AMD 2012: HSA in Gaming

GPGPU ALGORITHMS IN GAMES How Heterogeneous Systems Architecture can be leveraged to optimize algorithms in video games Matthijs De Smedt Nixxes Software B.V. Lead Graphics Programmer

| HSA Algorithms in Games | June 13th, 2012

CONTENTS

A short introduction Current usage of GPGPU in games Heterogeneous Systems Architecture Examples made possible by HSA

INTRODUCTION


VIDEOGAMES

Games are near real-time simulations Response time is key Most systems run in sync with the output frequency

– Rendering 60 frames per second – Allows for 16ms of processing time

Framerate is limited either by: – GPU – CPU – Display (VSync)

CPU

GPU

Input

Simulate

Render

Render


HARDWARE

Typical hardware target for PC games: – One multicore CPU – One GPU

Multiple GPUs: CrossFire – Transparent to the application – Driver alternates frames between GPUs

GPUs are becoming more general purpose: – General Purpose GPU algorithms (GPGPU)

CrossFire

GPGPU IN GAMES


INTRODUCTION TO GPGPU

Rendering is a sequence of parallel algorithms GPUs are great at parallel computation Evolution of hardware and software to general purpose First GPGPU was accomplished with programmable rendering

– DirectX – OpenGL

Second generation using dedicated GPGPU APIs: – CUDA – OpenCL – DirectCompute

Third generation of GPGPU on the way: – Heterogeneous Systems Architecture


GPGPU IN GAMES

Some GPGPU algorithms are being used in games right now. For example:

– Physics Particles

Fluid simulation

Destruction

– Specialized graphics algorithms Post-processing

All these algorithms drive visual effects

GPU particle system by Fairlight


CURRENT PHYSICS EXAMPLE

GPGPU particle simulation using DirectCompute Great for simulating thousands of visible particles Results of simulation are never copied back to CPU

– Can not interfere with gameplay – Not synced in networked games

Example: Smoke particles that affect game AI

CPU

GPU

Call GPU

Simulate particles

Render particles


GPGPU LIMITATIONS

Why isn’t GPGPU used more for non-graphics? Latency

– DirectX has many layers and buffers – DirectX commands are buffered up to multiple frames – Actual execution on the GPU is delayed

Copy overhead – GPU cannot directly access application memory – Must copy all data from and to the application

Functionality – Constrained programming models

HETEROGENEOUS SYSTEMS ARCHITECTURE


HETEROGENEOUS SYSTEMS ARCHITECTURE

Hardware Software

"Drivers" – HSA provides a new, thin Compute API – Very low latency – Unified Address Space – Exposes more hardware capabilities

HSA Intermediate Language – Virtual ISA – Introduces CPU programming features to the GPU

New features on discrete GPUs Accelerated Processing Unit

– Next generation processor – Multiple CPU and GPU cores on

the same die – Shared memory access – Soon to be as widespread as

multicore CPUs

New hardware and software


USING THE APU

Distinction between two hardware configurations APU without discrete GPU

– Found in many laptops, soon in many desktops – Use the on-die GPU for rendering

APU with discrete GPU: – Hard-core gamers will still use discrete GPUs – Asymmetrical CrossFire – Or: Dedicate the on-die GPU to Compute algorithms Could result in massive speedup of algorithms

Using SIMD co-processors to offload the CPU is familiar to PS3 developers


COPY OVERHEAD

Current Compute APIs require the application to explicitly copy all input and output memory – Copying can easily takes longer than processing on CPU! – Only small datasets or very expensive computations benefit from GPGPU

HSA introduces a Unified Address Space for CPU and GPU memory – CPU pointers on the GPU – Virtual memory on the GPU Paging over PCI-Express (discrete) or shared memory controller (APU)

– Fully coherent – Will make GPGPU an option for many more algorithms


LATENCY

DirectX commands are buffered When the GPU is fully loaded this buffer is saturated Delay between scheduling and executing a GPGPU program on a busy GPU can take multiple frames

– Results will be several frames behind – Game simulation needs all objects to be in sync

GPGPU is currently impractical to use for anything but visual effects


LATENCY

HSA’s new Compute API will reduce latency How to deal with a saturated GPU? A second GPU

– Dedicate the APU to Compute – Virtually no latency

HSA feature: Graphics pre-emption – Context switching on the GPU Interrupt a graphics task (typically a large command list)

Execute Compute algorithm

Switch back to graphics

– Can be used both on discrete GPUs or on the APU Choose the solution best suited to your needs


APU USAGE EXAMPLE

GPU CPU

HSA

Frame

Schedule

DirectCompute

Execute

Execute


PROGRAMMING MODEL

HSA Intermediate Language: HSAIL Designed for parallel algorithms JIT compiles your algorithm to CPU or GPU hardware

– Also makes multi-core SIMD programming easy! High level language features

– Object-oriented programming – Virtual functions – Exceptions

Debugging SysCall support

– I/O

EXAMPLE ALGORITHMS


PHYSICS

Current GPGPU physics solutions only output to the renderer With HSA you can simulate physics on the GPU

and get the results back in the same frame Use hardware acceleration to compute physics for

gameplay objects Reduced CPU load More objects, higher fidelity


FRUSTUM CULLING

Videogames tend to be GPU-bound Avoid rendering what cannot be seen Cull objects outside the camera viewport

– Test the bounding box of every object against the camera frustum

– Currently done on the CPU – Lots of vector math – Can be computed completely in parallel!

CPU needs the results immediately – HSA will allow low-latency execution


OCCLUSION CULLING

Objects may be hidden behind others: Occlusion Final per-pixel occlusion is only known after

rendering the scene Approximate occlusion by rendering low-detail

geometry – This kind of occlusion culling is currently being

done on CPU or on SPUs – Rendering is better suited to GPUs

HSA solution: – Software rasterization in Compute on the GPU – HSA does not yet expose graphics pipeline – Still much faster than a multicore CPU

Software occlusion culling in Battlefield 3


SORTING

Typically several long lists per frame need sorting Sorting on the GPU using a parallel sort algorithm

– Ken Batcher: Bitonic or Odd-even mergesort Copy overhead currently negates the performance

advantage of using a GPU sorting algorithm HSA solution:

– Unified Address Space – GPU can sort in-place in system memory


ASSET DECOMPRESSION

Game assets are stored compressed on disk Decompression is expensive The usage of some compression algorithms is

prevented by CPU speed Games are moving away from loading screens An APU with Unified Address Space

– Can be used to decompress new assets without taxing the CPU or discrete GPU

– Perhaps even use HSAIL I/O to read from disk – A better streaming experience for gamers


PATHFINDING

Some strategy games simulate thousands of units Pathfinding over complex terrain with thousands of

moving units is very expensive Clever approximate solutions are often used

– Supreme Commander 2 “Flow field” GPGPU pathfinding with HSA

– Use one GPU thread per unit to do a deep search for an optimal path

– With HSA such an algorithm can page all requisite data from system memory and write back found paths

– APU could be fully saturated with pathfinding without impacting framerate


CONCLUSION

Many algorithms in games are suitable for offloading to the GPU Heterogeneous Systems Architecture solves two major obstacles

– Latency – Memory access

HSAIL allows for entirely new kinds of GPGPU programs APUs can be used to offload the CPU HSA will finally make GPUs available to developers as full-featured co-processors


THANK YOU

Any questions?


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AMD 2012: HSA in Gaming

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