gpu programming with gpuimage and metal

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GPU PROGRAMMING WITH GPUIMAGE AND METAL JANIE CLAYTON-HASZ

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These are the slides from the talk I did on GPU Programming for CocoaConf Columbus 2014

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Page 1: Gpu Programming With GPUImage and Metal

GPU PROGRAMMING WITH GPUIMAGE AND METALJANIE CLAYTON-HASZ

Page 2: Gpu Programming With GPUImage and Metal

What is a GPU?A Graphics Processing Unit (GPU) is a small super computer that does one thing really really well. That one thing is processing floating point math in parallel.

There are several applications for being able to do really fast floating point math: Graphics processing, bioinformatics, molecular dynamics, etc…

Most people are going to primarily focus on graphics processing, as we will today

Page 3: Gpu Programming With GPUImage and Metal

What is Parallel Computing

The default processes in a project is serialized computing. One instruction is processed at a time and then the CPU moves on to the next one.

Parallel computing is the process of allowing multiple instructions to be carried out at once.

Can be done on different threads, cores, and even at the bit level.

Page 4: Gpu Programming With GPUImage and Metal

But I Have Concurrency!

Concurrency is about dealing with a lot of things at once.

Parallelism is about doing lots of things at once.

Page 5: Gpu Programming With GPUImage and Metal

Shader Basics

Shaders are the programs that determine what gets displayed on your screen.

Shaders determine shapes, colors, textures, lighting…

Everything you see and use comes down to shaders.

Page 6: Gpu Programming With GPUImage and Metal

GRAPHICS ON IOS DEVICES

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There are many levels of abstraction for graphics on iOS.

!

Some frameworks are more abstracted than others.

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UIKit

Sprite Kit

Core Animation/Graphics

OpenGL ES/GLKit

Page 9: Gpu Programming With GPUImage and Metal

A BRIEF HISTORY OF TIME, UH, OPENGL…

Page 10: Gpu Programming With GPUImage and Metal

OpenGL Origins

First released in 1992

Was an attempt to formalize a 3D graphic specification across platforms

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Problems with OpenGLWas created back when GPUs were not very powerful and existed on external graphics cards that could be swapped out

The computer system architecture was vastly different when OpenGL was created. Things that were not very efficient then, like the GPU, are vastly more efficient now.

Nothing is ever deprecated (Don’t ask Java programmers what that means, they don’t know)

Page 12: Gpu Programming With GPUImage and Metal
Page 13: Gpu Programming With GPUImage and Metal

Creation of OpenGL ES

ES: Embedded Systems

Wanted to strip out all of the dead code from OpenGL

Was specifically tailored to work on less powerful devices like mobile phones

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We don’t need a dozen turtles that all do the same thing

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OpenGL ES Specifics

Streamlined version of OpenGL

Everything you can do in OpenGL ES can directly be ported to OpenGL

Basically an optimized version of OpenGL

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CPU VS GPU PROGRAMMING

Page 17: Gpu Programming With GPUImage and Metal

CPU Expensive Tasks

Sending hardware commands to the GPU (Changing State Vectors)

Confirming that API usage is valid

Compiling the shaders

Interaction between the state and the shaders

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What the Heck is “State”??

Try to envision a client-server process. Instead of your program sending an instruction over the network to a server and getting data back, you are sending instructions from your CPU to your GPU to be executed. Since you are sending instructions away from your client to be done elsewhere, you want to minimize this as much as possible.

Page 19: Gpu Programming With GPUImage and Metal

What the Heck is “State”??

For example, in most Twitter client applications the client batches 20 or more Tweets in one call. This allows the application to feed tweets to the user without them having to wait for the network to deliver each and every tweet individually.

Page 20: Gpu Programming With GPUImage and Metal
Page 21: Gpu Programming With GPUImage and Metal

Fixed Function Pipeline

Present in OpenGL ES 1.1

Shaders were hard-coded into OpenGL

Easier to use, but were very limited

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Programmable Pipeline

Introduced in OpenGL ES 2.0

Shaders are now the responsibility of the programmer

Harder to do, but provides far more flexibility and options for effects

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GLSL SHADER BUILDING

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GLSL

OpenGL Shading Language (GLSL)

Introduced in OpenGL 2.0 in 2004

C-like language for building shaders, which are small, efficient programs to run on the GPU

Includes some specific data types and methods for processing geometry and graphics math that are not included in C

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Two Flavors of GLSL Shaders

VertexFragment

Page 26: Gpu Programming With GPUImage and Metal

Vertex Shaders

Page 27: Gpu Programming With GPUImage and Metal

Vertex Shaders

The Vertex Shader would record the vertices of the star (which would be broken down into a series of triangles)

The Vertex Shader would also specify that the area between the vertices is yellow. If there was a texture instead of a color, the shader would keep track of the texture coordinates.

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Fragment Shaders

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Fragment ShadersFragment Shaders determine what pixels receive which color.

If you look carefully at the star, there are areas outside the star that are yellow and areas inside that are white.

If there is a gradient, the Fragment Shader will calculate what specific color each individual pixel will be.

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Inputs and Outputs

Uniforms

Attributes

Varyings

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Uniforms

Values that don’t change during rendering

Read-Only

Available in both Vertex and Fragment Shaders

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Attributes

Vertex Shader only

Input values that change with every vertex, like their position, color, and texture coordinates

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Varyings

Used to pass data between the Vertex and the Fragment Shaders

Read-only in the Fragment Shader

Read-Write in the Vertex Shader

Varyings are the variables that determine the pixel color for the Fragment Shader

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GPU IMAGE

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Creating GPUImage

GPUImage dates back to iOS 5.

Unlike Core Image (at the time), GPUImage utilized shaders more efficiently to make image processing faster. Core Image has been improved over the years and they are now comparable.

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Why is GPUImage so Efficient?

OpenGL ES tasks must be performed on one thread

Many people utilize locks to manage the thread or, God forbid, only use the main thread. <shudder>

NSLock is expensive to the CPU

GPUImage utilizes a serial dispatch queue through GCD to manage anything that touches the GPU to keep everything happy and thread safe.

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Demo

Page 38: Gpu Programming With GPUImage and Metal

METAL: THE NEW KID IN TOWN

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What does Metal Promise?Deep hardware integration between Apple chips and Apple frameworks

General Purpose GPU programming (GPGPU)

Precompiled Shaders

up to 10 times more draw calls per frame

Being able to perform draw calls on multiple threads

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What Specifically are the CPU Expensive Tasks?

Compiling Shaders

Validating State

Start Work on the GPU

Page 41: Gpu Programming With GPUImage and Metal

Life Before Metal

All three of these expensive tasks were done on each and every single draw call.

All of these tasks don’t have to be done thousands of times a frame. Many can be done once, as long as the program knows that it does not have to continually check them.

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Life After Metal

Compiling Shaders: Now done when the applications builds

Validating State: Now done when the content loads

Start Work on the GPU: Still happens on each draw call. We can’t win them all…

Page 43: Gpu Programming With GPUImage and Metal

Why is This Important?

Before Metal, you would have to balance CPU time with GPU time. Tasks were so expensive that the GPU would usually not be used to capacity.

Now that the CPU tasks are less expensive, you can take that time to generate more AI and do more programming logic.

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Where Does Metal Help You?

Metal helps you when you have a lot of objects that need to work independently of one another.

Certain tasks, like image processing, do not involve a lot of objects, so you aren’t going to gain much with Metal.

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ZEN GARDEN DEMOEPIC GAMES

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Secret Sauce

Okay, so one of the big, obscure black boxes in this scheme is the promise of deep software/hardware integration. One thing I have not had the chance to study in depth is kernel programming and chip architecture. Knowing the idiosyncrasies of the chips and only having to support one type allows for more targeted processes.

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IS THERE ANY POINT IN LEARNING OPENGL ES ANYMORE?

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Yes, absolutely. Metal’s API is very similar to OpenGL ES. !

It will take a while for everyone to transition over to devices with A7 chips.

!

Apple will continue to support its developers who work with OpenGL ES, especially since the

Mac uses OpenGL and won’t be able to use Metal (yet). !

It isn’t like they would just throw out an older technology that works perfectly well and replace it with something

that barely works, right??

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Page 50: Gpu Programming With GPUImage and Metal

Well crap.

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Think Different.

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The End