New page about Side Effects Houdini

A New Page Has Arrived!

Check out the latest addition to my blog, all dedicated to the vast and mind-bending

Houdini FX 3D package from Side Effects Software…

https://baltazaar.wordpress.com/houdini/

houdini_indie_crest

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Are You a True Supporter or just skimming the cream?

Hi there!

There are a lot of cool Open Source projects out there these days that usually produce some sort of free (as in beer) software, being it a small tool, a plugin for another application or a complete application by it self. Some of it is of poor quality, but this is definitely not the rule.

Most people don’t really think longer than that they get this piece of software for free, and how great it is to not have to pay for things.

What I’m trying to convey with this post is that there are usually an immense amount of work behind all of these projects and usually the developers and designers don’t earn much, if anything for doing this work.

Would you do that?
Would you spend maybe 50 – 90 % of your spare time working for free? Even if you believed in what you where doing it’s quite a commitment.

My point is that it is actually possible to donate a tiny sum of money once in a while or each month automated via PayPal if you can afford, at least for those software projects you’re using day in, day out without paying a cent in licence or maintenance for doing.

A an example:

I use and enjoy Blender. It has become a rather huge project with a lot of good developers working on the project, and it has even got a couple of full-time, paid developers to work on it. And if you haven’t discovered the cool Gooseberry Project, take a visit to their homepage and look around a little.

The Blender Foundation and the Gooseberry Project is cooperating on many levels these days, there is even a special Gooseberry edition of Blender that you can download from the Blender Build-bots.  There is a lot happening to the former Blender Shop where you could buy tutorials and books from individual companies as well as from more or less community acclaimed Guru figures from the “Blender world”, in addition to accessories like T-Shirts and other apparel.

Now there is a really interesting project going on called the “Blender Cloud”, that merges many of the sources of information and especially components from the Open Movie projects like Sintel, Tears of Steel and much more. They have a subscription based service that is in its infancy yet, but fully worth the price of around $9 / month already, as you can view and download many of the earlier products sold on the Blender Shop and maybe the most valuable, you get direct access to all assets used when making movies like Sintel. Complete with finished models, scenes, scripts, concept art and absolutely everything that was stored while producing these projects.

Now this is a SUPERB way to learn how a real world project like this gets done.

So, show some real commitment to the Blender Foundation, by subscribing to the Blender Cloud ever expanding service of useful resources.
(The only drawback is that you have to pay for the first three months in one go the first time you open an account. After 3 months without paying anything it’s the usual 9 something dollars per month.)

blender_cloud

The MadMan is back.

 

More Madness

 

When I read my last post I must say I really must have had a boost of optimism and god-like energy over me back then, eh?

Books to be written, 3D art to be made, Allegorithmic products to review, Python programming, Blenders Game Engine, Modo stuff.. Phew!

Well. I have actually done something. I won’t present anything here today, but some things stayed and some things did not.

I’m still into writing the book series, that’s one. (But it will be a bit postponed)

What I’m still into is off course Blender! It has gotten a LOT of updates the last seven months.
I’m still willing to create a tutorial series on the BGE (Blender Game Engine) which we all know is programmed with Python. Actually A LOT of Blender is programmed with Python now. It’s just the low level bits and pieces left in hard core C / C++ (?asm?).

I was planning on starting on this in a couple of weeks and off course post it on this blog, if it is any interest for it.

As some of you know, I’m a software developer, mainly using C# and C++, but I’ve also been using some Python through the years.

Lately I’ve been keen on learning to program GAMES. What kind of games? No idea. Just games. Fun programs that can include gaming elements, like “gamification” or something.

So I’ve downloaded the latest Unreal 4 Engine, but that was totally overkill for me. I need to know the basics first.

Even Unity seems a bit cryptic to me sometimes. But that’s mostly because of the horrific implementation of C# / .Net they’ve chosen. Goes against all good usage patterns.

So now I’m going to test out Blenders Game Engine, to see if I can make any sense of that.

Along the way will be some tutorials and some Python code for those interested.

 

Also I’ll look into some rendering with Cycles, Thea Render and LuxRender…

 

Until next time…

Blender Logo

 

 

 

 

 

Modo Logo

What do you know, now I’m back on the Modo Track…

To cite myself in my last post:

“Then we have Modo. Once my favorite polygonal modeler, back in the version 3 days. Now the whole company has been snatched by The Foundry, and all clues given leads in the direction of massive feature and price increases. So, no more Modo for me I guess.”

Then, out of curiosity, I visited the new Modo Site to check out how things where looking and what do I see?

Modo upgrades at a 40% rebate…
So, after bragging about Modo 701’s ability to run smooth on Linux in an earlier post I decided to get my CC and order the upgrade from my commercial 401 license to the latest 701 SP3. Hey, it was like $220 or something… Can’t let that slip by when I’ve always wanted to learn the ins and outs of Modo. I’ve been using it since the second version, so I figured, what a heck, it’s Christmas!

Besides, I needed a stable modeler to run on Linux besides Blender.

I mean, Blender is an awesome project, but that’s the thing. It’s a “Project” in a constant state of flux more than it is a stable “Product”, one version does things different from the next, not just in terms of the details but in the core implementation. So I find myself using about double the time on modeling something in Blender compared to using Modo, and as we all know time is our most precious asset in life.

So this winter I’ll get up to speed on the new Bullet physics implementation, the sculpting tools, the Python and the C++ API’s and the rest of this polished package.

I’ve also purchased the “Substance Designer”  version 4, which has very smooth integration with the Modo shading system, and it’s a joy to use.
I urge you to try it out, either as a trial or as a non-commercial learning tool for $99.

My goal in the world of 3D is to gather set of tools that makes a “good enough” pipeline, suitable for artists on a semi to low budget, that does things clean and efficiently and produces output that’s “on par” with expensive packages like Autodesk’s suites and tools like NukeX. Modo will definitely be a large part of this pipeline, but so will Blender and the Substance Designer from Allegorithmic.

Blender has some good compositing tools, Modo is the king of UV’s and texturing and is starting to get quite good on things like rigging and animation as well.

I believe that it should be perfectly possible to get a complete 3D pipeline of good tools for around $1000. If 3D is what you wish to do for work or as an advanced hobby, that’s not a bad price to pay. But you’ll need to be constantly monitoring the extreme offers from the tool producers via newsletters, RSS and forums.

I’m so sick of reading about a semi-professional artist with an image posted in magazines like 3D World and when they list the products used you see things like: 3DS Max, V-Ray, Nuke, Mari, Photoshop and ZBrush.
Should I seriously believe that this artist has legal licenses for software with an estimated value of $15.000 – 20.000? Seriously doubt it.
And if so, it’s NOT WORTH IT! Go figure, man!

Learn how to draw, learn Blender, Gimp and a semi-expensive package like Modo or Lightwave and you’ll be able to create just as nice results.

It’s in the hands and the mind of the artist, not in the tools. Only a poor craftsman blames his tools.

Until next time, have a wonderful Christmas you all and remember: Never stop learning!

40% off on Modo 701!

40% off on Modo 701!

 

 

 

 

 

The MadMan.

 

 

 

The Future Of Decent 3D Software for Enthusiasts and Professionals alike

Blender Image
Create what you want for free!

Ok. We all know and love Blender. But let’s face it, it has its quirks that needs some ironing out before it can totally     replace a package like Autodesk XSI or Autodesk Maya for many professional artists, or at least they think it does.

Don’t get me wrong here, it is definitely the tool of choice if you want an open source solution that can do “almost      anything”.

This is not to say that Blender has missing features, on the contrary, it has features that Maya tries to include, but has shipped with pretty serious bugs since around version 2009.
Now they have snatched a perfectly good plugin called NEX and “integrated” a.k.a. “slammed” it on top of their existing buggy tools, creating somewhat of a monster. Again. Those guys really never seem to learn the basics of Software Lifecycle Management. I’ve tried using it, but it’s so buggy that it ruins my models from time to time.

The 2014 Edition is already in SP3 and still bugs aren’t sorted. This is a frustration for many small studios that depends on stable software and don’t have the resources to create their own in-house work-around using the SDK. But what do they do? Soon they’ll release a new 2015 version with even more features and new bugs on top of that.

Then we have Modo. Once my favorite polygonal modeler, back in the version 3 days. Now the whole company has been snatched by The Foundry, and all clues given leads in the direction of massive feature and price increases. So, no more Modo for me I guess. I have my stable commercial license for Modo 401 that never stops to be valid, but hey, things evolve right? Who knows if it will be compatible with the next operating system I’m forced to update to because of other applications demands?

It still amazes me to see a company like Newtek still being alive and actually even kicking these days with the new Chronosculpt and Nevronmotion applications that lets you sculpt in time based layers and record motion data via a cheap $99 Kinect Sensor! Way to go!
How much this will be used remains to be seen, but they are on a creative roll and they NEVER give up. That is the true spirit of life. In addition they’ve released a free update to existing v. 11 (maybe also v. 10?) customers in form of Lightwave 11.6.
This edition is the most groundbreaking release since version 9.6 in my eyes. It actually is a NEW LIGHTWAVE.
A lot of new cool modeling tools and a great deal of new features on the rigging and animation side with the introduction of the Genoma system and off course an updated integration of the Bullet Physics engine, so sorely needed.
To bad I only have an educational license for version 9.6. But they do have a good offer these days, letting me upgrade to 11.6 for around $350 or something.
But, it’s Christmas and I have other posts on my budget screaming for coverage…

When it comes to a simple, fast and stable polygonal modeler we have the option of using Silo. It’s a $159 application and is an extremely competent modeler application.
But it hasn’t been actively developed for over two years.
So the reason for starting this article was really to investigate the possibility to crowd-fund a purchase of the Silo source code from Nevercenter and turn it into an actively maintained Open Source project. Personally, I have an hour or two now and then for coding some C++, Python or whatever they’ve used to create the product.

The question is: How many others are in on a project like this?

I’ve posted a poll over at CG SOCIETY (CGTalk) and for those that would be so kind, I urge you to give your vote to one of the options presented there.

I’ve been lousy at posting new stuff to my blog lately and probably have lost a lot of readers, but hopefully some remain truthful to the old madman.

Here is the link to the post on CGC:

Poll for Open Sourcing Silo 

For those that does not have a CGC account, get one! Kidding.

I’ll present the poll in this post as well, though it won’t be linked with the results on CGC, so the best is if you take the time to register over there. They even have a $19 / Year deal for a premium subscription right now (normally $79) with a lot of goodies.

All the best,

The MadMan

Parallel Computing With CUDA Extensions (Part 2)

Parallel Computing With CUDA Extensions Part 2

A “kernel” in CUDA terms can be thought of as a series of instructions to be carried out
by a computation unit on a GPU. Basically a regular program.

1. You write kernels / code as if it where regular serial (top to bottom) programs, like
those designed to run on a single thread.

2. You tell the GPU to launch this code, and it will do so on multiple threads. (you can define how many)

But won’t this lead to the same code being executed multiple times?
Yes, it will, but that will be explained later.
If you write code to output “Hello World” and ask the GPU to run this on 50 threads,
you’ll be greeted back 50 times, and it will all happen in parallel.

But let’s say that your program code contains a vector of 50 floats.
For each of those floats you want something done, the same way, as fast as possible..
You tell the GPU to spawn 50 threads to work on this kernel (program code).

Inside the kernel when run on the GPU, each thread can connect to one vector member, and have full control over which thread works with what member of the vector.

Each thread starts doing the work as instructed in the code received from the CPU.

As an example, let’s say that when running the code on a CPU only, the CPU would have to traverse the vector members one by one, do the job needed, and continue on to the next member to do the same.
The total execution time for the task would vary based on how busy the CPU was and other factors, but let’s assume that we have 50 members that each needs 10 ms to be processed.

Easily this would take 50 x 10 ms (500 ms) to complete, as we work in a non-parallel way.
If we compare this to how the execution would be done in a GPU assisted way,
the time needed to process each element might be a bit higher, because of the general fact that the working unit will not be as fast as a regular CPU thread, so let’s say 20 ms per member.

The difference is that because these tasks are all started in parallel, they would finish processing the whole vector of 50 members in just 20 ms compared to the CPU, that would need to use 10 ms x 50 members, giving us 500 ms!

To not loose focus, it might help
to visualize situations in programming that could benefit from being able to do several equal tasks at the same time.

One thing that comes to my mind is in image editing applications. When you have an image consisting of millions of pixels, there will be several thousand pixels that share the same characteristics / properties, like color and brightness.
If you where to write a function to lighten or change the color of all those equal pixels, you’d basically have a job that could benefit from being executed simultaneously, rather than doing the same thing to each pixel in a linear fashion.

Usually, when programming using only the CPU, launching and running threads in parallel is considered an expensive and cumbersome activity.
The whole point of using the GPU as a processing unit for “regular” tasks is that it’s very good at certain things, like these two:

1. Launch a lot of threads (and “a lot” is MANY, think thousands)
2. To actually run these threads in parallel

So GPU’s makes perfect candidates for doing the kind of processing that’s lacking in regular CPU’s.

For those learning about programming, maybe as a student or on their own, I seriously believe that there will be heavy demand for competent C/C++ programmers that knows how to program using GPU assistance soon, and also into the unforeseeable future.

C and C++ might be lower-level than the languages you find most comfortable to use, but the truth is that even though these statically typed compiled languages has experienced a drop in general interest the last ten years, they’re now on the rise again thanks to technologies like this and because of the importance of power consumption / watts per cycle on modern handheld devices.

C++ is the most efficient language to use for low power consumption devices (if done right) compared to any other high-level language in existence today, and many large companies invests huge sums of money to the driving forces behind these languages now.

The future is mobile and the future is (hopefully) green.
To achieve this, we also need to start making software that’s green and environmentally friendly.

I hope this article has made you more interested in learning about GPU assisted processing using tools such as CUDA or OpenCL.

There’s more in the world than an Apple.

Parallel Computing With CUDA Extensions (Part 1)

cuda_spotlight

Parallel Computing With CUDA Extensions (Part 1)

First, let’s see how to rate a CPU in a parallel way of thinking.

Let’s say we have an eight Core Intel CPU.
That’s:

With eight cores, you can execute 8 operations (Wide AVX vector operations) per core,
and each core has support for running two threads in parallel via Intel “HyperThreading” technology, so you get:

8 cores * 8 operations/core * 2 threads and end up with what’s called
“128-Way Parallelism”

For more about AdvancedVectoreXtentions (AVX) in CPU’s, check this page.

Programming without taking advantage of ANY multithreading / parallel processing
techniques, means that for each program you run, you use

2/128 = 1/64 of your CPU’s total resources (including the automatic “HyperThreading”).

In an ordinary C/C++ program you can only run code that uses the CPU as
the computing resource.
If people really took advantage of their cores and threading capabilities, this would
probably be enough for most regular applications, but for applications that does a lot of
heavy calculations, like video / image processing or 3D graphics it’s way better if you could
offload some of these tasks to the simpler (in terms of instructions), but well capable GPU(‘s) in your machine.

One way to do this is through the use of CUDA extensions.

In this model, the CPU is considered the “HOST” and each GPU is a “DEVICE”
in your system that can be used for doing calculations.
When such a program is compiled, instructions for both the HOST and any DEVICE
is created.
In CUDA the GPU/DEVICE is seen as a “CO-PROCESSOR” to the CPU/HOST.
The processor also assumes that the HOST and DEVICE has access to separate physical
memory where they can store data.
The DEVICE memory is typically a very high-speed block of memory, faster than the one
on the HOST.

The HOST is “In charge” in CUDA and sends messges to the DEVICE telling it what to do.
The HOST keeps track of:

Moving data:
1. From CPU memory -> GPU memory
2. Grom GPU memory -> CPU memory
CUDA’s version of C’s memcpy() is cudaMemcpy()
3. Allocating GPU memory
Again CUDA uses cudaMalloc() instead of malloc()
4. Launch “kernel” on GPU (in CUDA, the HOST launches “kernels” on the DEVICE)

A Typical flow in a CUDA Application would be something like:

1. CPU runs cudaMalloc on GPU
2. CPU copies input data from CPU->GPU with cudaMemcpy
3. CPU launches the transfered “kernels” on GPU (kernel launch)
4. CPU copies results back with cudaMemcpy

So, what is this “Kernel” stuff all about?

Guess we’ll find out in part 2 of this series…

Luxology / The Foundry Modo 701 now available for Linux

Modo Logo

Ok, so I was a little bit intimidated when I first heard about the Luxology merger with The Foudry. I was thinking in terms of Autodesk buying every small to medium Graphics Company and whirling it all up into one big package (or three).
But then I read about the fact that they had already thought about joining forces for some time and that they actually knew each other long before the fusion took place. Than it’s a whole other story the way I see things.
One of the best things that came rather instantly after the fusion was a Linux Edition of Modo 701! I could barely believe my eyes when I first read about this and how fast they produced a well functioning beta.
Now I’ve installed Modo 701 on both my mediocre laptop computer and on my main workhorse, both running openSuse 12.3 and it works beautifully.
If you’d like to try Modo on Linux, they have a free 15 day trial that you can download from, mark my words, The Foundry’s homepage.
If you want to get a little bit more out of the demo, I’d go for the $25 package that includes the program itself (for 30 days I think), lots of contents and a lot of tutorial videos of extremely high quality.

I’ve been waiting to upgrade my Modo 401 commercial license for some time now, because I’ve not really felt that the updates in the previous editions have been worth it from my point of view.

Don’t get me wrong, the updates have been substantial, but not the updates in the parts of the program I mainly use for my modeling work, besides I’ve been using Blender for pretty much everything.
But this is my reason to upgrade to 701! No doubt about it.

I encourage everyone to try out the new edition of this incredible 3D application, that has in relatively short time went from being a “modeling only” tool to a complete 3D Production Pipeline product, including physics, animation, very nice rendering and other cool stuff. Now supported on both Windows, OS X and Linux!

Here is an image of Modo 701 running on my openSuse KDE Desktop:

Modo On Suse 12.3

 

Here’s a video showing some of Modo’s modeling capabilities:

I’ve joined LinkedIn! Please help me build a professional comp. sci / CGI network!

Hi there, dear readers of this blog!

I’ve recently joined LinkedIn, and I’m trying to build a network consisting of the best in the computer science, software engineering and CG industries.

I’ve gotten a couple of big fish allready, but my network is still super-tiny!

I’m hoping to use this network for both socializing and for sharing technical issues and to present new thoughts and ideas for future products.

I believe a network fusion of these three industries will be an important avenue for anyone interested in fronting their work on the public arena, absolutely free.

So please join and invite anyone you think should be in there. Anybody with a passion and some talent in any of these three fields are very welcome to join!

So please check my profile and join my network over at:

http://www.linkedin.com/pub/chris-sederqvist/57/713/103/en
Vis Chris Sederqvist sin LinkedIn-profilVis profilen til Chris Sederqvist

Dynamic Parallelism in CUDA Version 5!

After using all my spare time on Blender lately, I’m now going to digress into another realm.

After reading a TechBrief at the Nvidia Cuda Developer Society I had to wrap my head around something other than modeling, just for a little while! 🙂

In CUDA Version 5, you can now call a CUDA kernel from within another, without going via the CPU.
The “parent” kernel will launch a “child” grid, which can itself also create new work to form an execution hierarchy. The “parent” will only signal as completed once all children are done processing.
The recursive depth will be dependent on your GPU resources.

So, Dynamic Parallelism in CUDA 5 enables the use of a CUDA kernel to create (and sync) nested work via the device runtime API for triggering other kernels, perform memory management on the device and create streams and events all without needing to use a single line of CPU code!
A CUDA Kernel can also call GPU Libraries such as CUBLAS directly, without any CPU intervention.

The Device Runtime API in CUDA C/C++ is a subset of the CUDA Runtime API for the Host, keeping the same syntax for easy code reuse.

Here is an example of calling a kernel from within a kernel:

__global__ KernelChild(void* data){
 //Do something
}
__global__ KernelParent(void *data){
 if (threadIdx.x == 0) {
 KernelChild<<<1, 32>>>(data);
 cudaThreadSynchronize();
 }
 __syncthreads();
 //Do something
}
// On Host
KernelParent<<<8, 32>>>(data);

Reducing the traffic between the GPU and CPU on the PCI bridge will bring a key performance boost for things like fluid dynamics simulations or similar stuff requiring pre-processing passes over the data.

GPU Computing rocks!

Happy Summer Holidays!