Mass Market Applications of Massively Parallel Computing

Chas. Boyd

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Outline

• Projections of future hardware• The client computing space• Mass-market parallel applications• Common application characteristics• Interesting processor features

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The Physics of Silicon

• The way processors get faster has fundamentally changed

• No more free performance gains due to clock rate and Instruction-Level Parallelism

• Yet gates-per-die continues to grow• Possibly faster now that clock rate isn’t an issue• Estimate: doubling every 2-2.5 years

• New area means more cores and caches• In-order core counts may grow faster

than Out-of-Order core counts do

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The Old Story

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A Surplus of Cores

• ‘More cores than we know what to do with’• Literally

• Servers scale with transaction counts• Technical Computing

• history of dealing with parallel workloads• What are the opportunities for the PC client?

• Are there mass market applications that are parallelizable?

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Requirements of Mass Market Space

• Fairly easy to program and maintain• Cannot break on future hardware or

operating systems• Transparent back-compatibility, fwd compatibility• Mass market customers hate regressions!• Consumer software must operate for decades

• Must get faster automatically• Why we are here

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AMD Term:

• Personal Stream Computing• Actually nothing like ‘stream processing’ as used

by Stanford Brook, etc.

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Data-Parallel Processing

• Key technique, how do we apply it to consumers?

• What takes lots of data?• Media, pixels, audio samples• Video, imaging, audio• Games

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Video

• Decode, encode, transcode• Motion Estimation, DCT, Quantization

• Effects• Anything you would want to do to an image• Scaling, sepia, DVE effects (transitions)

• Indexing• Search/Recognition -convolutions

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Imaging

• Demosaicing• Extract colors with knowledge of sensor layout

• Segmentation• Identify areas of image to process

• Cleanup• Color correction, noise removal, etc.

• Indexing• Identify areas for tagging

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User Interaction with Media

• Client applications can/should be interactive• Mass market wants full automation• ‘Pro-sumer’ wants some options to participate, but with

real-time feedback (20+ fps) on 16 GPixel images• Automating media processing requires analysis

• Recognition, segmentation, image understanding• Is this image outdoors or inside?• Is this image right-side up?• Does it contain faces?• Are their eyes red?

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Imaging Markets

• In some sense, the broader the market, the more sophisticated the algorithm required• Although pro-sumers care more about performance, and

they are the ones that write the reviews

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FFT Performance

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Game Applications of Mass Parallel

• Rendering• Imaging• Physics• IK• AI

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Ultima Underworld 1993

Dark Messiah 2007Dark Messiah 2007

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Game Rendering

• Well established at this point, but new techniques keep being discovered

• Rendering different terms at different spatial scales• E.g. Irradiance can be computed more sparsely

than exit radiance enabling large increases in the number of incident light sources considered

• Spherical harmonic coefficient manipulations

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Game Imaging

• Post processing• Reduction (histogram or single average value)

• Exposure estimation based on log average luminance

• Exposure correction• Oversaturation extraction• Large blurs (proportional to screen size)• Depth of field• Motion blur

Half-Life 2Half-Life 2

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Half-Life 2Half-Life 2

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Half-Life 2Half-Life 2

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Game Physics

• Particles -non-interacting• Particles -interacting• Rigid bodies• Deformable bodies• Etc.

Game Processor EvolutionGame Processor Evolution

Vertex Shader

Pixel Shader

AnimationAI

Texture Creation

Mesh ModelingPhysicsContent Creation Process

Game Stack

Offline

CPU

GPU

Real Time

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Common Properties of Mass Apps

• Results of client computations are displayed at interactive rates• Fundamental requirement of client systems

• Tight coupling with graphics is optimal• Physical proximity to renderer is beneficial

• Smaller data types are key

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Support for Image Data Types

• Pixels, texels, motion vectors, etc.• Image data more important than float32s

• Data declines in size as importance drops• Bytes, words, fp11, fp16, single, double

• Bytes may be declining in importance• Hardware support for formatting is useful• Clock cycles required by shift/or/mul, etc.

cost too much power

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I/O Considerations

• Like most computations that are not 3-D rendering, GPUs are i/o bound

• Arithmetic intensity is lower than GPUs• Convolutions

• Support for efficient data types is very important

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GPU Arithmetic Intensity Projection

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GPU Arithmetic Intensity Projection

• 2-3 more process doublings before new memory technologies will help much• Stacked die?, 2k wide bus?, optical?

• Estimate at least 4x increase in nr of compute instructions per read operation

• Arithmetic intensities reach 64??• This is fine for 3-D rendering• Other data-parallel apps need more i/o

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I/O Patterns

• Solutions will have a variety of mechanisms to help with worsening i/o constraints

• Data re-use (at cache size scales) is relatively rare in media applications

• Read-write use of memory is rare• Read-write caches are less critical• Streaming data behavior is sufficient

• Read contention and write contention are the issue, not read-after-write scenarios

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Interesting Techniques

• Shared registers• Possibly interesting to help with i/o bandwidth• Reducing on-chip bandwidth may help

power/heat• Scatter

• Can be useful in scenarios that don’t thrash output subsystem

• Can reduce pressure on gather input system

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Convolution

• Key element of almost all image and video processing operations• Scaling, glows, blurs, search, segmentation

• Algorithm has very low arithmetic intensity• 1 MAD per sample

• Also has huge re-use (order of kernel size)• Shared registers should reduce arithmetic

intensity by factor of kernel size

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Processor Core Types

• Heterogeneous Many-core• In-Order vs. Out-of-Order

• Distinction arose from targeting 2 different workload design points

• Software can select ideal core type for each algorithm (workload design point)• Since not all cores can be powered anyway

• Hardware can make trade-offs on:• Power, area, performance growth rate

Workloads

Local Memory Accesses Streaming Memory Access

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CPUs

GPUs

Workload Differences

General Processing• Small batches• Frequent branches• Many data inter-

dependencies• Scalar ops• Vector ops

Media Processing• Large batches• Few branches• Few data inter-

dependencies• Scalar ops• Vector ops

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Lesson from GPGPU Research

• Many important tasks have data-parallel implementations• Typically requires a new algorithm• May be just as maintainable

• Definitely more scalable with core count

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APIs Must Hide Implementations

• Implementation attributes must be hidden from apps to enable scaling over time• Number of cores operating• Number of registers available• Number of i/o ports• Sizes of caches• Thread scheduling policies

• Otherwise, these cannot change, and performance will not grow

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Order of Thread Execution

• Shared registers and scatter share a pitfall:• It may be possible to write code that is dependent

on the order of thread execution• This violates scaling requirement

• The order of thread execution may vary from run-to-run (each frame)

• Will certainly vary between implementations• Cross-vendor and within vendor product lines

• All such code is considered incorrect

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System Design Goals

• Enable massively parallel implementations• Efficient scaling to 1000s of cores• No blocking/waiting• No constraints on order of thread execution• No read-after-write hazards

• Enable future compatibility• New hardware releases, new operating systems

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Other Computing Paradigms

• CPU –originated:• Lock-based, Lockless• Message Passing• Transactional Memory• May not scale well to 1000s of cores

• GPU Paradigms• CUDA, CtM• May not scale well over time

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High Level APIs

• Microsoft Accelerator• Google Peakstream• Rapidmind• Acceleware• Stream processing

• Brook, Sequoia

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Additional GPU Features

• Linear Filtering• 1-D, 2-D, 3-D floating point array indices• Image and video data benefit

• Triangle Interpolators• Address calculations take many clocks

• Blenders• Atomic reduction ops reduce ordering concerns

• 4-vector operations• Vector data, syntactic convenience

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Processor Opportunities

• Client computing performance can improve• Client space is a large un-tapped

opportunity for parallel processing• Hardware changes required are minimal

and fairly obvious• Fast display, efficient i/o, scalable over time