Pennsylvania CIS 565 Spring 2012 Agenda Instruction Optimizations Mixed Instruction Types

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CUDA Performance Considerations: Transcript

2 of 2 Varun Sampath Original Slides by Patrick Cozzi University of Pennsylvania CIS 565 Spring 2012 Agenda Instruction Optimizations Mixed Instruction Types Loop Unrolling Thread . 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. 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 . 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.. 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. 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). Hui. Li. Geoffrey Fox. Research Goal. provide . a uniform . MapReduce programming . model that works . on HPC . Clusters or . Virtual Clusters cores . on traditional Intel architecture chip, cores on . HSC PDHPE – CQ2 DP1. What are the planning considerations for improving performance. initial planning considerations. performance and fitness needs (individual, team). schedule of events/competitions. 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. Ajaya. . Neupane. , . Zhiyun. Qian . and . Nael. Abu-. Ghazaleh. University of California, Riverside. Rendered Insecure: . GPU Side Channel Attacks . are Practical. 1. G. raphics . P. rocessing . 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 May 8-Stephen Jones GTC 2018CUDANEW FEATURESAND BEYOND22CUDA ECOSYSTEM 2018CUDADOWNLOADS IN 20173500000CUDA REGISTERED DEVELOPERS800000GTC ATTENDEES80003CUDA DEVELOPMENT ECOSYSTEMFrom Ease of Use to S

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