Comparison with Performance comparison Frameworks This is definitely the most difficult page in the guide to write, but we do feel it’s important. Odds are, you’ve had problems you tried to solve and you’ve used another library to solve them. You’re here because you want to know if Vue can solve your specific problems better.
That’s what we hope to answer for you. We also try very hard to avoid bias. As the core team, we obviously like Vue a lot. There are some problems we think it solves better than anything else out there. If we didn’t believe that, we wouldn’t be working on it.
We do want to be fair and accurate though. If you notice an inaccuracy or something that doesn’t seem quite right, please let us know by opening an issue. Being so similar in scope, we’ve put more time into fine-tuning this comparison than any other. We want to ensure not only technical accuracy, but also balance. We point out where React outshines Vue, for example in the richness of their ecosystem and abundance of their custom renderers.
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With that said, it’s inevitable that the comparison would appear biased towards Vue to some React users, as many of the subjects explored are to some extent subjective. We acknowledge the existence of varying technical taste, and this comparison primarily aims to outline the reasons why Vue could potentially be a better fit if your preferences happen to coincide with ours. React community to revamp this section in the near future. In Vue, a component’s dependencies are automatically tracked during its render, so the system knows precisely which components actually need to re-render when state changes. Overall this removes the need for a whole class of performance optimizations from the developer’s plate, and allows them to focus more on building the app itself as it scales.
This approach has its own benefits, but also comes with various trade-offs that may not seem worthwhile for every developer. Vue embraces classic web technologies and builds on top of them. To show you what that means, we’ll dive into some examples. JSX is in some ways more advanced than what’s currently available for Vue templates. In Vue, we also have render functions and even support JSX, because sometimes you do need that power. However, as the default experience we offer templates as a simpler alternative. For many developers who have been working with HTML, templates feel more natural to read and write.
The preference itself can be somewhat subjective, but if it makes the developer more productive then the benefit is objective. HTML-based templates make it much easier to progressively migrate existing applications to take advantage of Vue’s reactivity features. It also makes it much easier for designers and less experienced developers to parse and contribute to the codebase. On a higher level, we can divide components into two categories: presentational ones and logical ones.
The percentage of these components depends on the type of app you are building, but in general we find presentational ones to be much more common. Single-file components give you full access to CSS in the same file as the rest of your component code. Lastly, the styling in Vue’s single-file component’s is very flexible. React instead chooses to leave these concerns to the community, creating a more fragmented ecosystem.
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Being more popular though, React’s ecosystem is considerably richer than Vue’s. It does not allow any configuration during project generation, while Vue’s project templates allow Yeoman-like customization. It only offers a single template that assumes you’re building a single-page application, while Vue offers a wide variety of templates for various purposes and build systems. It cannot generate projects from user-built templates, which can be especially useful for enterprise environments with pre-established conventions. It’s important to note that many of these limitations are intentional design decisions made by the create-react-app team and they do have their advantages.
You can read more about the differing philosophy here. Then you can start writing Vue code and even ship the minified version to production without feeling guilty or having to worry about performance problems. Since you don’t need to know about JSX, ES2015, or build systems to get started with Vue, it also typically takes developers less than a day reading the guide to learn enough to build non-trivial applications. This is great in that as a developer, you can apply your knowledge of a framework across multiple platforms. At this moment, Weex is still in active development and is not as mature and battle-tested as React Native, but its development is driven by the production needs of the largest e-commerce business in the world, and the Vue team will also actively collaborate with the Weex team to ensure a smooth experience for Vue developers.
With MobXMobX has become quite popular in the React community and it actually uses a nearly identical reactivity system to Vue. MobX workflow can be thought of as a more verbose Vue, so if you’re using that combination and are enjoying it, jumping into Vue is probably the next logical step. For that reason, the vast majority of comparisons above will also apply to them. The main difference will typically be a reduced ecosystem, often significantly, compared to React. This is because there were a lot of things that AngularJS got right and these were an inspiration for Vue very early in its development.
Learning enough to build non-trivial applications typically takes less than a day, which is not true for AngularJS. While this makes Vue more adaptable to a wide variety of projects, we also recognize that sometimes it’s useful to have some decisions made for you, so that you can just start coding. That’s why we offer a webpack template that can set you up within minutes, while also granting you access to advanced features such as hot module reloading, linting, CSS extraction, and much more. This makes the flow of data easier to reason about in non-trivial applications. Directives are meant to encapsulate DOM manipulations only, while components are self-contained units that have their own view and data logic.
In AngularJS, directives do everything and components are just a specific kind of directive. AngularJS becomes slow when there are a lot of watchers, because every time anything in the scope changes, all these watchers need to be re-evaluated again. Vue doesn’t suffer from this at all because it uses a transparent dependency-tracking observation system with async queueing – all changes trigger independently unless they have explicit dependency relationships. Interestingly, there are quite a few similarities in how Angular and Vue are addressing these AngularJS issues. We have a separate section for the new Angular because it really is a completely different framework from AngularJS.
For example, it features a first-class component system, many implementation details have been completely rewritten, and the API has also changed quite drastically. In many smaller-scale use cases, introducing a type system may result in more overhead than productivity gain. You can browse specific metrics for a more granular comparison, but speed is unlikely to be a deciding factor. Many developers enjoy this freedom, while some prefer having only one Right Way to build any application.
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With these basic skills, you can start building non-trivial applications within less than a day of reading the guide. Angular’s learning curve is much steeper. The API surface of the framework is huge and as a user you will need to familiarize yourself with a lot more concepts before getting productive. The complexity of Angular is largely due to its design goal of targeting only large, complex applications – but that does make the framework a lot more difficult for less-experienced developers to pick up. It provides a lot of established conventions and once you are familiar enough with them, it can make you very productive.
However, it also means the learning curve is high and flexibility suffers. It’s a trade-off when you try to pick between an opinionated framework and a library with a loosely coupled set of tools that work together. In Ember, you need to wrap everything in Ember Objects and manually declare dependencies for computed properties. Performance-wise, Vue outperforms Ember by a fair margin, even after the latest Glimmer engine update in Ember 2. Vue automatically batches updates, while in Ember you need to manually manage run loops in performance-critical situations. Over time though, Knockout development has slowed and it’s begun to show its age a little.
For example, its component system lacks a full set of lifecycle hooks and although it’s a very common use case, the interface for passing children to a component feels a little clunky compared to Vue’s. There also seem to be philosophical differences in the API design which if you’re curious, can be demonstrated by how each handles the creation of a simple todo list. It’s definitely somewhat subjective, but many consider Vue’s API to be less complex and better structured. Vue’s components can be loosely compared to Polymer’s custom elements and both provide a very similar development style. In Polymer, the team has also made its data-binding system very limited in order to compensate for the performance. For example, the only expressions supported in Polymer templates are boolean negation and single method calls. Its computed property implementation is also not very flexible.
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Riot and Vue probably share a lot in design philosophies. Riot traverses a DOM tree rather than using a virtual DOM, so suffers from the same performance issues as AngularJS. Caught a mistake or want to contribute to the documentation? Enter the characters you see below Sorry, we just need to make sure you’re not a robot. I did this for multiple GPU’s. You can press the figure to get a link to the performance table that is hosted with Google Docs.
2Gb of RAM is very nice. It is clear that GPU computation is BLOODY fast. But i HAVE to note, that only a SINGLE core of the CPU’s were used for the normal CPU functions. These algo’s have not really been optimized for multithreaded if I’m not mistaken. 20x is too much for any intel CPU to catch up with.
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GPU Computing is a must if fast image processing is important. Using 2xGTX670’s, you can use 2688 CORES. That means that if you don’t keep your GPU’s on a leash it might become self aware. OpenCV only natively supports 1 GPU per function, but ofcourse you can use more if you want. Luckily, If you know your way around OpenMP, it is quite easy to implement. You can use more GPU’s in OpenCV, there are some functions wich you can use with it. Specialization in computer vision and deep learning.
I realise that this article is quite old now but as you have updated the times including results for a GTX770 and Titan X I have a couple of questions if you have time? I am trying to get similar results to yours but I am not having much luck. 4000 8U image and I would expect both to be quicker than the CPU’s you tested on in 2012. Were you using OpenCV with the IPP’s installed or without? IIP isnt that interesting anymore, but TBB is. Check if you build with tbb. I am unsure whether i a have used those.
Do not underestimate the 2600K though, it worked with fast memory and it’s as decent as any i5. It will not easily beat an i7 though. Thanks for the quick reply, as you have a lot of experience with OpenCV I would like to pick your brain. My set up is an i7 6700HQ, DDR4, GTX 980M on windows, with OpenCV3. 0 compiled with TBB and CUDA SDK 7.
I think their may be something wrong with the calculation of the integral image using the CPU on my set up, i7 6700HQ, DDR4. My real interest is the CUDA comparison which I am having difficulty recreating on my set up, did you use a host or GPU timer? Your times on the GTX770 are 10 times quicker than this, can you share your test code? 4000 being one the calculation of the integral image using CUDA is incorrect, did you validate the results? 0’s integral function has an optimised SSE version, if the integral image type is an integer, I am assuming that your output image type was 32S? 1 when the integral image is a float.
Do you know a source that explains and compares other processing steps that needed for image stitching, like the feature matcher, bundle adjustment and SIFT feature extraction? Could you by any chance make the benchmark source code available. I’d like to run it on some older gpu’s I have. 2256 , author compares various GPUs for few openCV functions. What Aspects of Performance Should Be Measured?
What Problems Do Companies Want to Solve with SPM? Manufacturing companies have been using supplier scorecards to measure basic supplier performance metrics for a long time. In the past decade, however, both manufacturing and service firms have become increasingly aware of the importance of supplier performance and its critical impact on their own performance and market competitiveness. Good supplier performance is a key ingredient in enabling firms to achieve business performance excellence. But how can firms manage or even influence the performance of outside suppliers? Many companies pursue SPM as the quest for the perfect supplier scorecard.
They believe that if they get the right metrics on the scorecard, then supplier performance will improve. SPM involves more than supplier scorecards, which are only one element in the process. Successful results include: reduced costs, reduced risks, and increased value. Some firms want to implement SPM because they have been told that it’s the right thing to do.
They are convinced that supplier performance will improve and the results will speak for themselves. While supplier performance improvement is a distinctly possible outcome, a specific cost reduction based on that improvement can’t be guaranteed because different firms implement SPM with varying degrees of proficiency. Then, interpolate how much these costs could be reduced by implementing SPM. This gives senior management information on what types of savings can be expected and a better idea of cost of SPM versus return on investment. Approach 2 – Another approach to calculating the ROI of SPM is to estimate a poor-performance-to-cost ratio. For every dollar spent on critical suppliers, what percentage is lost to poor performance factors, such as poor delivery or poor quality performance?
Or, how many fewer sales dollars would you need to make up for poor supplier performance? While ROI estimates are just that, estimates, they demonstrate the types of cost benefits that SPM can bring in monetary terms. As an SPM project is implemented and progresses, it is important to track ROI in order to maintain senior management support. Not all improvements are measurable in dollars. Many, such as improved supplier relationships, are qualitative and equally valuable, and are likely to lead to measurable savings.