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// Halide tutorial lesson 12: Using the GPU
// This lesson demonstrates how to use Halide to run code on a GPU using OpenCL.
// On linux, you can compile and run it like so:
// g++ lesson_12*.cpp -g -std=c++11 -I ../include -I ../tools -L ../bin -lHalide `libpng-config --cflags --ldflags` -lpthread -ldl -o lesson_12
// LD_LIBRARY_PATH=../bin ./lesson_12
// On os x:
// g++ lesson_12*.cpp -g -std=c++11 -I ../include -I ../tools -L ../bin -lHalide `libpng-config --cflags --ldflags` -o lesson_12
// DYLD_LIBRARY_PATH=../bin ./lesson_12
// If you have the entire Halide source tree, you can also build it by
// running:
// make tutorial_lesson_12_using_the_gpu
// in a shell with the current directory at the top of the halide
// source tree.
#include "Halide.h"
#include <stdio.h>
using namespace Halide;
// Include some support code for loading pngs.
#include "halide_image_io.h"
using namespace Halide::Tools;
// Include a clock to do performance testing.
#include "clock.h"
// Define some Vars to use.
Var x, y, c, i, ii, xo, yo, xi, yi;
// We're going to want to schedule a pipeline in several ways, so we
// define the pipeline in a class so that we can recreate it several
// times with different schedules.
class MyPipeline {
Func lut, padded, padded16, sharpen, curved;
MyPipeline(Buffer<uint8_t> in) : input(in) {
// For this lesson, we'll use a two-stage pipeline that sharpens
// and then applies a look-up-table (LUT).
// First we'll define the LUT. It will be a gamma curve.
lut(i) = cast<uint8_t>(clamp(pow(i / 255.0f, 1.2f) * 255.0f, 0, 255));
// Augment the input with a boundary condition.
padded(x, y, c) = input(clamp(x, 0, input.width()-1),
clamp(y, 0, input.height()-1), c);
// Cast it to 16-bit to do the math.
padded16(x, y, c) = cast<uint16_t>(padded(x, y, c));
// Next we sharpen it with a five-tap filter.
sharpen(x, y, c) = (padded16(x, y, c) * 2-
(padded16(x - 1, y, c) +
padded16(x, y - 1, c) +
padded16(x + 1, y, c) +
padded16(x, y + 1, c)) / 4);
// Then apply the LUT.
curved(x, y, c) = lut(sharpen(x, y, c));
// Now we define methods that give our pipeline several different
// schedules.
void schedule_for_cpu() {
// Compute the look-up-table ahead of time.
// Compute color channels innermost. Promise that there will
// be three of them and unroll across them.
curved.reorder(c, x, y)
.bound(c, 0, 3)
// Look-up-tables don't vectorize well, so just parallelize
// curved in slices of 16 scanlines.
Var yo, yi;
curved.split(y, yo, yi, 16)
// Compute sharpen as needed per scanline of curved.
sharpen.compute_at(curved, yi);
// Vectorize the sharpen. It's 16-bit so we'll vectorize it 8-wide.
sharpen.vectorize(x, 8);
// Compute the padded input as needed per scanline of curved,
// reusing previous values computed within the same strip of
// 16 scanlines.
padded.store_at(curved, yo)
.compute_at(curved, yi);
// Also vectorize the padding. It's 8-bit, so we'll vectorize
// 16-wide.
padded.vectorize(x, 16);
// JIT-compile the pipeline for the CPU.
// Now a schedule that uses CUDA or OpenCL.
void schedule_for_gpu() {
// We make the decision about whether to use the GPU for each
// Func independently. If you have one Func computed on the
// CPU, and the next computed on the GPU, Halide will do the
// copy-to-gpu under the hood. For this pipeline, there's no
// reason to use the CPU for any of the stages. Halide will
// copy the input image to the GPU the first time we run the
// pipeline, and leave it there to reuse on subsequent runs.
// As before, we'll compute the LUT once at the start of the
// pipeline.
// Let's compute the look-up-table using the GPU in 16-wide
// one-dimensional thread blocks. First we split the index
// into blocks of size 16:
Var block, thread;
lut.split(i, block, thread, 16);
// Then we tell cuda that our Vars 'block' and 'thread'
// correspond to CUDA's notions of blocks and threads, or
// OpenCL's notions of thread groups and threads.
// This is a very common scheduling pattern on the GPU, so
// there's a shorthand for it:
// lut.gpu_tile(i, block, thread, 16);
// Func::gpu_tile behaves the same as Func::tile, except that
// it also specifies that the tile coordinates correspond to
// GPU blocks, and the coordinates within each tile correspond
// to GPU threads.
// Compute color channels innermost. Promise that there will
// be three of them and unroll across them.
curved.reorder(c, x, y)
.bound(c, 0, 3)
// Compute curved in 2D 8x8 tiles using the GPU.
curved.gpu_tile(x, y, xo, yo, xi, yi, 8, 8);
// This is equivalent to:
// curved.tile(x, y, xo, yo, xi, yi, 8, 8)
// .gpu_blocks(xo, yo)
// .gpu_threads(xi, yi);
// We'll leave sharpen as inlined into curved.
// Compute the padded input as needed per GPU block, storing
// the intermediate result in shared memory. In the schedule
// above xo corresponds to GPU blocks.
padded.compute_at(curved, xo);
// Use the GPU threads for the x and y coordinates of the
// padded input.
padded.gpu_threads(x, y);
// JIT-compile the pipeline for the GPU. CUDA, OpenCL, or
// Metal are not enabled by default. We have to construct a
// Target object, enable one of them, and then pass that
// target object to compile_jit. Otherwise your CPU will very
// slowly pretend it's a GPU, and use one thread per output
// pixel.
// Start with a target suitable for the machine you're running
// this on.
Target target = get_host_target();
// Then enable OpenCL or Metal, depending on which platform
// we're on. OS X doesn't update its OpenCL drivers, so they
// tend to be broken. CUDA would also be a fine choice on
// machines with NVidia GPUs.
if (target.os == Target::OSX) {
} else {
// Uncomment the next line and comment out the lines above to
// try CUDA instead.
// target.set_feature(Target::CUDA);
// If you want to see all of the OpenCL, Metal, or CUDA API
// calls done by the pipeline, you can also enable the Debug
// flag. This is helpful for figuring out which stages are
// slow, or when CPU -> GPU copies happen. It hurts
// performance though, so we'll leave it commented out.
// target.set_feature(Target::Debug);
void test_performance() {
// Test the performance of the scheduled MyPipeline.
Buffer<uint8_t> output(input.width(), input.height(), input.channels());
// Run the filter once to initialize any GPU runtime state.
// Now take the best of 3 runs for timing.
double best_time = 0.0;
for (int i = 0; i < 3; i++) {
double t1 = current_time();
// Run the filter 100 times.
for (int j = 0; j < 100; j++) {
// Force any GPU code to finish by copying the buffer back to the CPU.
double t2 = current_time();
double elapsed = (t2 - t1)/100;
if (i == 0 || elapsed < best_time) {
best_time = elapsed;
printf("%1.4f milliseconds\n", best_time);
void test_correctness(Buffer<uint8_t> reference_output) {
Buffer<uint8_t> output =
curved.realize(input.width(), input.height(), input.channels());
// Check against the reference output.
for (int c = 0; c < input.channels(); c++) {
for (int y = 0; y < input.height(); y++) {
for (int x = 0; x < input.width(); x++) {
if (output(x, y, c) != reference_output(x, y, c)) {
printf("Mismatch between output (%d) and "
"reference output (%d) at %d, %d, %d\n",
output(x, y, c),
reference_output(x, y, c),
x, y, c);
bool have_opencl_or_metal();
int main(int argc, char **argv) {
// Load an input image.
Buffer<uint8_t> input = load_image("images/rgb.png");
// Allocated an image that will store the correct output
Buffer<uint8_t> reference_output(input.width(), input.height(), input.channels());
printf("Testing performance on CPU:\n");
MyPipeline p1(input);
if (have_opencl_or_metal()) {
printf("Testing performance on GPU:\n");
MyPipeline p2(input);
} else {
printf("Not testing performance on GPU, "
"because I can't find the opencl library\n");
return 0;
// A helper function to check if OpenCL seems to exist on this machine.
#ifdef _WIN32
#include <windows.h>
#include <dlfcn.h>
bool have_opencl_or_metal() {
#ifdef _WIN32
return LoadLibrary("OpenCL.dll") != NULL;
#elif __APPLE__
return dlopen("/System/Library/Frameworks/Metal.framework/Versions/Current/Metal", RTLD_LAZY) != NULL;
return dlopen("libOpenCL.so", RTLD_LAZY) != NULL;