PPT-CSC 2231: Parallel Computer Architecture and Programming

Parallel Processing Multicores Prof Gennady Pekhimenko University of Toronto Fall 2017 The content of this lecture is adapted from the lectures of Onur Mutlu CMU Summary Parallelism. 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 Hardware Parallelism. Computing: execute instructions that operate on data.. Flynn’s . taxonomy (Michael Flynn, 1967) classifies computer architectures based on the number of instructions that can be executed and how they operate on data.. Time for Hardware Upgrade. Uzi Vishkin. . . The pompous version. After 40 years of “wandering in the desert”, general-purpose parallelism is very close to capturing the “promised land” of mainstream computing. Processor . Architecture . Types. Memory . Access . Architectures. Shared . Memory . Model & the . Mutex. Lecturer:. Simon Winberg. Digital Systems. EEE4084F. Attribution-. ShareAlike. 4.0 International (CC BY-SA 4.0). Reinvention of Computing for Parallelism. Uzi Vishkin. . Same title, . http://www.umiacs.umd.edu/users/vishkin/XMT/. cacm2010.pdf. , to appear in CACM. Conclusion of Intro Slide(s). Productivity: code development time + runtime . 6/16/2010. Parallel Programming Abstractions. 1. Tasks . vs. Threads. Similar but not the same.. 6/16/2010. Parallel Programming Abstractions. 2. h/w processors. Operating System. T. hreads. Task Scheduler. Justin Y. Shi | . shi@temple.edu. Temple University. reproducibility@XSEDE. | An . XSEDE14 . Workshop | July . 14, . 2014 | Atlanta. , GA. Why . Decoupling. can . I. mprove . P. rogram Reproducibility?. The Tyranny of. the Storage Hierarchy. Henry Neeman, University of Oklahoma. Charlie Peck, Earlham College. Tuesday October 11 2011. Parallel Programming: . Storage . Hierarchy. OK Supercomputing Symposium, Tue Oct 11 2011. Midterm Review. Material on exam . Module 1-10. Module 5 is mixed in there somewhere. Chapters 3-6, 8 and 9 . Understand the CUDA C programming model . Understand the architecture limitations and how to navigate them to improve the performance of your code . Early Adopter: ASU - Intel Collaboration in Parallel and Distributed Computing Yinong Chen , Eric Kostelich , Yann -Hang Lee, Alex Mahalov , Gil Speyer, and Violet R. Syrotiuk 1 st NSF /TCPP Workshop on Parallel and Distributed Computing Education ( Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Lectures. Monday. 6-9pm. Moore 212. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. Image from . Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2013. Lectures. Monday and Wednesday. 6-7:30pm. Towne . 307. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. Prof. . Smruti. R. . Sarangi. IIT Delhi. 1. What is computer architecture? . The study of computer processors and computer systems. 2. What does a computer do? . Almost everything: . . Financial calculations. Fall 2015. Lars Ailo Bongo (larsab@cs.uit.no). Course topics. Parallel programming. The parallelization process. Optimization of parallel programs. Performance analysis. Data-intensive computing. Parallel programs.