GPU path tracing tutorial 1: Drawing First Blood

// 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 }, //Rght 
 { 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");
}

// 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);}

• 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

• 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 

• smallpt by Kevin Beason
• line-for-line explanation of smallpt code by David Cline 
• "Realistic Ray Tracing" book by Peter Shirley
• Scratch-a-Pixel : excellent online rendering tutorials (ray tracing and rasterisation) 
• tokaspt: a very fast CUDA path tracer by Thierry Berger Perrin (renders only spheres)
• CUDA path tracing tutorial on RichieSams blog 
• Raytracer in Rust
• Triers CUDA ray tracing tutorial
• Real-time CUDA raytracer (triangle meshes and BVH acceleration structure) by Thanassis Tsiodras
• A simple real-time ray tracer in CUDA and OpenGL tutorial (+ code) by Igor Sevo
• CUDA path tracer by Peter Kutz and Karl Li
• UPenn CUDA path tracing course by Yining Karl Li: part 1, part 2, GitHub code project
• University of Pennsylvania GPU Programming and Architecture course: excellent beginner to advanced level course to learn CUDA, path tracing in CUDA, CUDA optimisation and pitfalls, performance profiling with NSight
• CUDA programming and optimisation tutorials
• CUDA optimisation