PPT-Formal Metrics for Large-Scale Parallel Performance

ISC 2015 Kenneth Moreland and Ron Oldfield Sandia National Laboratories Parallel Algorithm Speedup Parallel Algorithm Speedup Serial time for large problem sizes Cannot be measured in practice. C-DAC/SECG/2006 C-DAC/SECG/2007 To determine best parallel algorithm By. Chi . Bemieh. . Fule. August 6, 2013. THESIS PRESENTATION . Outline. . of. . today’s. presentation. Justification of the study. Problem . statement. Hypotheses. Conceptual. . framework. Research . Scale Down, Scale Up, Scale Out. . Phillip B. . Gibbons. Intel Science & Technology Center. for Cloud Computing. Keynote Talk at IPDPS’15. May 28, 2015. ISTC for Cloud Computing. Underlying Infrastructure. packings. of ellipsoids with . OpenMP. and HyperFlow. Monika Bargieł. 1. , . Łukasz. Szczygłowski. 1. , . Radosław. Trzcionkowski. 1. , . Maciej. Malawski. 1,2. 1. Department of Computer Science,. Nishant Totla. , . Aditya. . Devarakonda. , . Sanjit. . Seshia. SAT Solving. Given a propositional logic formula in conjunctive normal form (CNF), does there exist a satisfying assignment?. . 6/16/2010. Parallel Performance. 1. Performance Considerations. I parallelized my code and …. The code slower than the sequential version, or. I don’t get enough speedup . What should I do?. 6/16/2010. 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?. Speed . Dataing. : Measuring Student Success. Dr. Lou Guthrie, Benchmarking Institute. Sounds better…. Graduation Rates are not a good measure of student success for CCs.. Cover only approximately 13% of your students – FT, FT, degree-seeking. Rouven Krebs (SAP AG). , Christof Momm (SAP AG), Samuel Kounev (KIT). SPEC RG Cloud. , . May . 2012. Isolation . and Shared Resources. provides. Service Provider. High overhead, low utilization .  need to share. 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. Performance Metrics for Graph Mining Tasks 1 Outline Introduction to Performance Metrics Supervised Learning Performance Metrics Unsupervised Learning Performance Metrics Optimizing Metrics Statistical Significance Techniques (USG) needle interventions, such as central venous catheterization (CVC), require simultaneous sonography and needle placement. . Ideally novices should master this skill in a simulated setting, prior to progressing to the clinical realm, in order to minimize risk. Thus. Fall 2018. 28 November 2018. Computer Architecture. Lecture 19b: . Heterogeneous Multi-Core Systems. Computer Architecture Research. If you want to do research . in any of the covered topics or any topic in Comp Arch, HW/SW Interaction &... 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 .