collection articles explores ways how to make your Objective-C code run faster. The series covers exclusively Objective-C and Foundation specifically. Optimizing code in general is not part of the agenda.
The Portable Network Graphics (PNG) is a format for storing compressed raster graphics. The compression engine is based on the Deflate method [RFC1951], designed by PKWare and originally used in PKZIP.
a visual profiler for Linux-x86. The program helps in visualizing and describing what's going on in the heart of the program. It helps developers in finding and analyzing bottlenecks in their applications. Rapido scans the binary and the shared objects fo
a free Java class file shrinker, optimizer, and obfuscator. It can detect and remove unused classes, fields, methods, and attributes. It can then optimize bytecode and remove unused instructions.
a tool for source-to-source transformation and optimisation of C++ programs. It is intended to be used as a test-bed for various high-level optimisations; the traditional textbook optimisations are assumed to be handled by the C++ compiler.
provides a software development platform that allows developers to take advantage of a new generation of high performance processors. These new processors, including GPUs, the IBM Cell, and other multi-core processors
an open source framework for applications that intercept ntercepts ( JDBC )and optionally modify database statements. For JBoss, ATG, Orion, JOnAS, iPlanet, WebLogic, WebSphere, Resin and Tomcat.
it is possible to execute Python code at speeds approaching that of fully compiled languages, by "specialization". The current prototype operates on i386-compatible processors and shows 2 to 100 times speed-ups, depending on code.
by Mark Larson. "The most important thing to remember is to TIME your code. Trying different tricks might or might not speed up your code. So it is very important to time your code to see if you do get a speedup as you try each trick."
a collection of popular software routines on x86 class processors. Spanning simple arithmetic routines to rich domains such as image and signal processing, it provides a quick path to high performance development.
a compilation of Michael's writings on assembly language and graphics programming (including Dr. Dobb's Journal "Graphics Programming" column).Focus is on profiling and code testing, as well as performance optimization.
Having only the services you need running will make your system faster, more stable and secure. So the first thing you need to do after installing a Linux distribution is to manually edit the list of enabled services.
a library of simple functions that are optimized for various CPUs. These functions are generally loops implementing simple algorithms, such as converting an array of N integers to floating-point numbers...
introduced by Marco Dorigo in 1992 in his PhD thesis, is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. They are inspired by the behavior of ants in finding paths from the colony to food.
blog entry about static code analyzers such as Checkstyle, PMD, FindBugs etc. and focuses on some of the issues that they spot in code. (PDM is great also for C++)
Target the parts of your applications that take the most time, by Martyn Honeyford "Improving the performance of your applications is rarely a wasted effort, but it's not always clear which functions the program is spending most of its execution time on. Learn how to pinpoint performance bottlenecks using gprof for both user-space and system calls on Linux®."
Supports the Six-Core AMD Opteron™ processor all earlier processors. Multiple simultaneous symbol servers. Process filters: You can limit the reported data to certain processes. API: Custom tools can be built using the API to control profiling and to analyze profile data. This feature opens new ways to work with and build on CodeAnalyst. Call stack data for running process: Call Stack information can be captured about a process using the command line tool.
an instrumentation framework for building dynamic analysis tools. Includes a memory error detector, two thread error detectors, a cache and branch-prediction profiler, a call-graph generating cache profiler, and a heap profiler. It also includes two experimental tools: a heap/stack/global array overrun detector, and a SimPoint basic block vector generator.
combines threading and memory error checking into one powerful error checking tool. It helps increase the reliability, security, and accuracy of C/C++ applications from within Microsoft Visual Studio*. Intel Parallel Inspector uses dynamic instrumentation that requires no special test builds or compilers, so it’s easier to test code more often.
a different approach to image optimization, based on how image data is stored in different formats. Let’s start with the JPEG format and a simple technique called the eight-pixel grid.
Applied Mathematics (AM) is an international journal dedicated to the latest advancement of applied mathematics. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of applied mathematics.
Intelligent Control and Automation is a scholarly open access, peer-reviewed, quarterly and fully refereed journal focusing on theories, methods and applications in intelligent control and automation. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share and discuss various new issues and developments in different areas of intelligent control and automation.
Nailgun is a client, protocol, and server for running Java programs from the command line without incurring the JVM startup overhead. Programs run in the server (which is implemented in Java), and are triggered by the client (written in C), which handles all I/O.
* 1 Load the framework from Google Code * 3 Combine all your scripts and minify them * 5 Keep selection operations to a mini by caching * 6 Keep DOM manipulation to a min * 7 Wrap everything in a single element when inserting DOM * 8 Use IDs instead of classes wherever possible * 9 Give your selectors a context * 10 Use chaining properly * 11 use animate properly * 12 Learn about event delegation * 13 Use classes to store state * 14 or use jQuery's internal data() method to store state * 15 Write your own selectors * 16 Streamline your HTML and modify it once the page has loaded * 17 Lazy load content for speed and SEO benefits * 18 Use jQuery's utility functions * 19 Use noconflict to rename the jquery object when using other frameworks * 20 How to tell when images have loaded * 22 How to check if an element exists * 23 Add a JS class to your HTML attribute * 24 Return 'false' to prevent default behaviour * 25 Shorthand for the ready event
In many problems that require extensive searching, the solution can be described as satisfying two competing constraints, where satisfying each independently does not pose a challenge. As an alternative to tree-based and stochastic searching, for these problems we propose using an iterated map built from the projections to the two constraint sets. Algorithms of this kind have been the method of choice in a large variety of signal-processing applications; we show here that the scope of these algorithms is surprisingly broad, with applications as diverse as protein folding and Sudoku.Our survey of applications shows the difference map algorithm often achieves results comparable to much more sophisticated, special purpose algorithms. Efficient implementations of constraint projections usually are easier than designing the linear program solver at the heart of many optimization algorithms.
Whole-program optimization is a compilation technique in which optimizations operate over the entire program. This allows the compiler many optimization opportunities that are not available when analyzing modules separately (as with separate compilation). Most of MLton's optimizations are whole-program optimizations. Because MLton compiles the whole program at once, it can perform optimization across module boundaries. As a consequence, MLton often reduces or eliminates the run-time penalty that arises with separate compilation of SML features such as functors, modules, polymorphism, and higher-order functions. MLton takes advantage of having the entire program to perform transformations such as: defunctorization, monomorphisation, higher-order control-flow analysis, inlining, unboxing, argument flattening, redundant-argument removal, constant folding, and representation selection. Whole-program compilation is an integral part of the design of MLton and is not likely to change.
This paper presents an automatic fusion system, stream fusion, based on equational transformations, that fuses a wider range of functions than existing short-cut fusion systems. In particular, stream fusion is able to fuse zips, left folds and functions over nested lists, including list comprehensions. A distinguishing feature of the stream fusion framework is its simplicity: by transforming list functions to expose their structure, intermediate values are eliminated by general purpose compiler optimisations. We have reimplemented the entire Haskell standard list library on top of our framework, providing stream fusion for Haskell lists. By allowing a wider range of functions to fuse, we see an increase in the number of occurrences of fusion in typical Haskell programs. We present benchmarks documenting time and space improvements.
The Haskell String type is notoriously inefficient. We introduce a new data type, ByteString, based on lazy lists of byte arrays, combining the speed benefits of strict arrays with lazy evaluation. Equational transformations based on term rewriting are used to deforest intermediate ByteStrings automatically. We describe novel fusion combinators with improved expressivity and performance over previous functional array fusion strategies. A library for ByteStrings is implemented, providing a purely functional interface, and approaches the speed of low-level mutable arrays in C.
It is considered a smart business practice for website operators to liberate themselves from dependence on search engine traffic. Search engine optimization ranking of a website may involve the release of their content, HTML and associated coding.
Blurbpoint one of the seo firm from india offers SEO services for your online business. We are leading seo company offering seo services, link building packages.
We focus on scientific/engineering software development using RAD abilities of Python language, accompanied with free scientific libraries such as NumPy and SciPy. Our mainstream research activity is numerical optimization, including nonsmooth optimization and solving systems of nonlinear equations. OpenOpt framework - universal numerical optimization package with several own solvers (e.g. ralg) and connections to tens of other, graphical output of convergence and many other goodies FuncDesigner - tool to rapidly build functions over variables/arrays and get their derivatives via automatic differentiation. Also, you can perform integration, interpolation, solve systems of linear/nonlinear/ODE equations and numerical optimization problems coded in FuncDesigner by OpenOpt (see some examples in its doc), uncertainty analysis and interval analysis DerApproximator - tool to get (or check user-supplied) derivatives via finite-difference approximation SpaceFuncs - tool for 2D, 3D, N-dimensional geometric modeling with possibilities of parametrized calculations, numerical optimization and solving systems of geometrical equations
"The CPU," he said, "runs at a certain speed. It can execute a fixed number of instructions per second, and no more. There is a finite limit to how many instructions per second it can execute. Right?"
"Right," I said.
"So there is no way, really, to make code go faster, because there is no way to make instructions execute faster. There is only such a thing as making the machine do less."
He paused for emphasis.
"To go fast," he said slowly, "do less."
A couple of posts ago, I talked about a simple monte carlo simulation for diffusion limited aggregation. In this post, I’m going to talk about another algorithm that, at its heart, is based on random numbers. Unlike DLA though, this algorithm isn’t about simulating a physical system. Instead, it is about a method for solving optimization problems that are generally poorly suited to traditional numerical optimization techniques. This post describes an application of a library implementing the GEP method posed by Cândida Ferreira nearly 10 years ago. I started messing with GEP shortly after the paper “Gene Expression Programming: A New Adaptive Algorithm for Solving Problems” was published in the journal Complex Systems. The paper sat in a pile for a while, and about two years ago I picked it up again and started to implement it as a Haskell library.
Ateji PX, now available for the Java language, by reducing development & debugging time, Ateji PX let you concentrate your talent on good design & performance tuning. You can find linear programming, portfolio optimization software & java threads with Ate