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Ironpython in Action
IronPytho IN ACTION Michael J. Foord Christian Muirhead FOREWORD BY JIM HUGUNIN MANNING IronPython in Action Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> IronPython in Action MICHAEL J. FOORD CHRISTIAN MUIRHEAD MANNING Greenwich (74° w. long.) Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> For online information and ordering of this and other Manning books, please visit www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact Special Sales Department Manning Publications Co. Sound View Court 3B fax: (609) 877-8256 Greenwich, CT 06830 email: [email protected] ©2009 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15% recycled and processed without the use of elemental chlorine. -
The Essentials of Stackless Python Tuesday, 10 July 2007 10:00 (30 Minutes)
EuroPython 2007 Contribution ID: 62 Type: not specified The Essentials of Stackless Python Tuesday, 10 July 2007 10:00 (30 minutes) This is a re-worked, actualized and improved version of my talk at PyCon 2007. Repeating the abstract: As a surprise for people who think they know Stackless, we present the new Stackless implementation For PyPy, which has led to a significant amount of new insight about parallel programming and its possible implementations. We will isolate the known Stackless as a special case of a general concept. This is a Stackless, not a PyPy talk. But the insights presented here would not exist without PyPy’s existance. Summary Stackless has been around for a long time now. After several versions with different goals in mind, the basic concepts of channels and tasklets turned out to be useful abstractions, and since many versions, Stackless is only ported from version to version, without fundamental changes to the principles. As some spin-off, Armin Rigo invented Greenlets at a Stackless sprint. They are some kind of coroutines and a bit of special semantics. The major benefit is that Greenlets can runon unmodified CPython. In parallel to that, the PyPy project is in its fourth year now, and one of its goals was Stackless integration as an option. And of course, Stackless has been integrated into PyPy in a very nice and elegant way, much nicer than expected. During the design of the Stackless extension to PyPy, it turned out, that tasklets, greenlets and coroutines are not that different in principle, and it was possible to base all known parallel paradigms on one simple coroutine layout, which is as minimalistic as possible. -
9. Optimization
9. Optimization Marcus Denker Optimization Roadmap > Introduction > Optimizations in the Back-end > The Optimizer > SSA Optimizations > Advanced Optimizations © Marcus Denker 2 Optimization Roadmap > Introduction > Optimizations in the Back-end > The Optimizer > SSA Optimizations > Advanced Optimizations © Marcus Denker 3 Optimization Optimization: The Idea > Transform the program to improve efficiency > Performance: faster execution > Size: smaller executable, smaller memory footprint Tradeoffs: 1) Performance vs. Size 2) Compilation speed and memory © Marcus Denker 4 Optimization No Magic Bullet! > There is no perfect optimizer > Example: optimize for simplicity Opt(P): Smallest Program Q: Program with no output, does not stop Opt(Q)? © Marcus Denker 5 Optimization No Magic Bullet! > There is no perfect optimizer > Example: optimize for simplicity Opt(P): Smallest Program Q: Program with no output, does not stop Opt(Q)? L1 goto L1 © Marcus Denker 6 Optimization No Magic Bullet! > There is no perfect optimizer > Example: optimize for simplicity Opt(P): Smallest ProgramQ: Program with no output, does not stop Opt(Q)? L1 goto L1 Halting problem! © Marcus Denker 7 Optimization Another way to look at it... > Rice (1953): For every compiler there is a modified compiler that generates shorter code. > Proof: Assume there is a compiler U that generates the shortest optimized program Opt(P) for all P. — Assume P to be a program that does not stop and has no output — Opt(P) will be L1 goto L1 — Halting problem. Thus: U does not exist. > There will -
Goless Documentation Release 0.6.0
goless Documentation Release 0.6.0 Rob Galanakis July 11, 2014 Contents 1 Intro 3 2 Goroutines 5 3 Channels 7 4 The select function 9 5 Exception Handling 11 6 Examples 13 7 Benchmarks 15 8 Backends 17 9 Compatibility Details 19 9.1 PyPy................................................... 19 9.2 Python 2 (CPython)........................................... 19 9.3 Python 3 (CPython)........................................... 19 9.4 Stackless Python............................................. 20 10 goless and the GIL 21 11 References 23 12 Contributing 25 13 Miscellany 27 14 Indices and tables 29 i ii goless Documentation, Release 0.6.0 • Intro • Goroutines • Channels • The select function • Exception Handling • Examples • Benchmarks • Backends • Compatibility Details • goless and the GIL • References • Contributing • Miscellany • Indices and tables Contents 1 goless Documentation, Release 0.6.0 2 Contents CHAPTER 1 Intro The goless library provides Go programming language semantics built on top of gevent, PyPy, or Stackless Python. For an example of what goless can do, here is the Go program at https://gobyexample.com/select reimplemented with goless: c1= goless.chan() c2= goless.chan() def func1(): time.sleep(1) c1.send(’one’) goless.go(func1) def func2(): time.sleep(2) c2.send(’two’) goless.go(func2) for i in range(2): case, val= goless.select([goless.rcase(c1), goless.rcase(c2)]) print(val) It is surely a testament to Go’s style that it isn’t much less Python code than Go code, but I quite like this. Don’t you? 3 goless Documentation, Release 0.6.0 4 Chapter 1. Intro CHAPTER 2 Goroutines The goless.go() function mimics Go’s goroutines by, unsurprisingly, running the routine in a tasklet/greenlet. -
Value Numbering
SOFTWARE—PRACTICE AND EXPERIENCE, VOL. 0(0), 1–18 (MONTH 1900) Value Numbering PRESTON BRIGGS Tera Computer Company, 2815 Eastlake Avenue East, Seattle, WA 98102 AND KEITH D. COOPER L. TAYLOR SIMPSON Rice University, 6100 Main Street, Mail Stop 41, Houston, TX 77005 SUMMARY Value numbering is a compiler-based program analysis method that allows redundant computations to be removed. This paper compares hash-based approaches derived from the classic local algorithm1 with partitioning approaches based on the work of Alpern, Wegman, and Zadeck2. Historically, the hash-based algorithm has been applied to single basic blocks or extended basic blocks. We have improved the technique to operate over the routine’s dominator tree. The partitioning approach partitions the values in the routine into congruence classes and removes computations when one congruent value dominates another. We have extended this technique to remove computations that define a value in the set of available expressions (AVA IL )3. Also, we are able to apply a version of Morel and Renvoise’s partial redundancy elimination4 to remove even more redundancies. The paper presents a series of hash-based algorithms and a series of refinements to the partitioning technique. Within each series, it can be proved that each method discovers at least as many redundancies as its predecessors. Unfortunately, no such relationship exists between the hash-based and global techniques. On some programs, the hash-based techniques eliminate more redundancies than the partitioning techniques, while on others, partitioning wins. We experimentally compare the improvements made by these techniques when applied to real programs. These results will be useful for commercial compiler writers who wish to assess the potential impact of each technique before implementation. -
Language and Compiler Support for Dynamic Code Generation by Massimiliano A
Language and Compiler Support for Dynamic Code Generation by Massimiliano A. Poletto S.B., Massachusetts Institute of Technology (1995) M.Eng., Massachusetts Institute of Technology (1995) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 1999 © Massachusetts Institute of Technology 1999. All rights reserved. A u th or ............................................................................ Department of Electrical Engineering and Computer Science June 23, 1999 Certified by...............,. ...*V .,., . .* N . .. .*. *.* . -. *.... M. Frans Kaashoek Associate Pro essor of Electrical Engineering and Computer Science Thesis Supervisor A ccepted by ................ ..... ............ ............................. Arthur C. Smith Chairman, Departmental CommitteA on Graduate Students me 2 Language and Compiler Support for Dynamic Code Generation by Massimiliano A. Poletto Submitted to the Department of Electrical Engineering and Computer Science on June 23, 1999, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract Dynamic code generation, also called run-time code generation or dynamic compilation, is the cre- ation of executable code for an application while that application is running. Dynamic compilation can significantly improve the performance of software by giving the compiler access to run-time infor- mation that is not available to a traditional static compiler. A well-designed programming interface to dynamic compilation can also simplify the creation of important classes of computer programs. Until recently, however, no system combined efficient dynamic generation of high-performance code with a powerful and portable language interface. This thesis describes a system that meets these requirements, and discusses several applications of dynamic compilation. -
Faster Cpython Documentation Release 0.0
Faster CPython Documentation Release 0.0 Victor Stinner January 29, 2016 Contents 1 FAT Python 3 2 Everything in Python is mutable9 3 Optimizations 13 4 Python bytecode 19 5 Python C API 21 6 AST Optimizers 23 7 Old AST Optimizer 25 8 Register-based Virtual Machine for Python 33 9 Read-only Python 39 10 History of Python optimizations 43 11 Misc 45 12 Kill the GIL? 51 13 Implementations of Python 53 14 Benchmarks 55 15 PEP 509: Add a private version to dict 57 16 PEP 510: Specialized functions with guards 59 17 PEP 511: API for AST transformers 61 18 Random notes about PyPy 63 19 Talks 65 20 Links 67 i ii Faster CPython Documentation, Release 0.0 Contents: Contents 1 Faster CPython Documentation, Release 0.0 2 Contents CHAPTER 1 FAT Python 1.1 Intro The FAT Python project was started by Victor Stinner in October 2015 to try to solve issues of previous attempts of “static optimizers” for Python. The main feature are efficient guards using versionned dictionaries to check if something was modified. Guards are used to decide if the specialized bytecode of a function can be used or not. Python FAT is expected to be FAT... maybe FAST if we are lucky. FAT because it will use two versions of some functions where one version is specialised to specific argument types, a specific environment, optimized when builtins are not mocked, etc. See the fatoptimizer documentation which is the main part of FAT Python. The FAT Python project is made of multiple parts: 3 Faster CPython Documentation, Release 0.0 • The fatoptimizer project is the static optimizer for Python 3.6 using function specialization with guards. -
Jupyter Tutorial Release 0.8.0
Jupyter Tutorial Release 0.8.0 Veit Schiele Oct 01, 2021 CONTENTS 1 Introduction 3 1.1 Status...................................................3 1.2 Target group...............................................3 1.3 Structure of the Jupyter tutorial.....................................3 1.4 Why Jupyter?...............................................4 1.5 Jupyter infrastructure...........................................4 2 First steps 5 2.1 Install Jupyter Notebook.........................................5 2.2 Create notebook.............................................7 2.3 Example................................................. 10 2.4 Installation................................................ 13 2.5 Follow us................................................. 15 2.6 Pull-Requests............................................... 15 3 Workspace 17 3.1 IPython.................................................. 17 3.2 Jupyter.................................................. 50 4 Read, persist and provide data 143 4.1 Open data................................................. 143 4.2 Serialisation formats........................................... 144 4.3 Requests................................................. 154 4.4 BeautifulSoup.............................................. 159 4.5 Intake................................................... 160 4.6 PostgreSQL................................................ 174 4.7 NoSQL databases............................................ 199 4.8 Application Programming Interface (API).............................. -
Transparent Dynamic Optimization: the Design and Implementation of Dynamo
Transparent Dynamic Optimization: The Design and Implementation of Dynamo Vasanth Bala, Evelyn Duesterwald, Sanjeev Banerjia HP Laboratories Cambridge HPL-1999-78 June, 1999 E-mail: [email protected] dynamic Dynamic optimization refers to the runtime optimization optimization, of a native program binary. This report describes the compiler, design and implementation of Dynamo, a prototype trace selection, dynamic optimizer that is capable of optimizing a native binary translation program binary at runtime. Dynamo is a realistic implementation, not a simulation, that is written entirely in user-level software, and runs on a PA-RISC machine under the HPUX operating system. Dynamo does not depend on any special programming language, compiler, operating system or hardware support. Contrary to intuition, we demonstrate that it is possible to use a piece of software to improve the performance of a native, statically optimized program binary, while it is executing. Dynamo not only speeds up real application programs, its performance improvement is often quite significant. For example, the performance of many +O2 optimized SPECint95 binaries running under Dynamo is comparable to the performance of their +O4 optimized version running without Dynamo. Internal Accession Date Only Ó Copyright Hewlett-Packard Company 1999 Contents 1 INTRODUCTION ........................................................................................... 7 2 RELATED WORK ......................................................................................... 9 3 OVERVIEW -
Optimizing for Reduced Code Space Using Genetic Algorithms
Optimizing for Reduced Code Space using Genetic Algorithms Keith D. Cooper, Philip J. Schielke, and Devika Subramanian Department of Computer Science Rice University Houston, Texas, USA {keith | phisch | devika}@cs.rice.edu Abstract is virtually impossible to select the best set of optimizations to run on a particular piece of code. Historically, compiler Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimiza- writers have made one of two assumptions. Either a fixed tions are designed to reduce the execution time of compiled optimization order is “good enough” for all programs, or code, but not necessarily the size of the compiled code. Fur- giving the user a large set of flags that control optimization ther, different results can be achieved by running some opti- is sufficient, because it shifts the burden onto the user. mizations more than once and changing the order in which optimizations are applied. Register allocation only com- Interplay between optimizations occurs frequently. A plicates matters, as the interactions between different op- transformation can create opportunities for other transfor- timizations can cause more spill code to be generated. The mations. Similarly, a transformation can eliminate oppor- compiler for embedded systems, then, must take care to use tunities for other transformations. These interactions also the best sequence of optimizations to minimize code space. Since much of the code for embedded systems is compiled depend on the program being compiled, and they are of- once and then burned into ROM, the software designer will ten difficult to predict. Multiple applications of the same often tolerate much longer compile times in the hope of re- transformation at different points in the optimization se- ducing the size of the compiled code. -
Pykota Documentation
PyKota Documentation A full featured Print Quota Solution for CUPS Jérôme Alet C@LL - Conseil Internet & Logiciels Libres 48 Avenue Vincent Arnaud 06300 Nice France Email : [email protected] Phone : +33 (0) 4 93 27 12 98 PyKota Documentation: A full featured Print Quota Solution for CUPS by Jérôme Alet Copyright © 2003, 2004, 2005, 2006 Jerome Alet Abstract This document will describe the setup of the PyKota Print Quota system for the Common UNIX Printing System, and its day-to-day use. Legal Notice This documentation is licensed under the terms of the GNU General Public License as described below. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. A copy of the entire license is included in the section entitled "GNU General Public License". In case you encounter any problem, please contact Jérôme Alet (mailto:[email protected]) , author of the present document. $Date: 2006-12-03 11:50:20 +0100 (dim, 03 déc 2006) $ $Revision: 2422 $ Table of Contents 1. -
Peephole Optimization from Wikipedia, the Free Encyclopedia
Peephole optimization From Wikipedia, the free encyclopedia In compiler theory, peephole optimization is a kind of optimization performed over a very small set of instructions in a segment of generated code. The set is called a "peephole" or a "window". It works by recognising sets of instructions that can be replaced by shorter or faster sets of instructions. Contents 1 Replacement rules 2 Examples 2.1 Replacing slow instructions with faster ones 2.2 Removing redundant code 2.3 Removing redundant stack instructions 3 Implementation 4 See also 5 References 6 External links Replacement rules Common techniques applied in peephole optimization:[1] Constant folding – Evaluate constant subexpressions in advance. Strength reduction – Replace slow operations with faster equivalents. Null sequences – Delete useless operations. Combine operations – Replace several operations with one equivalent. Algebraic laws – Use algebraic laws to simplify or reorder instructions. Special case instructions – Use instructions designed for special operand cases. Address mode operations – Use address modes to simplify code. There can, of course, be other types of peephole optimizations involving simplifying the target machine instructions, assuming that the target machine is known in advance. Advantages of a given architecture and instruction sets can be exploited, and disadvantages avoided in this case. Examples Replacing slow instructions with faster ones The following Java bytecode ... aload 1 aload 1 mul ... can be replaced by ... aload 1 dup mul ... This kind of optimization, like most peephole optimizations, makes certain assumptions about the efficiency of instructions. For instance, in this case, it is assumed that the dup operation (which duplicates and pushes the top of the stack) is more efficient than the aload X operation (which loads a local variable identified as X and pushes it on the stack).