LIMP: Large Image Manipulation Project
Library design is an imprecise art. It would be impractical to include in a design all possible uses for a library of any sufficient size or complexity. Thus, it is inevitable that many libraries (and programs) reach evolutionary dead ends, where newly anticipated uses or algorithms can no longer fit nicely into the existing architecture.
The quickest short term solution is usually to hack in new interfaces, but over time the accumulation of hacks tends to reduce the stability, understandability and maintainability of code. The software industry comes up against this problem often, but fortunately has found a simple yet elegant solution—start over. Usually, rewriting lower- to middle-level interfaces can remove the accumulation of hacks. However, if the design criteria in question are incorporated throughout the code, sometimes only a total rewrite can truly help.
This is not solely a trait of software design. Software engineering is an expression of mathematics in a confined space (your computer). Through this heritage, it shares traits with other inexact sciences such as physics, where rewriting or reworking theories is not uncommon.
During the last five years, I've written many image-processing algorithms, from specialized routines for machine vision to complete libraries for commercial video and aerial image-processing software. The last commercial library has been in use for three years and has weathered many interface changes and enhancements. But just as each library was in some ways an improvement over previous attempts, I saw ways to improve performance and capability. Following in the footsteps of the makers of the six million dollar man—I wanted to make it faster, smarter and better than before and at significantly reduced cost.
My commercial library had a large amount of code tied to it, so simply modifying the existing code was not an option. It seemed as if I would never be able to incorporate a new library into my commercial work because of enormous design incompatibilities. Rather than have this new library be destined to collect electronic dust on my hard drive, I decided to start completely from scratch as open source. In late November 1998, the Large Image Manipulation Program (LIMP) was born.
It's likely that even as open source, this library would have been inconspicuous enough to draw little, if any, attention. However, after a few months spent developing LIMP in my spare time, Open Source Remote Sensing (OSRS, http://remotesensing.org/) was born. I was thrilled at the thought of having an open source library that was actually useful to someone, so LIMP was moved to OSRS for public development.
The purpose of LIMP is to allow the processing of large images using a minimal amount of memory. A number of available libraries can be used for image processing, any of which could be used to give identical results. The differences between these libraries can often be summed up by answering a few questions:
Can the data be processed on demand, or must all the processed data be in memory or on disk?
How easy is it to write new algorithms for the library?
How efficient is it?
The simplest image processing library would first allow loading an image into memory, then provide pixel-level access to the data (read and write), and finally allow storing the data back to disk. Advantages of such a scheme include a simple set of interfaces and (given enough memory or small enough images) nearly optimal computational efficiency. Disadvantages include high memory usage and therefore poor scaling with image size or number of images.
If memory usage is not a problem, this would be the optimal way of dealing with images. Unfortunately, memory cannot yet be considered infinite for many image-processing demands. As an example, the very first test of my last commercial library was to load 1200 images consisting of 300 gigabytes of data and display them at once. Actual processing was done in blocks of images to avoid having a failure wipe out weeks of processing time. In an attempt to avoid repeating historical blunders, I would never say that no one will ever have several hundred gigabytes of memory. I imagine when that time comes, people will work with even larger data sets.
In handling large images, many proprietary libraries reduce memory usage by sacrificing ease of use and efficiency. Great lengths can be taken to reduce the loss, but all such libraries are at a slight disadvantage in these areas and LIMP is no exception.
By learning from past experiences, many interfaces in LIMP have been designed to promote speed-enhancing optimizations as well as to group complex code into a few internal locations, where they can be more easily maintained. Because complex code is grouped into reusable templates, many types of functions and conversions can be written without having to deal with any complexity that would normally be encountered in large image processing.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide