probabilistic pointer analysis for speculative optimizations.

by Jeffrey Da Silva in 2006

Written in English
Published: Pages: 106 Downloads: 29
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Edition Notes

Statementby Jeffrey Da Silva.
The Physical Object
Paginationxii, 106 leaves.
Number of Pages106
ID Numbers
Open LibraryOL21206405M

Probabilistic Design for Optimization and Robustness: Presents the theory of modeling with variation using physical models and methods for practical applications on designs more insensitive to variation. Provides a comprehensive guide to optimization and robustness for probabilistic design. Features examples, case studies and exercises throughout. The methods presented can be applied to a wide. The book comes with a wiki that contains pages that extend some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.   Probabilistic and percentile/quantile functions play an important role in several applications, such as finance (Value-at-Risk), nuclear safety, and the environment. Recently, significant advances have been made in sensitivity analysis and optimization of probabilistic functions, which is the basis for construction of new efficient approaches.1/5(1). This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear journal aims to maintain a healthy balance between general solution techniques and problem-specific.

View Notes - Data Flow Analysis from CS at Carnegie Mellon University. Lecture 4 Introduction to Data Flow Analysis Fl I. Structure of data flow analysis fl II. sensitive to the control flow in a function – intraprocedural analysis • Examples of optimizations: References A Probabilistic Pointer Analysis for Speculative.   Probabilistic Methods in Combinatorial Analysis (Encyclopedia of Mathematics and its Applications Book 56) - Kindle edition by Sachkov, Vladimir N., Vatutin, V. A.. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Probabilistic Methods in Combinatorial Analysis (Encyclopedia of Mathematics and its Manufacturer: Cambridge University Press. A new, faster sampling method for probabilistic analysis is being added to Slide2 this month: the Response Surface method. Overall, slope probabilistic analysis can take a long time to compute for complex models. With the Response Surface method though, a small sample of strategically selected cases are computed and the other cases are predicted. Analysis Of Tcp Performance In Data Center Networks Agrawal Prathima Kulkarni ‎analysis of tcp performance in data center networks on this book addresses the need to improve tcp’s performance inside data centers by providing solutions that are both practical and backward compatible with standard tcp.

Brief Course Description. Probabilistic analysis and randomized algorithms have become an indispensible tool in virtually all areas of Computer Science, ranging from combinatorial optimization, machine learning, data streaming, approximation algorithms analysis and designs, complexity theory, coding theory, to communication networks and secured protocols.

probabilistic pointer analysis for speculative optimizations. by Jeffrey Da Silva Download PDF EPUB FB2

In contrast, recently-proposed speculative optimizations can aggressively exploit the maybe case, especially if the likelihood that two pointers alias can be quantified. This paper proposes a Probabilistic Pointer Analysis (PPA) algorithm that statically predicts.

A probabilistic pointer analysis for speculative optimizations. Download Citation | A probabilistic pointer analysis for speculative optimizations | Pointer analysis is a critical compiler analysis used to dis ambiguate the indirect memory references that.

A Probabilistic Pointer Analysis for Speculative Optimizations Jeff Da Silva and J. Gregory Steffan Department of Electrical and Computer Engineering University of Toronto {dasilva,steffan}@ Abstract Pointer analysis is a critical compiler analysis used to disambiguate the indirect memory references that result from the use of Cited by: A Probabilistic Pointer Analysis for Speculative Optimization A Probabilistic Pointer Analysis Jeff DaSilva Greg Steffan Electrical and Computer Engineering University of Toronto Tin-Fook Ngai and Sun Chan, A Compiler Framework for Speculative Analysis and Optimizations.

PLDICited by: In contrast, recently-proposed speculative optimizations can aggressively exploit the maybe case, especially if the likelihood that two pointers alias could be quantified. This dissertation proposes a Probabilistic Pointer Analysis (PPA) algorithm that statically predicts the probability of each points-to relation at every program point.

Interprocedural probabilistic pointer analysis Abstract: When performing aggressive optimizations and parallelization to exploit features of advanced architectures, optimizing and parallelizing compilers need to quantitatively assess the profitability of any transformations in order to achieve high performance.

A Systematic Approach to Probabilistic Pointer Analysis Alessandra Di Pierro1, Chris Hankin 2, and Herbert Wiklicky 1 University of Verona, Ca’ Vignal 2 - Strada le Grazie 15 I Verona, Italy 2 Imperial College London, Queen’s Gate London SW7 2AZ, UK Abstract.

We present a formal framework for syntax directed proba. Abstract. Probabilistic points-to analysis is an analysis technique for defining the probabilities on the points-to relations in programs. It provides the compiler with some optimization chances such as speculative dead store elimination, spec-ulative redundancy elimination, and speculative code scheduling.

Although sev. Don't show me this again. Welcome. This is one of over 2, courses on OCW. Find materials for this course in the pages linked along the left. MIT OpenCourseWare is a free & open publication of material from thousands of MIT courses, covering the entire MIT curriculum.

No enrollment or registration. However, the "may be" case can be capitalized by the modern class of speculative optimizations if the probability that two memory references alias can be measured. Focusing on multithreading, a prevailing technique of programming, this paper presents a new flow-sensitive technique for probabilistic pointer analysis of multithreaded programs.

Data dependence analysis is the foundation to many reordering related compiler optimizations and loop parallelization. Traditional data dependence analysis algorithms are developed primarily for Fortran-like subscripted array variables. They are not very effective for pointer-based references in C or C++.

A probabilistic pointer analysis for speculative optimizations. In: Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems, pp.

– () Google Scholar. A Probabilistic Pointer Analysis for Speculative Optimizations Jeff Da Silva and Gregg Stephan - EECG - University of Toronto Presentation Slides: Pointer analysis is a critical compiler analysis used to disambiguate the indirect memory references that result from the use of pointers and pointer-based data structures in C and other similar programming languages.

Importance of probabilistic analysis in aerospace design 2. Monte Carlo (MC) methods 3. Probability & statistics refresher 4. Turbine blade heat transfer example 5. MC method for uniform distributions 6. MC method for non-uniform distributions 3. A Probabilistic Pointer Analysis for Speculative Optimizations; Making Context-sensitive Points-to Analysis with Heap Cloning Practical For The Real World; Heap: Heap Abstractions for Static Analysis; Shape Analysis; Shape Analysis by WISC: Introduce shape analysis for heap; Shape Analysis and Applications by UT.

Probabilistic dataflow analysis frameworks infer such facts about a program, while also providing the probability with which a fact is likely to be true. We propose a new Probabilistic Dataflow Analysis Framework which uses path profiles and information about the nesting structure of loops to obtain improved probabilities of dataflow facts.

Pointer analysis [17,29], especially probabilistic, inter-procedural and context-sensitive pointer analysis [3, 5, 14] could help us obtain this information with less detailed profiling. Probabilistic Systems Analysis: An Introduction to Probabilistic Models, Decisions, and Applications of Random Processes [Breipohl, Arthur M.] on *FREE* shipping on qualifying offers.

Probabilistic Systems Analysis: An Introduction to Probabilistic Models, Decisions, and Applications of Random ProcessesReviews: 1.

Last: First: Advisor: Month: Title: pdf: Saldana: Manuel: Chow: September: A Parallel Programming Model for a Multi-FPGA Multiprocessor Machine: pdf: Ta-Min: Richard. The need for nondeterministic probabilistic evaluation.

The most efficient and economical way to handle the variations in design is to use a probabilistic analysis rather than stacking up the worst-case tolerances. The Monte Carlo simulation is the most widely used method in this area and plays a central role in RBDO and robust optimization.

The Probabilistic Method, Fourth Edition is an ideal textbook for upper-undergraduate and graduate-level students majoring in mathematics, computer science, operations research, and statistics. The Fourth Edition is also an excellent reference for researchers and combinatorists who use probabilistic methods, discrete mathematics, and number theory.

The Probabilistic Method (Wiley Series in Discrete Mathematics and. 25 jul view table of contents for the probabilistic method wiley interscience series in discrete mathematics and optimization.

The neural network must have four inputs since the data set has four input variables (sepal length, sepal width, petal length, and petal width). CHAPTER THIRTEEN Preparations for Probabilistic Analysis Questions Addressed in Chapter 13 What are the main goals of probabilistic analyses.

What is the method statement in probabilistic analysis. What is the - Selection from Project Risk Management: Essential Methods for Project Teams and Decision Makers [Book]. Pointer analysis provides information to disambiguate indirect reads and writes of data through pointers and indirect control flow through function pointers or virtual functions.

Thus it enables application of other program analyses to programs containing pointers. There is a large body of literature on pointer analysis. SSAPRE based optimizations: PRE for expressions register promotion, strength reduction, Instruction scheduling Control flow analysis Edge/path profile Heuristic rules Alias profile Heuristic rules Figure 3.

A framework of speculative analyses and optimizations. Figure 3 depicts our framework of speculative analysis and optimization. Probabilistic analysis of slope stability has been described by Caldwell and Moss () and Whittlestone et al. (), who illustrated the application of probability of failure analysis to dump design using the Y = 0 method as described earlier.

Probabilistic methods require an additional step in the stability assessment and design process. Probabilistic Expert Systems emphasizes the basic computational principles that make probabilistic reasoning feasible in expert systems. The key to computation in these systems is the modularity of the probabilistic model.

Shafer describes and compares the principal architectures for exploiting this modularity in the computation of prior and posterior probabilities. RocTopple is an interactive software tool for performing toppling analysis and support design of rock slopes.

The analysis is based on the popular block toppling method of Goodman and Bray first published in a paper, Toppling of Rock Slopes, in Input the slope parameters, discontinuity spacing, dip angle and strength, and RocTopple automatically generates the toppling blocks.

Probabilistic analysis of algorithms: What’s it good for. Univ. von Stellenbosch. To insert some new item w into a BST, we compare to the element y at the root of T.

If w. Branch Elimination. Eliminate a branch to a branch. Example: In the code fragment below, the branch to L1 and then to L2 can be replaced with a single branch to L2.The Probabilistic Method (Wiley Series in Discrete Mathematics and Optimization) Leave a reply. probabilistic combinatorics - Institute of Discrete Mathematics and.Based on the probabilistic traveling salesman problem (PTSP) and on the probabilistic minimum spanning tree problem (PMSTP), the objective of this paper is to give a rigorous treatment of the probabilistic analysis of these problems in the plane.

More specifically we present general finite-size bounds and limit theorems for the objective.