Application Performance Analysis of the Cortex-A9 MPCore

Application Performance Analysis

of the Cortex-A9 MPCore

Bryan Lawrence

This project in ARM is in part funded by ICT-eMuCo, a European project supported under the Seventh Framework Programme (7FP) for research and technological development

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Agenda



Motivation



Experimentation platforms



Performance exploration of different application classes



Performance evaluation of multiple concurrent applications



Summary and conclusion

Phone ++ Upcoming Use Cases



Mobile Internet Browsing



Video conferencing



Gaming on the Go



Multi-player over 3G / 4G Network

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Mobile Phone Applications

Compute Intensive

Tablet Applications

Compute Intensive

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Achieving Scalable Performance



Clock frequency of processor not the only metric of performance



Scalable, energy efficient performance required from mobile devices – phones, tablets to large enterprise computing

Hardware Platforms



Versatile Express

 ARM-NEC Cortex™-A9 processor

test-chip ~400MHz

 Cortex-A9 x 4

 4x NEON™/FPU

 32KB I&D invidual L1 caches

 512K L2 cache

 1GB RAM (32b DDR2)



Early Partner Silicon

 Cortex-A9 x 2 @ 1GHz

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Video Decode / Encode



Hardware encoder/decoders are common in consumer



Video/audio codecs standards evolve rapidly



Many codecs are used infrequently to justify h/w



Consumer applications involve other video processing

 Different from encode / decode (E.g. video editing)



Simultaneous encode / decode required for video conferencing



FFmpeg used for decode



X264 library used with FFmeg for video encode



CIF & VGA resolutions

 Commonly used in video conf.



Movie trailers used

 Order of computation more than video conf. Streams



Compression factor of 100 - 200

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H.264 Decode / Encode



Results for single core operation

 Normalized logarithmic scales used

 Encode is more compute intensive than decode (at least ~2-3 times)

 Writing out decoded streams

to secondary storage media limited by media bandwidth

H.264 Decode / Encode



Concurrent video decode + encode

 Important use case for video conferencing

 Excellent scalability is observed for up to all 4 cores

 Encoding is at least

2-3 times or more compute intensive than decode

 Ideally more resources

should be dedicated to encode

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On2/Google VP8



Libvpx library used for decoding VP8 (from WebM project)



Libvpx uses multi-threading and actively takes advantage of parallelizability available in the VP8 codec.



Comparative results obtained on Versatile Express and 1GHz dual core platforms

On2/Google VP8



Shows good scalability with the number of cores.



Scalability is relatively independent of the number of partitions in the video frame



Saturation is observed for no. of threads > no. of cores



Designers can query the platform to fetch the no. of cores –

determine available paralelizability

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Compilation - ffmpeg



Code compilation has inherent parallelism in terms of modules



Most build systems allow for this compilation to be exploited

 E.g. make –j 4



Compilation of FFmpeg and Linux Kernel shown here

1GHz dual-core

Compilation – Linux Kernel

1GHz dual-core



Almost linear speed-up is observed with no. of cores for both cases



Effectively doubles (quadruples) the utilized memory bandwidth for 2 cores (4 cores)

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Browsers



Browser benchmark using collection of web-pages similar to the mix found in common browsing



Speed-up of 1.54 times observed between single and dual core execution



The ‘webcore’ fraction of the pie grows for multicore execution

Normalized Performance Execution time decomposition

Multiple Concurrent Applications



Multitasking is becoming mainstream in mobile devices today



Common combinations include

 Browser + Audio playback

 E.g. Internet Radio

 Browser + background download



Independent applications can benefit immensely from

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Browser + Pandora Internet Radio



Speed up factor of 1.9



Super linear speed-up can be observed sometimes due to reduced cache pollution from conflicting applications



The speed-up can be traded for energy by

slowing the cores down (depends on the

fabrication process technology used)

Normalized Performance

Execution time decomposition

Browser + Internet File Download



Speed up factor of 1.64x



Common use case

involves downloading an App from an application store or market-place while browsing the internet



Email synchronization in the bakground also forms

Normalized Performance

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Cortex-A9 MP Benefits – Performance

Browser (single app)

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1.54

1 Core 2 Core

Cortex-A9 MP Benefits – Richer Experience

Browser (single app)

1

1.54

Browser + Pandora

0.78

1.50

Browser + Download

0.73

1.20

1 Core 2 Core

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Cortex-A9 MP Benefits – Richer Experience

Browser (single app)

1

1.54

Browser + Pandora

0.78

1.50

Browser + Download

0.73

1.20

1 Core 2 Core 1.64x 1.9x

Summary and Conclusion



This presentation demonstrates the scalability of the ARM Cortex-A9 MPCore™ processor across various classes of applications, on today’s currently available software



Better power/performance can be achieved using an efficient low power ARM multicore processor, as compared to a single processor at much higher freq.



Next generation software will make more intensive use of threads, and scalability will improve further.

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Thank You

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