CODEWITHSUNDEEP
glslprocessingconditional-statements
asimes
asimes

Reputation: 5894

GLSL conditional penalties

I've written my first couple of GLSL programs for Processing (a visual language similar to Java that can load shaders) recently that make fractals. In the loop that handles the fractal code, I have an escape conditional that breaks if a point would tend to infinity.

It works fine and it is similar to how I generally write the code for non-GLSL. However someone told me that two paths are calculated every time a conditional is executed. I've had a hard time finding exactly how much of a penalty is caused by conditionals in GLSL.

Edit: To the best of my understanding in non-GLSL when an if is encountered a path is assumed. If the "correct" path was assumed everything is great. If the "wrong" path was assumed then "bad" work is discarded and instructions continue along the "correct" path. The penalty might be say 3 (or whatever number) of instructions. I want to know if there is some number (3 or whatever) of instructions that are the penalty or if both paths are calculated all the way through.

Here is the code if the explanation is not clear enough:

// Mandelbrot Set code
int i = 0;
float zr = x;
float zi = y;
for (; i < maxIterations; i++) {
    float sqZr = zr*zr;
    float sqZi = zi*zi;
    float twoZri = 2.0*zr*zi;
    zr = sqZr-sqZi+x;
    zi = twoZri+y;
    if (sqZr+sqZi > 16.0) break;
}

Upvotes: 4

Views: 1330

Answers (1)

bjorke
bjorke

Reputation: 3305

On old GPUs, both sides of an if() clause were executed and the correct result chosen at the end. On newer ones, this is only the case if the compiler thinks it would be more efficient. if() clauses are not free: the generic rule of thumb I have used for some time is: "if() costs 14 clock cycles" though the latest GPUs may be cheaper.

Why is this so? Because GPUs are stream processors, they want to have identical data-loading profiles for all pixels (especially for gradient values like texture colors or values from vertex registers). The principle of SIMD -- even when the devices are not strictly SIMD -- is usually the way to get the most performance from such devices.

When in doubt, see if you can use one of the NVIDIA perf analysis tools on your code, or just try writing the code (it's short!) a few different ways and comparing your performance for your specific GPU.

(BTW Processing is not Java-like: it's Java)

Upvotes: 4

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