What I learned at Activision.

Today is my last day at Activision.

Not quite the end of an era, but my six-year stint at Activision|Blizzard|King has been by far the longest I’ve so far worked for a given company, and I wanted to write something about it.

I don’t usually do things like this, but long gone are the times where I pretended this blog could stay anonymous. Also, I do think that we should really talk more about our experiences with teams and companies, be more open. I’ve never done that on this blog (albeit I’ve always been happy to chat about anything in person), so let me quickly fix that.

This has been my, to be honest quite lucky, video-game career trajectory:

- Milestone (Italy, racing games). The indie company. Here, we were a family. And as most families, often loud and dysfunctional, sometimes fighting, but in the end, for me, it was always fair and always fun. We were pioneers, not because we were necessarily doing state-of-the-art things, but because nobody around us knew better, we had to figure out everything on our own. Eventually, that became a limiting factor for my own growth, but it was great to start there.

- Electronic Arts (Canada, Fight Night team). My team at EAC was probably the best example of a well-organized game studio. Everything was neat, productions went smoothly, and we created some quite kick-ass graphics as well. EA, and probably even more so EA Sports is truly a game developer. And by that I mean that it takes part in the game development, we had shared guidelines, procedures, technologies, resources. Of course, each team could still have plenty of degrees of freedom to custom-fit the EA way to their specific reality, but you always felt part of a bigger ecosystem and had access to this gigantic wealth of knowledge and people across the globe. Fun times!

- Relic (Space Marines, mostly). Relic had much more of the “indie” feel of my Milestone days. Not quite the same, we were a big team in an even bigger studio, part of a publicly traded company, but we were also exploring uncharted territory (for us), with very smart people and lots of last-minute hacking. I’m proud of what we achieved, it was fun and I loved the spirit we had in our rendering/optimization corner of the office. We did perhaps chew a bit more than we could though, doing something unprecedented for the studio. THQ was also failing fast, which didn’t help.

- Capcom (Vancouver). This studio is now closed. It was an unlucky choice for me, our project was riddled with all kinds of problems, in all possible dimensions. Eventually, I was laid off, then the project was canceled, and a few years later, the studio went down. It was a very stressful time, day after day I was quite unhappy. Still, I have to say I met some excellent people and I’m glad that I now know them!

- And now, Activision.

It’s the people.

One of the company mottos is “it’s the people”. I didn’t particularly think that these “values”, that all companies put forward, were particularly received in Activision’s case, at least when it came to Central Technology I always felt we didn’t pay much attention. But for me, it was the people, first and foremost.

Activision is the place where I found the smartest people around me, by far. And I’ve worked with very smart people, in great companies, but nothing touches this.

Now, I have to say, I have also a unique, very biased vantage point. Being a technical director in central technology means to interface mostly with the most senior technical people and the studio leadership. I was not in production and not working with a single studio. A different ballgame.

But still, I can’t even make list here! Ok, to give you a picture. ACME: Michael, Wade, Paul, Aki, I don’t think I can in a few words express the brilliance of these individuals. Paul’s technical abilities are unbeatable. He knows everything and can do anything. Wade is worse, infuriatingly smart, and any time I find the tiniest flaw in Michael’s life I have a sigh of relief (my girlfriend says he’s my work husband, even if I do have several man crushes to be honest, yes, there is a list). Aki started as an intern and was recently hired full time. I think he is already a better programmer than I am, and I definitely do not suffer from impostor’s syndrome...

Peter-Pike Sloan’s research team is the best R&D team I’ve ever seen, with people like Michal Iwanicki, Josiah Manson, Ari... But then again, I literally can’t make a list and I’m talking only about the rendering people, no actually, the rendering people I know best, even! My home team at Radical, which was a great studio in its own right, has been fantastic, Josh, Ryan, Tom, Peter, Andrew. CTN and that shadertoy genius of Paul Malin. And then the game teams. I mean, you can’t beat Drobot, can you? Jorge Jimenez! Dimitar Lazarov, Danny Chan, these are people who every project decide to completely change how Call of Duty renders things. Because why not, right? And of course, the people above me, Christer, whom I admired way before he landed at Activision but also Dave Cowling who hired me, and Andy our CTO who came to speak to us a year before he got hired, and even back then I thought he was an incredibly smart guy. And that's to speak just of the people in the Call of Duty orbit...

Truth is, if you know, you already know. If you don’t, it’s hard for me to tell you, so let’s just cut it short. Amazing people.

Not just smart.

Ok, so we have smart people, blah blah. What gives, are you just showing off or is there a point to this? There is actually.

And the lesson is not even “how to hire smart people” or that you should hire smart people. To be honest, I don’t think we have a way, even if Christer (and myself even) did put a lot of time and effort thinking about the process, you can only affect some multipliers I think. Mostly, teams seem to me to build up from the gravitational pull, around a given company culture. Once you have a given number of people that value given things, they tend to grab other people who also share the same core values, even if you want to have different points of view.

No, what Activision really taught me is that smart people don’t really matter, per se. Brilliance alone doesn’t mean that good things will happen to your products, actually, it can be dangerous because it can correlate with ego, especially if you haven’t reached the highest level of enlightenment yet.

What’s interesting about this particular bunch of smart people, is that they are also what actors call “grounded”. There is little bullshit going around. Tech is not made for tech’s sake. We don’t even have an engine, in a time where even if you really just have a game, one game, one codebase, you would call silly codenames each library and each little bit of tech, and maybe put some big splash screens before your main titles. Made with.. xyz.

This is actually a lesson that originates from the early days of Call of Duty at IW. From what I’m told, IW never named their engine, because any time you name something it becomes a bit more of a thing, and you start working for it. And they were a studio making games, the game is the thing. Nothing else.

This meshes just so well with the Activision way. So many people taught me so many lessons here, but the kind of fil rouge I found is this attention to what matters, when especially for us, rendering people, is so annoyingly easy to get distracted, quite literally, by the cool shiny thing that’s in the hype at the moment.

You might even call it ROI, I sometimes do even if perhaps I should not, it sounds “bad”. But you have to be aware of what matters, what are the trade-offs, what should you spend your time on. Which doesn’t mean you are a drone doing complex math in Excel, quite the contrary, there is definitely space for doing things because you want to, and you like to. 

Thing is, we cannot really compute the ROI of our tech stuff, especially for a thing that is so far from the product sales as rendering code is. But, we can be aware of these things. Even just thinking about them a bit makes a big difference.

Peter-Pike could be another example. I said I’ve never seen a better R&D team. Does it mean that there aren’t other teams that do research as good, or maybe even “better” sometimes? Of course, there are! The difference though is impact. Not only almost all our R&D work shipped on the given year’s title, but it’s also focused on what matters. Either help ship the title, technology that is needed to do what they want to do, or help the teams work better, technology to help productivity. Often both.

And this then again reflects also in the people. Yes, Peter-Pike is an accomplished academic, and his team does real research, meaning, things we don’t know how or even if we will solve. But he’s also incredibly pragmatic. He is hands-on coding all the times. More than I am. Better than I am!

Activision and Electronic Arts...

...couldn’t be more different, contrary to the popular belief that lumps all the three-four big publishers into the same AAA bucket.

And that is also what was interesting and life-changing. You see, I truly love them both. They are great in their own right and the results speak for themselves. They both have great people, I don’t even need to tell you that.

But they work so differently.

EA as I said, feels like a big game developer, a community. Sharing is one of the key values, finding the best ways of doing things, leveraging their size. It sounds very reasonable, and it is.

But Activision is almost the opposite. It feels like a publisher, who owns a number of internal studios. The studios, of course, are accountable to the publisher, but they are independent, that is key, core value. Central technology is not there to tell people how to do things, but as a publisher-side resource to help if possible. The teams are incredibly strong on their own, even in terms of R&D. And, at least from my vantage point, they get to call all their own shots. Which is again very reasonable, if you tell people they are accountable they should definitely have the freedom of choice too.

And yet again. Valuing independence versus valuing a shared community, the opposite viewpoints end up in practice creating results that are not that dissimilar. How comes?

If you’ve been doing this for a while, it won’t be even surprising. The key is that we don’t really know what’s best. Companies and teams even technologies and products organize around given values, a cultural environment that was created probably when there were almost a handful of employees. And once you have these the truth is you can most often structure everything else around in a way that makes sense, that works. 

We see this so often in code too. A handful of key decisions are made because of legacy or opinions. We’re going to be a deferred renderer. Well, now certain things are harder, others are easier. Certain stuff needs to happen early in the frame, other late, and you have certain bottlenecks and idle times, and you work from there, find smart ways to put work where the GPU is free, and shift techniques to remove bottlenecks and so on.

Which doesn't mean everything is ever perfect, mind you. We always have pain points and room for improvement, and different strategies yield different issues. To a degree, this is even lucky!  I don't make games, I help people and technology. The day Unity will solve all technical and organizational problems for game making is the day I'll be out of a job. At least in this industry...

Ok, then why?

If you love all these companies. Why leave? What are you not telling...
Well. I’m not too bright. And instead of keeping my great job, sometimes I venture into the unknown. But that’s a tale for another day...

C++, it’s not you. It’s me.

How I learned not to worry and tolerate C++

If you follow the twitter-verse (ok, and you happen to be in the same small circle of grumpy gamedevs that forms my bubble) you might have noticed lately a rise of rage and sarcasm against C++ and the direction it's taking.

I don't want to post all the relevant bits, but the crux of the issue, for the lucky among you who don't do social media, is the growing disconnect between people woking on big, complex, performance-sensitive and often monolithic and legacy-ridden codebases that we find in game development, and the ideas of "modernity” of the C++ standard community.

Our use-case is perhaps peculiar. We maintain large codebases, with large teams, but we never did great at modularization. This is our fault, and I’m not sure why it happened. Maybe it’s a combination of factors, including certain platforms and compilers not working well with dynamic libraries, or performance concerns. But most likely, it’s also the product of a creative environment where experimentation is a necessity, planning is inherently hard and architecture work is often simply neglected due to other production pressures.

The (AAA) gamedev use-case

Whatever are the historical reasons, the reality is that we live in an environment where most often than not:

• We don’t care about the STL, not its performance improvements. We developed our own bespoke containers, both because we need very ad-hoc algorithms tuned to specific problem sizes, and because of design issues of the STL itself which made it impossible to use (e.g. its historical reluctance to play well with things like memory alignment).
• We do care about all declinations of “performance”. Not only the final, all-optimizations enabled, code generation, but also performance in debug builds, and compiler performance. Iteration times.
• We care about being able to reason about code. Simplicity, not counted as lines of code, but as the ability to clearly understand what a line of code does.   We often would prefer verbose, even in the eyes of some more arcane code, to unpredictable code, where to understand the relation between what we wrote and what happens requires a lot of context, of “global” information.

Given this scenario, it should be clear to see how most of the C++ additions since C++11 have gone in the “wrong” direction. They are typically not adding any expressive power for our use-cases, but instead, bring lots of complexity to an already almost impossible-to-master language. Complexity that also “trickles” down to tooling, compilers, compile times, debuggers and so on.

It’s hard to overstate how bad this chasm is growing, with some direction being truly infuriating, but let me just bring a concrete example. Take the modernization of STL containers, say r-value references, initializer lists and all the ecosystem around that. Clearly, a huge feature for C++, allowing even significant savings for certain uses of the STL. And what you would call “a zero cost abstraction” - if you don’t use it, you don’t see it. Everyone’s happy, right?

Quite the contrary! The prime example of something that is entirely useless for people who already know about the cost of constructors, temporaries and the like, designed their code thinking of how to layout and transform bits, instead of higher level concepts sketched in a UML diagram.

Useless but dangerous, as these concepts increased the complexity of the language exponentially, to the point that very few can really claim to understand all their nuances, and yet are still hard to entirely avoid in projects made by lots of people, and where you do not necessarily even have the control of all the code due to external libraries.

And when all this humongous effort is taken on one side, features that would truly help performance sensitive large “system” programming, are still completely ignored. C++ still doesn’t have a “restrict” keyword. Strict aliasing is incredibly troublesome and even getting worse with certain proposals. Threads and memory alignment were implemented first (and arguably better) in the good old C.

We still don’t have vector types, not to mention fancier features like the ability of “transposing” the memory layout of arrays of structures (into SOAs or AOSOA and so on). We don’t have anything to help tooling, or compile times, like standardized reflection, serialization or proper modules. And we keep adding metaprogramming (templates) features before tacking complexity and usability issues (e.g. concepts, that now are scheduled to be in C++20).

What’s C++ anyway?

I think part of the disconnect, and even anger in the community, is due to a misinterpretation of what C++ is and always has been.

There is this myth that C++ is a low-level, high-performance/system programming language. It’s false, and it has always been false.

Clearly, a language that didn’t bother until recently to implement threads, is not a language for high-performance anything. There is nothing in C++ that concerns with system programming either, that is the C part of it, really. And so on and so forth, the more you look the more evident that truth is. Even the ancient Fortran can say it’s more concerned with performance than C++. C++ is not ISPC, nor Cuda. It doesn’t come with algorithms and data structure for low-latency, constrained memory use-cases, neither does care about large-scale data cases. Even python can be seen as a better language for high-performance computation, due to its ability to quickly implement embedded-DSLs. Nowadays, a lot of high-performance code leverages specialized compilers that use are embedded in relatively high-level languages via reflection. None of that is possible in C++.

C++ is a “zero cost abstraction” language. That part is true. But “zero cost” is not about performance. In fact, the guarantee that something won’t cost you anything when it’s not used doesn’t really mean that it will be fast when you do use it! What it gives, instead, is peace-of-mind. Bjarne said, do you like C? And its ecosystem? Great! Use this one, I guarantee I won’t screw with C, and if you like anything else I added, you get to use it.

And don’t get me wrong. This is genius. And a fundamental lesson that so many other languages still today don’t understand. Making a nice language is the easy part. If you’re even just a tourist of computer languages, have been exposed with concepts from veterans like Lisp and ML and so on, it’s not that hard to come up with a perfectly pleasant little language. And in fact, we have so, so many of them today. The hard part is selling such language! And if you don’t have a community with a strong need that nobody but you serve, that means you have to persuade people to hop over from whatever what they’re currently using. That is almost impossible.

Bjarne understood that the ecosystem is what matters most and C++ succeeded by being a drop-in replacement for C. Unfortunately now C++ is so complex, that creating a new language that seamlessly can work and replace it is a tall order, but it would also be an incredibly powerful tool for adoption.

Why things changed and what can we do about it?

Bjarne got marketing right, he probably understood people more than languages, which is not an insult, to the contrary! People are all that matters.

But if this is the premise, it wouldn’t even be surprising that C++ is drifting away from systems programming, from what C programmers care about. Nowadays, these use-cases are a minority. 

It is not unreasonable, from that point of view, to now try to appease to the cool kids writing web stuff. People who might be using python, or java, or go and so on. Programmers who are accustomed to working fast, gluing together frameworks and libraries more than they write any bespoke algorithm and data structure. In fact, when you think about it, adding OO to C was just what was trendiest at the time. It was a hype and marketing based decision, not really a smart one, as we now maybe see more clearly as the OO hype dissipates.

But I don’t even think it’s necessarily a conscious design decision. This language is huge today. Its community its huge, and it decided to go the way of the design by committee. Should you never do that? In my circles, the answer is definitely no, design by committee is the death of technology. But let’s present a more positive way of looking at it.

We know that democracy has a cost, a huge cost. It’s definitely not the most efficient way of governing, nor it produces the most brilliant decisions. It can be in fact, incredibly dumb. This is not news, even in the Roman Republic there were provisions for senators to elect a dictator for a temporary period when strong leadership was deemed necessary. Of course, the risk is for a dictator to become a tyrant, as Romans learned. So, democracies trade efficiency for risk aversion, variance for mean.

And that is what C++ is today. It’s an old language, even if it wants to play cool, it fundamentally is in maintenance mode, listening to a lot of people with a lot of ideas and going in the direction of the majority, not of strong design decisions. Sometimes people argue that the solution would be to have more representation of certain use cases in the committee. 

Perhaps a bit would help, but for how much I do like politics, I really don’t care about language ones. If I could vote on something, I would vote to remove people from the committee, make it a smaller one, not a noisier and bigger one, even if the noise was to argue “in my favor”. I simply don’t think you can have a lot of compromise in technology without ending up with something mediocre.

So? So we live with C++. Not because we like it, but because we need the ecosystem. The compilers, the IDEs, the language extensions, the low-level intrinsics, the legacy code. We restrict our usage to mostly C, and grab whatever rare new feature happens to integrate decently in our workflows. Maybe one day some new language will come, we already use bits and pieces of other ones when needed.

And maybe someday someone will learn the real lesson Bjarne had to teach, and really kill it! There is actually no reason for example that a language could not compile its own syntax in its files, but also allow to include C++ headers. Yes, you’d have to suffer and pay the pain of integrating a C++ compiler in whatever you come up with, and yes, your language would most probably just be something that expands to C++. Exactly how C++ started on top of C. But it’s unsexy, boring work, so most people will write yet another C-ish thing with a bit of ML in it on top of LLVM and call it a day...

Addendum: "Alex" asked an interesting question in the comments - why don't we make our own. I tried to answer that as well if you have a look below! 👇 

Computer Graphics R&D: An industry perspective

Slides from a recent talk I gave at the University of British Columbia.

Download here.

"Coder" color palettes for data visualization

Too often when programmers want to visualize data (which they should do often!), we simply resort to so called "coder-colors", encoding values directly into RGB channels (e.g. R = data1, G = data2 ...) without much consideration.

This is unfortunate, because it can both significatively distort the data, rendering it in a non perceptually linear fashion and biasing certain data columns to be more important than others (e.g. the blue channel is much less bright than the green one), and make the visualization less clear as we leverage only one color characteristic (brightness) to map the data.

The idea here is to build easy to use palette approximations for data visualization that can be coded as C/Java/Shader/etc... functions and replace "coder colors" with minimal effort.

Features we're looking for:

• Perceptual linearity 
• The palette steps should be equal in JND units
• We could prove this by projecting the palette in color space made for appearance modeling (e.g. CIELAB) and looking at the gradient there. 
• Good range 
• We want to use not just brightness, but color variations as well.
• We could even follow curved paths in a perceptually linear color space, we are not restricted to straight lines..
• The objective is to be able to clearly distinguish >10 steps.
• Intuitive for the task at hand, legible
• E.g sequential data (0...1) versus diverging or categorical data (-1...1).
• Colorblind aware
• The encoding should primarily rely on brightness variation, color variation should be used only to try to increment the range/contrast and using colorblind safe colors.

Now, before I dump some code, I have to disclaim that albeit I tried to follow the principles listed above, I don't claim I am absolutely confident in the end results... Color appearance modelling is quite hard in practice, it depends on the viewing environment and the overall image being displayed, and there are many different color spaces that can be used.

The following palettes were done mostly by using CIELAB ramps and/or looking at well-known color combinations used in data visualization. 

The code below is GLSL, but I avoided on purpose to use GLSL vectors so it's trivial to copy and paste in C/Java/whatever else...

One-dimensional data.

vec3 ColorFn1D (float x)

{

x = clamp (x, 0.0, 1.0);

float r = -0.121 + 0.893 * x + 0.276 * sin (1.94 - 5.69 * x);

float g = 0.07 + 0.947 * x;

float b = 0.107 + (1.5 - 1.22 * x) * x;

return vec3 (r, g, b);

}

This palette is similar to R's "Viridis", even if it wasn't derived from the same data. You can notice the sine in one of the channels, it's not unusual for most of these palettes to be well approximated using sine waves because the most straightforward way to derive a brighness-hue-saturation perceptual color space is to use cylindrical transforms of color spaces that are rotated so one axis represents brightness, and the other two are color components (e.g. that's how CIELAB works with the related cylindrical transforms like CIELCH and HSLUV)

Palette, example use and sRGB plot

Note how the palette avoids stretching to pure black. This is wise both because the bottom range of sRGB is not great in terms of perceptual uniformity, and because lots of output devices won't do particularly great when dealing with blacks.

One-dimensional data, diverging.

vec3 ColorFn1Ddiv (float y)

{

y = clamp (y, -1.0, 1.0);

#if 0

float r = 0.569 + (0.396 + 0.834 * y) * sin (2.15 + 0.93 * y);

float g = 0.911 + (-0.06 - 0.863 * y) * sin (0.181 + 1.3 * y);

float b = 0.939 + (-0.309 - 0.705 * y) * sin (0.125 + 2.18 * y);

#else

float r = 0.484 + (0.432 - 0.104 * y) * sin(1.29 + 2.53*y);

float g = 0.334 + (0.585 + 0.00332 * y) * sin(1.82 + 1.95*y);

float b = 0.517 + (0.406 - 0.0348 * y) * sin(1.23 + 2.49*y);

#endif

return vec3 (r, g, b);

}

Palette, example use and sRGB plot

One-dimensional data, two categories.

Essentially, one dimensional data + a flag. It choses between two palettes that are designed to be similar in brightness but always quite easy to distinguish, at any brightness level.

vec3 ColorFn1DtwoC (float x, int c)

{

x = clamp (x, 0.0, 1.0);

float r, g, b;

if (c == 0)

{

r = max (0.0, -0.724 + (2.52 - 0.865*x)*x);

g = 0.315 + 0.589*x;

b = x > 0.464 ? (0.302*x + 0.641) : (1.27*x + 0.191);

}

else

{

r = 0.539 + (1.39 - 0.965 * x) * x;

g = max (0.0, -0.5 + (2.31 - 0.878*x)*x);

b = 0.142 + 0.539*x*x*x;

}

return vec3 (r, g, b);

}

Two examples, varying the category at different spatial frequencies
and the two palettes in isolation.

These palettes can't go too dark or too bright, because otherwise it won't be easy to distinguish colors anymore.

The following is a (very experimental) version which supports up to five different categories:

vec3 ColorFn1DfiveC (float x, int c)

{

x = clamp (x, 0.0, 1.0);

float r, g, b;

switch (c)

{

case 1 :

r = 0.22 + 0.71*x; g = 0.036 + 0.95*x; b = 0.5 + 0.49*x;

break;

case 2 :

g = 0.1 + 0.8*x;

r = 0.48 + x * (1.7 + (-1.8 + 0.56 * x) * x);

b = x * (-0.21 + x);

break;

case 3 :

g = 0.33 + 0.69*x; b = 0.059 + 0.78*x;

r = x * (-0.21 + (2.6 - 1.5 * x) * x);

break;

case 4 :

g = 0.22 + 0.75*x;

r = 0.033 + x * (-0.35 + (2.7 - 1.5 * x) * x);

b = 0.45 + (0.97 - 0.46 * x) * x;

break;

default :

r = g = b = 0.025 + 0.96*x;

}

return vec3 (r, g, b);

}

Two dimensions

Making a palette to map two dimensional data to color is not easy, really depends on what we're going to use it for. 

The following code implements a variant on the straightforward mapping of the two data channels to red and green, designed to be more perceptually linear.

vec3 ColorFn2D (float x, float y)

{

x = clamp (x, 0.0, 1.0);

y = clamp (y, 0.0, 1.0);

// Optional: gamma remapping step

x = x < 0.0433 ? 1.37 * x : x * (0.194 * x + 0.773) + 0.0254;

y = y < 0.0433 ? 1.37 * y : y * (0.194 * y + 0.773) + 0.0254;

float r = x;

float g = 0.6 * y;

float b = 0.0;

return vec3 (r, g, b);

}

Two-channel mapping and example use contrasted with naive
red-green direct mapping (rightmost image)

As an example of a similar palette designed with a different goal, the following was made to highlight areas where the two data sources intersect, by shifting towards white (with the mapping done via the red and blue channels, primarily, instead of red and green).
Beware of how this one is used, because it could be easily misinterpreted for a conventional red-blue channel mapping as we're so accustomed to these kinds of direct mappings.

vec3 ColorFn2D (float x, float y)

{

x = clamp (x, 0.0, 1.0);

y = clamp (y, 0.0, 1.0);

float r = x;

float g = 0.5*(x + 0.6)*y;

float b = y;

return vec3 (r, g, b);

}

Another two-channel mapping and example use contrasted 
with naive red-blue direct mapping (rightmost image)

Lastly, a (very experimental) code snippets for two-dimensional data where one dimension is divergent:

vec3 ColorFn2Ddiv (float x, float div)

{

x = clamp (x, 0.0, 1.0);

div = clamp (div, -1.0, 1.0);

#if 0

div = div * 0.5 + 0.5;

float r1 = (0.0812 + (0.479 + 0.267) * x) * div;

float g1 = (0.216 + 0.407 * x) * div;

float b1 = (0.323 + 0.679 * x) * div;

div = 1.0 - div;

float r2 = (0.0399 + (0.391 + 0.196) * x) * div;

float g2 = (0.232 + 0.422 * x) * div;

float b2 = (0.0910 + (0.137 - 0.213) * x) * div;

    

return vec3(r1, g1, b1) + vec3(r2, g2, b2);

#else

float r = 0.651 + (-0.427 - 0.138*div) * sin(0.689 + 1.95*div);

float g = 0.713 + 0.107*div - 0.0565*div*div;

float b = 0.849 - 0.13*div - 0.233*div*div;

    

return vec3 (r, g, b) * (x * 0.7 + 0.3);

#endif

}

DataLog & TableLog

What:

• A simple system to serialize lists of numbers. 

Why: 

• Programmers should use visualization as an everyday tool when developing algorithms. 
• Most times if you just look at the final results via some aggregate statistics, for non trivial code, you end up missing important details that could lead to better solutions. 
• Visualize often and early. Visualize the dynamic behaviour of your code!
• What I used to do for the most part is to printf() from C code times values in a simple csv format, or directly as Mathematica arrays.
• Mathematica is great for visualization and often with a one-liner expression I can process and display the data I emitted. Often I even copy the Mathematica code to do so as a comment in the C source.
• Sometimes I peek directly in the process memory...
• This hack’n’slash approach is fine, but it starts to be very inconvenient when you need to dump a lot of data and/or if the data is generated by multiple threads or in different stages in the program.
• Importing the data can be very slow as well!
• Thus, I finally decided I needed a better serialization code...

Features:

• Schema-less. Serializes arrays of numbers. Supports nested arrays, no need to know the array dimensions up-front. Can represent any structure.
• Compact. Stores numbers, internally, in the smallest type that can contain them (from 8-bit integers to double-precision floating point). Decodes always as double, transparently.
• Sample import code for Processing.
• Can also serialize to CSV, Mathematica arrays and UBJSON (which Mathematica 11.x can import directly)
• Multi-thread safe.
• Automatically sorts and optionally collates together data streams coming from different threads.
• Not too slow. Usable. I would probably rewrite it from scratch now that I understand what I can do better - but the current implementation is good enough that I don't care, and the interface is ok.
• Absolutely NOT meant to be used as a "real" serialization format, everything is meant to be easy to drop in an existing codebase, zero dependencies, and get some data out quickly, to then be removed...

Bonus: "TableLog" (included in the same source)

• A system for statistical aggregation, for when you really have lots of data...
• ...or the problem is simple enough that you know what statistics to extract from the C code!
• Represents a data table (rows, columns).
• Each row should be an independent "item" or experiment.
• Each column is a quantity to be measured of the given item.
• Multiple samples (data values) can be "pushed" to given rows/columns.
• Columns automatically compute statistics over samples.
• Each column can aggregate a different number of samples.
• Each column can be configured to compute different statistics: average, minimum, maximum, histograms of different sizes.
• Multithread-safe.
• Multiple threads can write to different rows...
• ...or the same row can be "opened" globally across threads.
• Columns can be added incrementally (but will appear in all rows).

--- Grab them here! ---

DataLog: C code - computing & exporting data

DataLog: Processing code - importing & visualizing data

TableLog: C code

TableLog: Data imported in Excel

More visualization examples...