Parallel Processing Sharing The Load PowerPoint Presentations - PPT
Inside a Processor. Chip in Package. Circuits. Primarily Crystalline Silicon. 1 mm – 25 mm on a side. 100 million to billions of transistors. current “feature size” . (process). ~ 14 nanometers.
Mohammadhossein . Behgam. Agenda. Need for parallelism. Challenges. Image processing algorithms. Data handling & Load Balancing. Communication cost & performance. What is the problem?. Image Processing applications can be very computationally demanding due to:.
Anthony Waterman. Topics to Discuss. Are online games . c. onceptually. . p. arallel?. What portions of a game benefit from parallelization?. Graphics Processing Units (GPUs) . General-Purpose . C. omputing .
Pipelining. Instruction Pipeline. Pipeline Hazards and their solution. Array and Vector Processing. Pipelining and Vector Processing. Parallel Processing. It refers to techniques that are used to provide simultaneous data processing..
Dr. Guy Tel-. Zur. Lecture 10. Agenda. Administration. Final presentations. Demos. Theory. Next week plan. Home assignment #4 (last). Final Projects. Next Sunday: Groups 1-1. 6. will present. Next Monday: Groups 1.
CUDA Lecture 1. Introduction to Massively Parallel Computing. A quiet revolution and potential buildup. Computation: TFLOPs . vs. . 100 GFLOPs. CPU in every PC – massive volume and potential impact.
1 Parallel Processing A parallel processing system is able to perform concurrent data processing to achieve faster execution time The system may have two or more ALUs and be able to execute two or more instructions at the same time Also the system m
Tensor Contractions. David . Ozog. *, Jeff R. Hammond. †. , James . Dinan. †. , . Pavan. . Balaji. †. , Sameer . Shende. *, Allen . Malony. * . *University of Oregon . †. Argonne National Laboratory.
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.
Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr
Angen Zheng. Static. Load . Balancing. Distribute the load evenly across processing unit.. Is this good enough? . It depends!. No data dependency!. Load distribution remain unchanged!. Initial Balanced Load Distribution.
Justin Y. Shi | . email@example.com. Temple University. reproducibility@XSEDE. | An . XSEDE14 . Workshop | July . 14, . 2014 | Atlanta. , GA. Why . Decoupling. can . I. mprove . P. rogram Reproducibility?.