The Evolution of 3D Rendering and Why You Need to Consider It

When it comes to the world of rendering, it is easy to say that 3D rendering has by far become the most popular with graphic designers, filmmakers, architects and etc. This rendering is gradually changing the dynamics of engineering and creative graphic industries.

This type of rendering manipulates images in a three dimensional manner, promoting a more realistic visual effect. Here are few of the many reasons why you should consider using a cloud rendering for 3D rendering.

Reduced Design Cycles

3D rendering can make solid modeling possible which eventually shortens the design cycle and ultimately results in a streamlined manufacturing process. Using cloud rendering services for 3D visuals leaves you with a highly qualified team that can bring out the best of your project.

High Quality with Low Costs

One of the best reasons to consider 3D rendering is that you will be not only able to save time, but also acquire a quality 3D product that can make waves. Moreover, the costs involved in this type of rendering are significantly lower. What more could you want when you have access to the best quality services in the market at a cost effective rate!

Online Publications

3D rendered projects can easily be published online, without hassle. These can help you attract attention via social media and access to a potential clientele who can pay top dollar for your images.

At Fox Renderfarm we offer pioneering self-service cloud computing for rendering, research into cluster rendering, parallel computing technology and computing services for cloud rendering.

If you are considering keeping up with technology by using 3D rendering in your projects, cloud is the way to go! If you are considering the services of online or cloud rendering for your project, or even a Fox Render Farm, contact us today at our 24-hour hotline:+86 130 5800 5448 or at service@rayvision.com and we will be happy to be of service!

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The Future of 3D Rendering is in the Cloud
The Future of 3D Rendering is in the Cloud
When we think of 3D animation, we imagine an artist sitting at a work station plugging away in software like 3DS Max or Cinema 4D. We think of pushing polygons around, adjusting UVs and key frames. We imagine the beautifully rendered final output. What we don't often think about is the hardware it takes to render our art. Illustrators may get by rendering stills on their own workstation. But, rendering frames for animation requires multiple computers to get the job done in a timely manor. Traditionally, this meant companies would build and manager their own render farms. But having the power to render animations on-site comes with a considerable price. The more computers that have to be taken care of, the less time there is to spend on animation and other artistic tasks. If a is large enough it will require hiring dedicated IT personnel. Well-funded studios might buy brand-new machines to serve as render slaves. But, for smaller studios and freelancers, render farms are often built from machines too old to serve as workstations anymore. Trying to maintain state-of-the-art software on old machines is often challenging. Even when newer equipment is used, there is still a high energy cost associated with operating it. The electricity required by several processors cranking out frames non-stop will quickly become expensive. Not to mention, those machines get hot. Even a single rack of slaves will need some type of climate control. These issues have made many animation companies see the benefits of rendering in the cloud. As high-speed internet access becomes available across the globe, moving large files online has become commonplace. You can upload a file to a render service that will take on the headaches for you. They monitor the system for crashes. They install updates and patches. They worry about energy costs. Plus, there is the speed advantage. Companies dedicated to rendering are able to devote more resources to their equipment. Their farm will have more nodes. Their hardware will be more up-to-date and faster. The solution provides couldn't come fast enough. There seems no end to the increasing demands made on render hardware. Artists and directors are constantly pushing the limits of 3D animation. Scenes that may have been shot traditionally a few years ago are now created with computer graphics to give directors more control. With modern 3D software it's easier to meet those creative demands. Crashing waves in a fluid simulation, thousands of knights rushing towards the camera or millions of trees swaying in the wind might be cooked up on a single workstation. But, even as software improves, processing those complex scenes takes more power than ever before. It's not only artistic demands from content creators putting render hardware through its paces. The viewing public has enjoyed huge improvements to display resolution in recent years. The definition of high-definition keeps expanding. What many consider full HD at 1920 x 1080 is old news. Many platforms now support 4K resolution at 4096 x 2160. In many cases, that's large enough for hi-res printing! The public is also getting use to higher frame rates. For years, the industry standard for film has been 24 frames per second (fps). But, in 2012, Peter Jackson shot The Hobbit: An Unexpected Journey at 48 fps. While some prefer the classic look of 24 fps film, animators have to prepare for higher frame rates becoming standard. Everything is pointing towards could soon be as uncommon as hosting your own website. It's something that is simply better done by a dedicated company. Welcome to the age of cloud rendering. About Author :Shaun Swanson - who has fifteen years of experience in 3D rendering and graphic design. He has used several software packages and has a very broad knowledge of digital art ranging from entertainment to product design. This article posted on http://goarticles.com/article/The-Future-of-3D-Rendering-Is-in-the-Cloud/9416094/
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2014-09-11
Linux’s Place in The Film Industry
Linux’s Place in The Film Industry
In 1991, a student named Linus Torvalds began developing a new operating system as a hobby. That hobby, which would later be called Linux, forever changed the world of computers. Since Linux is open source, anyone can license it for free and modify the source code to their liking. This has made Linux one of the most popular operating systems in the world. Linux is everywhere. The web server maintaining this page is very likely Linux based. You may have a version of Linux in your pocket right now. Google’s Android operating system is a modified version of Linux. Several world governments use Linux extensively for day to day operations. And, many would be surprised to learn that Linux has become the standard for major FX studios. In the early 90s, Hollywood studios relied on SGI and its Irix operating system to run animation and FX software. At the time, Irix was one of the best systems available for handling intense graphics. But a change was about to sweep through the computer industry. Windows began to dominate the business world, and Intel began making powerful chips at a lower price point. These market forces made expensive SGI systems hard to justify. When studios began looking for a system to replace Irix, Windows wasn’t an option due to its architecture. The proprietary software in place at many studios was written for Irix. Since Irix and Linux were both Unix based, porting that software to Linux was easier than porting to Windows. Render farms were the first to be converted. In 1996, Digital Domain was the first production studio to render a major motion picture on a Linux farm with Titanic. DreamWorks, ILM, Pixar and others quickly followed. Workstations were next for Linux once artists realized the performance boost in the new operating system. Under pressure from studios, commercial software vendors got on board and started releasing Linux compatible versions. Maya, Houdini, Softimage and other popular 3D applications quickly became available for Linux. By the early 2000s, most major studios were dominated by Linux. While Windows and Mac environments are still used for television and small independent films, practically all blockbuster movies are now rendered on Linux farms. Linux has many advantages for render farms. The obvious benefits are cost and customization. Since Linux is free to license, startup costs are greatly reduced compared to commercial systems. And, since Linux is open source, completely customized versions of the operating system are possible. There are other advantages. Linux machines can multitask well and are easy to network. But the single greatest advantage is stability. Unlike other operating systems, Linux doesn’t slow down over time. It is common for Linux machines to run for months, yes months, without needing a reboot. With all these advantages, it’s surprising to learn many online render farms still haven’t embraced Linux. While a handful of farms like Rebus, Rendersolve and Rayvision support Linux, Windows is still the most common environment for cloud rendering services. It’s not likely anything will replace Linux’s role in the film industry soon. Studios are heavily invested in Linux with millions of lines of custom code. While anything is possible, it would take another industry change akin to the PC revolution to shake Linux from its place in Hollywood. The story of Linux is almost like a Hollywood movie itself. It shows us that anything is possible. It’s hard to believe that a simple student project forever changed the world of computers and became the backbone of the film industry. About: The author, Shaun Swanson, has fifteen years of experience in 3D rendering and graphic design. He has used several software packages and has a very broad knowledge of digital art ranging from entertainment to product design. If you want to know more about 3D Rendering, Follow us on Facebook, Linkedin.
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2014-10-21
Three Aspects to See the Differences Between GPU and CPU Rendering(2)
Three Aspects to See the Differences Between GPU and CPU Rendering(2)
2. The Magic Inside of CPU and GPU The above picture origins from the NVIDIA CUDA document. The green color represents computing units, orange-red color represents storage units, and the orange color represents control units. The GPU employs a large number of computing units and an extremely long pipeline, with only a very simple Cache-free control logic is required. But CPU is occupied by a large amount of Cache, with complex control logic functionality and lots of optimization circuits. Compare to GPU, CPU’s computing power is only a small part of it. And that’s most of the GPU's work look like, large work load of computation, less technique but more repetitive computing required. Just like when you have a task of doing the number calculation within 100 for a hundred million times, probably the best way is to hire dozens of elementary school students and divide the task, since the task only involve repetitive work instead of highly-skills requiring. But CPU is like a prestigious professor, who is proficient in high-level of math field, his ability equals to 20 elementary students, he gets higher paid, of course. But who would you hire if you are Fox Conn’s recruiting officer? GPU is like that, accumulates simple computing units to complete a large number of computing tasks, while based on the premise that the tasks of student A and student B is not dependent on each other. Many problems involving large numbers of computing have such characteristics, such as deciphering passwords, mining, and many graphics calculations. These calculations can be decomposed into multiple identical simple tasks. Each task can be assigned to an elementary school student. But there are still some tasks that involve the issue of "cloud." For example, when you go to a blind date, it takes both of the parties’ willingness to continue for further development. There's no way to go further like getting married when at least one of you are against this. And CPU usually takes care of more complicated issue like this. Usually we can use GPU to solve the issue if it’s possible. Use the previous analogy, the speed of the GPU's computing depends on the quantity of students are employed, while the CPU's speed of operation depends on the quality the professor is employed. Professor's ability to handle complex tasks can squash students, but for less complex tasks with more workload required, one professor still can not compete with dozens of elementary students. Of course, the current GPU can also do some complicated work, which is equivalent to junior high school students’ ability. But CPU is still the brain to control GPU and assign GPU tasks with halfway done data 3. Parallel Computing First of all, let's talk about the concept of parallel computing, which is a kind of calculation, and many of its calculation or execution processes are performed simultaneously. This type of calculation usually can divide into smaller tasks, which can then be solved at the same time. Parallel computing can co-process with CPU or the main engine, they have their own internal storage, and can even open 1000 threads at the same time. When using the GPU to perform computing, the CPU interacts mainly with the following: Data exchange between CPU and GPU Data exchange on the GPU In general, only one task can be performed on one CPU or one GPU computing core (which we usually call “core”) at the same time. With Hyper-Thread technology, one computing core may perform multiple computing tasks at the same time. (For example, for a dual-core, four-thread CPU, each computing core may perform two computing tasks at the same time without interruption.) What Hyper-thread do is usually double computing core’s logical computing functionality. We usually see that the CPU can run dozens of programs at the same time. In fact, from a microscopic point of view, these dozens of programs are still dependent and serial computing supported, such as on four-core and four-thread CPU. Only four operations can be performed at a time, and these dozens of programs can only be executed on four computing cores. However, due to the fast switching speed, what is shown on the macroscopic is the superficial fact that these programs are running “simultaneously”. The most prominent feature of GPU is: large numbers of computing cores. The CPU's computing core is usually only four or eight, and generally does not exceed two digits. The GPU computing core for scientific computing may have more than a thousand calculation cores. Due to the huge advantage of the number of computing cores, the number of computations that the GPU can perform far outweighs the CPU. At this time, for those calculations that can be performed in parallel computing, using the advantages of the GPU can greatly improve the efficiency. Here let me explain a little bit of the task's serial computation and parallel computation. In general, serial computing is doing the calculation one by one. In parallel computing, several parallel computing is to do it simultaneously. For example, to calculate the product of real number a and vector B=[1 2 3 4], the serial calculation is to first calculate a*B[1], then calculate a*B[2], then calculate a*B[3], and finally calculate a*B[4] to get the result of a*B. The parallel calculation is to calculate a*B[1], a*B[2], a*B[3], and a*B[4] at the same time and get a *B results. If there is only one computing core, then four independent computing tasks cannot be executed in parallel computing, and can only be calculated in serial computing one by one; But if there are four computing cores, four independent computing tasks can be divided and executed on each core. That is the advantage of parallel computing. Because of this, the GPU has a large number of computing cores, and the scale of parallel computing can be very large, computing problems that can be solved by parallel computing, which showing superior performance over CPU. For example, when deciphering a password, the task is decomposed into several pieces that can be executed independently. Each piece is allocated on one GPU core, and multiple decipher tasks can be performed at the same time, thereby speeding up deciphering process. But parallel computing is not a panacea, it requires a premise that the problems can be executed in parallel computing only when they are able to be decomposing into several independent tasks, which many of them are not able to be decomposed. For example, if a problem has two steps, and the second step of the calculation depends on the result of the first step, then the two parts cannot be executed in parallel computing and can only be sequentially executed in sequence. In fact, our usual calculation tasks often have complex dependencies that can not be parallel computed. This is the big disadvantage of the GPU. About GPU programming, there are mainly the following methods:
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2018-08-22
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