PPT-Parallel Performance

6162010 Parallel Performance 1 Performance Considerations I parallelized my code and The code slower than the sequential version or I dont get enough speedup What should I do 6162010. 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.. Performance Theory - 1. Parallel Computing. CIS . 410/. 510. Department of Computer and Information Science. Outline. Performance scalability. Analytical performance measures. Amdahl. ’. s. law and Gustafson-. Software. Steve Teixeira. Product Unit Manager. Microsoft Corporation. Session Code. : DEV401. My Assumptions About You. You have some knowledge of parallel development issues. You’d like to focus on the new stuff: Visual Studio 2010 and .NET 4.0. Geo-Simulations. Saulo Henrique Cabral Silva. Tiago Garcia de Senna Carneiro*. TerraLAB. – . Laboratory. for . Earh. System . Modeling. . and. . Simulation. Computer. . Science. . Department. Andrew Lumsdaine. Indiana University. lums@osl.iu.edu. My Goal in Life. Performance with elegance. Introduction. Overview of our high-performance, industrial strength, graph library. Comprehensive features. Improving Computer Performance. What performance translates into:. Time taken to do computation. Improving performance . → reducing time taken. What key benefits improving performance can bring:. Can solve “now-computationally-attainable” problems in . Dimension Reduction. Student: . Seung-Hee. . Bae. Advisor: . Dr. Geoffrey C. Fox. School of Informatics and Computing. Pervasive Technology Institute. Indiana University. Thesis Defense, Jan. 17, 2012. Task Scheduling in Heterogeneous . Computing . Environments . Senior Design Students: Christopher . Blandin. . and Dylan . Machovec. Post-doctoral Scholar: Bhavesh Khemka. Faculty Advisor: H. J. Siegel. Parallel . Architectures. . & Performance Analysis. Parallel computer: multiple-processor system supporting parallel programming.. Three principle types of architecture. Vector computers, in particular processor arrays. CS453. Lecture 3. A sequential algorithm is evaluated by its runtime (in general, asymptotic runtime as a function of input size). . The asymptotic runtime of a sequential program is identical on any serial platform. . A View from Berkeley. Dave Patterson. Parallel Computing Laboratory. U.C. Berkeley. July, 2008. Outline. What Caused the Revolution?. Is it an Interesting, Important Research Problem or Just Doing Industry’s Dirty Work?. CUDA Lecture 3. Parallel Architectures and Performance Analysis. Conventional Von Neumann architecture consists of a processor executing a program stored in a (main) memory:. Each main memory location located by its address. Addresses start at zero and extend to 2. Se-Joon Chung. Background and Key Challenges. The trend in computing hardware is parallel systems.. It is challenging for programmers is to develop applications that transparently scales its parallelism to leverage the increasing number of processor cores.. Osman . Sarood. How faster can we run?. Suppose we have this serial problem with 12 tasks. How fast can we run given 3 processors?. Running in parallel. Execution time reduces from 12 . secs. to 4 .