In early 2011 I developed a
simple real-time path traced Pong game together with Kerrash on top of an open source GPU path tracer called
tokaspt (developed by Thierry Berger-Perrin) which could only render spheres, but was bloody fast at it. The physics were bodged, but the game proved that path tracing of very simple scenes at 30 fps was feasible, although a bit noisy. You can still download it from
https://code.google.com/p/tokap-the-once-known-as-pong/. Since that time I've always wanted to write a short and simple tutorial about GPU path tracing to show how to make your GPU draw an image with high quality ray traced colour bleeding with a minimum of code and now is a good time to do exactly that.
This tutorial is not meant as an introduction to ray tracing or path tracing as there are plenty of excellent ray tracing tutorials for beginners online such as
Scratch-a-Pixel (also check out the
old version which contains more articles) and
Minilight (more links at the bottom of this article). The goal of this tutorial is simply to show how incredibly easy it is to turn a simple CPU path tracer into a CUDA accelerated version. Being a fan of the KISS principle from design and engineering (Keep It Simple Stupid) and aiming to avoid unnecessary complexity, I've chosen to cudafy Kevin Beason's
smallpt, the most basic but still fully functional CPU path tracer around. It's a very short piece of code that doesn't require the user to install any tedious libraries to compile the code (apart from Nvidia's CUDA Toolkit).
To keep things simple and free of needless clutter, I've stripped out the code for the tent filter, supersampling, Russian Roulette and the material BRDFs for reflective and refractive materials, leaving only the diffuse BRDF. The 3D vector class from smallpt is replaced by CUDA's own built-in float3 type (built-in CUDA types are more efficient due to automatic memory alignment) which has the same linear algebra math functions as a vector such as addition, subtraction, multiplication, normalize, length, dot product and cross product. For reasons of code clarity, there is no error checking when initialising CUDA. To compile the code, save the code in a file with ".cu" file extension and follow these
CUDA installation guides to install
Nvidia's GPU Computing Toolkit and configure the programming tools to work with CUDA.
After reading the
slides from David Cline, the commented code below should speak for itself, but feel free to drop me a comment below if some things are still not clear.
So without further ado, here's the full CUDA code (also on Github at
https://github.com/straaljager/GPU-path-tracing-tutorial-1):
// smallptCUDA by Sam Lapere, 2015
// based on smallpt, a path tracer by Kevin Beason, 2008
#include <iostream>
#include <cuda_runtime.h>
#include <vector_types.h>
#include "device_launch_parameters.h"
#include <cutil_math.h> // from http://www.icmc.usp.br/~castelo/CUDA/common/inc/cutil_math.h
#define M_PI 3.14159265359f // pi
#define width 512 // screenwidth
#define height 384 // screenheight
#define samps 1024 // samples
// __device__ : executed on the device (GPU) and callable only from the device
struct Ray {
float3 orig; // ray origin
float3 dir; // ray direction
__device__ Ray(float3 o_, float3 d_) : orig(o_), dir(d_) {}
};
enum Refl_t { DIFF, SPEC, REFR }; // material types, used in radiance(), only DIFF used here
struct Sphere {
float rad; // radius
float3 pos, emi, col; // position, emission, colour
Refl_t refl; // reflection type (e.g. diffuse)
__device__ float intersect_sphere(const Ray &r) const {
// ray/sphere intersection
// returns distance t to intersection point, 0 if no hit
// ray equation: p(x,y,z) = ray.orig + t*ray.dir
// general sphere equation: x^2 + y^2 + z^2 = rad^2
// classic quadratic equation of form ax^2 + bx + c = 0
// solution x = (-b +- sqrt(b*b - 4ac)) / 2a
// solve t^2*ray.dir*ray.dir + 2*t*(orig-p)*ray.dir + (orig-p)*(orig-p) - rad*rad = 0
// more details in "Realistic Ray Tracing" book by P. Shirley or Scratchapixel.com
float3 op = pos - r.orig; // distance from ray.orig to center sphere
float t, epsilon = 0.0001f; // epsilon required to prevent floating point precision artefacts
float b = dot(op, r.dir); // b in quadratic equation
float disc = b*b - dot(op, op) + rad*rad; // discriminant quadratic equation
if (disc<0) return 0; // if disc < 0, no real solution (we're not interested in complex roots)
else disc = sqrtf(disc); // if disc >= 0, check for solutions using negative and positive discriminant
return (t = b - disc)>epsilon ? t : ((t = b + disc)>epsilon ? t : 0); // pick closest point in front of ray origin
}
};
// SCENE
// 9 spheres forming a Cornell box
// small enough to be in constant GPU memory
// { float radius, { float3 position }, { float3 emission }, { float3 colour }, refl_type }
__constant__ Sphere spheres[] = {
{ 1e5f, { 1e5f + 1.0f, 40.8f, 81.6f }, { 0.0f, 0.0f, 0.0f }, { 0.75f, 0.25f, 0.25f }, DIFF }, //Left
{ 1e5f, { -1e5f + 99.0f, 40.8f, 81.6f }, { 0.0f, 0.0f, 0.0f }, { .25f, .25f, .75f }, DIFF }, //Right
{ 1e5f, { 50.0f, 40.8f, 1e5f }, { 0.0f, 0.0f, 0.0f }, { .75f, .75f, .75f }, DIFF }, //Back
{ 1e5f, { 50.0f, 40.8f, -1e5f + 600.0f }, { 0.0f, 0.0f, 0.0f }, { 1.00f, 1.00f, 1.00f }, DIFF }, //Frnt
{ 1e5f, { 50.0f, 1e5f, 81.6f }, { 0.0f, 0.0f, 0.0f }, { .75f, .75f, .75f }, DIFF }, //Botm
{ 1e5f, { 50.0f, -1e5f + 81.6f, 81.6f }, { 0.0f, 0.0f, 0.0f }, { .75f, .75f, .75f }, DIFF }, //Top
{ 16.5f, { 27.0f, 16.5f, 47.0f }, { 0.0f, 0.0f, 0.0f }, { 1.0f, 1.0f, 1.0f }, DIFF }, // small sphere 1
{ 16.5f, { 73.0f, 16.5f, 78.0f }, { 0.0f, 0.0f, 0.0f }, { 1.0f, 1.0f, 1.0f }, DIFF }, // small sphere 2
{ 600.0f, { 50.0f, 681.6f - .77f, 81.6f }, { 2.0f, 1.8f, 1.6f }, { 0.0f, 0.0f, 0.0f }, DIFF } // Light
};
__device__ inline bool intersect_scene(const Ray &r, float &t, int &id){
float n = sizeof(spheres) / sizeof(Sphere), d, inf = t = 1e20; // t is distance to closest intersection, initialise t to a huge number outside scene
for (int i = int(n); i--;) // test all scene objects for intersection
if ((d = spheres[i].intersect_sphere(r)) && d<t){ // if newly computed intersection distance d is smaller than current closest intersection distance
t = d; // keep track of distance along ray to closest intersection point
id = i; // and closest intersected object
}
return t<inf; // returns true if an intersection with the scene occurred, false when no hit
}
// random number generator from https://github.com/gz/rust-raytracer
__device__ static float getrandom(unsigned int *seed0, unsigned int *seed1) {
*seed0 = 36969 * ((*seed0) & 65535) + ((*seed0) >> 16); // hash the seeds using bitwise AND and bitshifts
*seed1 = 18000 * ((*seed1) & 65535) + ((*seed1) >> 16);
unsigned int ires = ((*seed0) << 16) + (*seed1);
// Convert to float
union {
float f;
unsigned int ui;
} res;
res.ui = (ires & 0x007fffff) | 0x40000000; // bitwise AND, bitwise OR
return (res.f - 2.f) / 2.f;
}
// radiance function, the meat of path tracing
// solves the rendering equation:
// outgoing radiance (at a point) = emitted radiance + reflected radiance
// reflected radiance is sum (integral) of incoming radiance from all directions in hemisphere above point,
// multiplied by reflectance function of material (BRDF) and cosine incident angle
__device__ float3 radiance(Ray &r, unsigned int *s1, unsigned int *s2){ // returns ray color
float3 accucolor = make_float3(0.0f, 0.0f, 0.0f); // accumulates ray colour with each iteration through bounce loop
float3 mask = make_float3(1.0f, 1.0f, 1.0f);
// ray bounce loop (no Russian Roulette used)
for (int bounces = 0; bounces < 4; bounces++){ // iteration up to 4 bounces (replaces recursion in CPU code)
float t; // distance to closest intersection
int id = 0; // index of closest intersected sphere
// test ray for intersection with scene
if (!intersect_scene(r, t, id))
return make_float3(0.0f, 0.0f, 0.0f); // if miss, return black
// else, we've got a hit!
// compute hitpoint and normal
const Sphere &obj = spheres[id]; // hitobject
float3 x = r.orig + r.dir*t; // hitpoint
float3 n = normalize(x - obj.pos); // normal
float3 nl = dot(n, r.dir) < 0 ? n : n * -1; // front facing normal
// add emission of current sphere to accumulated colour
// (first term in rendering equation sum)
accucolor += mask * obj.emi;
// all spheres in the scene are diffuse
// diffuse material reflects light uniformly in all directions
// generate new diffuse ray:
// origin = hitpoint of previous ray in path
// random direction in hemisphere above hitpoint (see "Realistic Ray Tracing", P. Shirley)
// create 2 random numbers
float r1 = 2 * M_PI * getrandom(s1, s2); // pick random number on unit circle (radius = 1, circumference = 2*Pi) for azimuth
float r2 = getrandom(s1, s2); // pick random number for elevation
float r2s = sqrtf(r2);
// compute local orthonormal basis uvw at hitpoint to use for calculation random ray direction
// first vector = normal at hitpoint, second vector is orthogonal to first, third vector is orthogonal to first two vectors
float3 w = nl;
float3 u = normalize(cross((fabs(w.x) > .1 ? make_float3(0, 1, 0) : make_float3(1, 0, 0)), w));
float3 v = cross(w,u);
// compute random ray direction on hemisphere using polar coordinates
// cosine weighted importance sampling (favours ray directions closer to normal direction)
float3 d = normalize(u*cos(r1)*r2s + v*sin(r1)*r2s + w*sqrtf(1 - r2));
// new ray origin is intersection point of previous ray with scene
r.orig = x + nl*0.05f; // offset ray origin slightly to prevent self intersection
r.dir = d;
mask *= obj.col; // multiply with colour of object
mask *= dot(d,nl); // weigh light contribution using cosine of angle between incident light and normal
mask *= 2; // fudge factor
}
return accucolor;
}
// __global__ : executed on the device (GPU) and callable only from host (CPU)
// this kernel runs in parallel on all the CUDA threads
__global__ void render_kernel(float3 *output){
// assign a CUDA thread to every pixel (x,y)
// blockIdx, blockDim and threadIdx are CUDA specific keywords
// replaces nested outer loops in CPU code looping over image rows and image columns
unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;
unsigned int i = (height - y - 1)*width + x; // index of current pixel (calculated using thread index)
unsigned int s1 = x; // seeds for random number generator
unsigned int s2 = y;
// generate ray directed at lower left corner of the screen
// compute directions for all other rays by adding cx and cy increments in x and y direction
Ray cam(make_float3(50, 52, 295.6), normalize(make_float3(0, -0.042612, -1))); // first hardcoded camera ray(origin, direction)
float3 cx = make_float3(width * .5135 / height, 0.0f, 0.0f); // ray direction offset in x direction
float3 cy = normalize(cross(cx, cam.dir)) * .5135; // ray direction offset in y direction (.5135 is field of view angle)
float3 r; // r is final pixel color
r = make_float3(0.0f); // reset r to zero for every pixel
for (int s = 0; s < samps; s++){ // samples per pixel
// compute primary ray direction
float3 d = cam.dir + cx*((.25 + x) / width - .5) + cy*((.25 + y) / height - .5);
// create primary ray, add incoming radiance to pixelcolor
r = r + radiance(Ray(cam.orig + d * 40, normalize(d)), &s1, &s2)*(1. / samps);
} // Camera rays are pushed ^^^^^ forward to start in interior
// write rgb value of pixel to image buffer on the GPU, clamp value to [0.0f, 1.0f] range
output[i] = make_float3(clamp(r.x, 0.0f, 1.0f), clamp(r.y, 0.0f, 1.0f), clamp(r.z, 0.0f, 1.0f));
}
inline float clamp(float x){ return x < 0.0f ? 0.0f : x > 1.0f ? 1.0f : x; }
inline int toInt(float x){ return int(pow(clamp(x), 1 / 2.2) * 255 + .5); } // convert RGB float in range [0,1] to int in range [0, 255] and perform gamma correction
int main(){
float3* output_h = new float3[width*height]; // pointer to memory for image on the host (system RAM)
float3* output_d; // pointer to memory for image on the device (GPU VRAM)
// allocate memory on the CUDA device (GPU VRAM)
cudaMalloc(&output_d, width * height * sizeof(float3));
// dim3 is CUDA specific type, block and grid are required to schedule CUDA threads over streaming multiprocessors
dim3 block(8, 8, 1);
dim3 grid(width / block.x, height / block.y, 1);
printf("CUDA initialised.\nStart rendering...\n");
// schedule threads on device and launch CUDA kernel from host
render_kernel <<< grid, block >>>(output_d);
// copy results of computation from device back to host
cudaMemcpy(output_h, output_d, width * height *sizeof(float3), cudaMemcpyDeviceToHost);
// free CUDA memory
cudaFree(output_d);
printf("Done!\n");
// Write image to PPM file, a very simple image file format
FILE *f = fopen("smallptcuda.ppm", "w");
fprintf(f, "P3\n%d %d\n%d\n", width, height, 255);
for (int i = 0; i < width*height; i++) // loop over pixels, write RGB values
fprintf(f, "%d %d %d ", toInt(output_h[i].x),
toInt(output_h[i].y),
toInt(output_h[i].z));
printf("Saved image to 'smallptcuda.ppm'\n");
delete[] output_h;
system("PAUSE");
}
Optionally, the following 3D vector algebra functions can be inserted at the top of the file instead of #including "cutil_math.h". Instead of creating a Vector3D class (with 3 floats), CUDA's built-in float3 type is used instead as built-in types have automated memory alignment and provide higher for performance. The "__host__ __device__" keywords in front of the functions allow them to run on both the CPU and GPU.
// 3D vector algebra from cutil_math.h
/*DEVICE_BUILTIN*/
struct float3 {float x, y, z;};
typedef struct float3 float3;
// add
inline __host__ __device__ float3 operator+(float3 a, float3 b){return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);}
inline __host__ __device__ void operator+=(float3 &a, float3 b){a.x += b.x; a.y += b.y; a.z += b.z;}
inline __host__ __device__ float3 operator+(float3 a, float b){return make_float3(a.x + b, a.y + b, a.z + b);}
inline __host__ __device__ float3 operator+(float b, float3 a){return make_float3(b + a.x, b + a.y, b + a.z);}
inline __host__ __device__ void operator+=(float3 &a, float b){a.x += b; a.y += b; a.z += b;}
// subtract
inline __host__ __device__ float3 operator-(float3 a, float3 b){return make_float3(a.x - b.x, a.y - b.y, a.z - b.z);}
inline __host__ __device__ void operator-=(float3 &a, float3 b){a.x -= b.x; a.y -= b.y; a.z -= b.z;}
inline __host__ __device__ float3 operator-(float3 a, float b){return make_float3(a.x - b, a.y - b, a.z - b);}
inline __host__ __device__ float3 operator-(float b, float3 a){return make_float3(b - a.x, b - a.y, b - a.z);}
inline __host__ __device__ void operator-=(float3 &a, float b){a.x -= b; a.y -= b; a.z -= b;}
// multiply
inline __host__ __device__ float3 operator*(float3 a, float3 b){return make_float3(a.x * b.x, a.y * b.y, a.z * b.z);}
inline __host__ __device__ void operator*=(float3 &a, float3 b){a.x *= b.x; a.y *= b.y; a.z *= b.z;}
inline __host__ __device__ float3 operator*(float3 a, float b){return make_float3(a.x * b, a.y * b, a.z * b);}
inline __host__ __device__ float3 operator*(float b, float3 a){return make_float3(b * a.x, b * a.y, b * a.z);}
inline __host__ __device__ void operator*=(float3 &a, float b){a.x *= b; a.y *= b; a.z *= b;}
// divide
inline __host__ __device__ float3 operator/(float3 a, float3 b){return make_float3(a.x / b.x, a.y / b.y, a.z / b.z);}
inline __host__ __device__ void operator/=(float3 &a, float3 b){a.x /= b.x; a.y /= b.y; a.z /= b.z;}
inline __host__ __device__ float3 operator/(float3 a, float b){return make_float3(a.x / b, a.y / b, a.z / b);}
inline __host__ __device__ void operator/=(float3 &a, float b){a.x /= b; a.y /= b; a.z /= b;}
inline __host__ __device__ float3 operator/(float b, float3 a){return make_float3(b / a.x, b / a.y, b / a.z);}
// min
inline __host__ __device__ float3 fminf(float3 a, float3 b){return make_float3(fminf(a.x, b.x), fminf(a.y, b.y), fminf(a.z, b.z));}
// max
inline __host__ __device__ float3 fmaxf(float3 a, float3 b){return make_float3(fmaxf(a.x, b.x), fmaxf(a.y, b.y), fmaxf(a.z, b.z));}
// lerp
inline __device__ __host__ float3 lerp(float3 a, float3 b, float t){return a + t*(b - a);}
// clamp value v between a and b
inline __device__ __host__ float clamp(float f, float a, float b){return fmaxf(a, fminf(f, b));}
inline __device__ __host__ float3 clamp(float3 v, float a, float b){return make_float3(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b));}
inline __device__ __host__ float3 clamp(float3 v, float3 a, float3 b){return make_float3(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z));}
// dot product
inline __host__ __device__ float dot(float3 a, float3 b){return a.x * b.x + a.y * b.y + a.z * b.z;}
// length
inline __host__ __device__ float length(float3 v){return sqrtf(dot(v, v));}
// normalize
inline __host__ __device__ float3 normalize(float3 v){float invLen = rsqrtf(dot(v, v));return v * invLen;}
// floor
inline __host__ __device__ float3 floorf(float3 v){return make_float3(floorf(v.x), floorf(v.y), floorf(v.z));}
// frac
inline __host__ __device__ float fracf(float v){return v - floorf(v);}
inline __host__ __device__ float3 fracf(float3 v){return make_float3(fracf(v.x), fracf(v.y), fracf(v.z));}
// fmod
inline __host__ __device__ float3 fmodf(float3 a, float3 b){return make_float3(fmodf(a.x, b.x), fmodf(a.y, b.y), fmodf(a.z, b.z));}
// absolute value
inline __host__ __device__ float3 fabs(float3 v){return make_float3(fabs(v.x), fabs(v.y), fabs(v.z));}
// reflect
//returns reflection of incident ray I around surface normal N
// N should be normalized, reflected vector's length is equal to length of I
inline __host__ __device__ float3 reflect(float3 i, float3 n){return i - 2.0f * n * dot(n, i);}
// cross product
inline __host__ __device__ float3 cross(float3 a, float3 b){return make_float3(a.y*b.z - a.z*b.y, a.z*b.x - a.x*b.z, a.x*b.y - a.y*b.x);}
Full code on Github:
https://github.com/straaljager/GPU-path-tracing-with-CUDA-tutorial-1
In this example, it's pretty easy to turn C/C++ code into CUDA code (CUDA is a subset of the C language). The differences with the CPU version of smallpt are as follows:
- smallpt's 3D Vector struct is replaced by CUDA's built-in float3 type (linear algebra vector functions for float3 are defined in cutil_math.h)
- CUDA specific keyword __device__ before functions that should run on the GPU and are only callable from the GPU
- CUDA specific keyword __global__ in front of the kernel that is called from the host (CPU) and which runs in parallel on all CUDA threads
- a custom random number generator that runs on the GPU
- as GPUs don't handle recursion well, the radiance function needs to be converted from a recursive function to an iterative function (see Richie Sam's blogpost or Karl Li's slides for more details) with a fixed number of bounces (Russian roulette could be implemented here to terminate paths with a certain probability, but I took it out for simplicity)
- in a CPU raytracer, you loop over each pixel of the image with two nested loops (one for image rows and one for image columns). On the GPU the loops are replaced by a kernel which runs for each pixel in parallel. A global thread index is computed instead from the grid dimensions, block dimensions and local thread index. See http://www.3dgep.com/introduction-to-cuda-using-visual-studio-2008/ for more details
- the main() function calls CUDA specific functions to allocate memory on the CUDA device (cudaMalloc()), launch the CUDA kernel using the "<<< grid, block >>>" syntax and copy the results (in this case the rendered image) from the GPU back to the CPU, where the image is saved in PPM format (a supersimple image format)
When running the code above, we get the following image (1024 samples per pixel, brute force path tracing):
Path traced color bleeding rendered entirely on the GPU! On my laptop's GPU (Geforce 840M) it renders about 24x faster than the multithreaded CPU version (laptop Core-i7 clocked at 2.00 Ghz). The neat thing here is that it only took about 100 lines (if you take out the comments) to get path tracing working on the GPU. The beauty lies in its simplicity.
Even though the path tracing code already works well, it is actually very unoptimized and there are many techniques to speed it up:
- explicit light sampling (or next event estimation): sample the light source directly instead of using brute force path tracing. This makes an enormous difference in reducing noise.
- jittered sampling (also called stratified sampling): instead of sampling a pixel randomly, divide the pixel up into a number of layers (strata) in which random sampling is performed. According to Peter Shirley's book this way of sampling (which is partly structured and partly random) is one of the most important noise reduction methods
- better random number generators
- various importance sampling strategies: this code already performs cosine weighted importance sampling for diffuse rays, favouring rays with directions that are closer to the normal (as they contribute more to the final image). See http://www.rorydriscoll.com/2009/01/07/better-sampling/.
- ray tracing acceleration structures: kd-trees, octrees, grids, bounding volume hierarchies provide massive speedups
- using shared memory and registers whenever possible is many times faster than using global/local memory
- memory alignment for coalesced reads from GPU memory
- thread compaction: since CUDA launches a kernel in groups of 32 threads in parallel ("warps"), threads taking different code paths can give rise to thread divergence which reduces the GPU's occupancy. Thread compaction aims to mitigate the effects of thread divergence by bundling threads following similar code paths
I plan to cover the following topics (with CUDA implementations) in upcoming tutorials whenever I find some time:
- an interactive viewport camera with progressive rendering,
- textures (and bump mapping),
- environment lighting,
- acceleration structures,
- triangles and triangle meshes
- building more advanced features on top of Aila and Laine's GPU ray tracing framework which is also used by Blender's Cycles GPU renderer
- dissecting some code snippets from Cycles GPU render or SmallLuxGPU
