© The Aerospace Corporation 2011

Why 21st Century Software Engineering is More Like French Fries than Ever

Eric M. DashofyComputer Systems Research DepartmentThe Aerospace Corporation

CSRD/CSTS/ETGMarch 8, 2011

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I’ll give you the answer up front…

• It comes in many different sizes:– Small

– Medium

– Large

– Super-Size Me!

• It’s cheap and it doesn’t last long

• Nearly any alternative is healthier

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Outline

• I’ll talk about three areas of interest at different scales– Small: Wireless sensor networks

– Medium: Tiny embedded computers, clustered

– Large: High-performance and cloud computing

• And identify some unique challenges in each area

• And describe some Aerospace research or investigations in each one

© The Aerospace Corporation 2011

Large Fries

High-Performance Computing

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High-Performance Computing

• Critical domain to work of The Aerospace Corporation• Solving computationally-intensive problems, generally with large data

sets• Sometimes data sets are from “real” sensor sources:

– Signal processing, image processing• Sometimes they are synthesized

– e.g., find optimal satellite configuration through exhaustive search or Monte-Carlo method

• …using a variety of different, interconnected computing architectures, techniques, and resources beyond a single core/serial implementation.

• HPC-style resources are everywhere: laptops, desktops, gaming consoles, embedded processors…

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Major trends in HPC

• Increasing the speed of individual processing elements– i.e., more megahertz!

• Increasing the “smarts” within processors– i.e., longer pipelines, branch prediction, speculative execution

• Parallelism is the name of the game: dwarfs megahertz and smarts

At the level of… Through…

Instructions Multi-issue processors

Data elements Vector processing, SIMD instructions

Threads “Hyperthreading”

Processor elements Multicore processors, Heterogeneous Multicore

Processors Symmetric Multiprocessing (SMP), NUMA

Machines Cluster-based computing

Clusters Grid computing

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Increasing Complexity in HPC

• Put another way…

Chip Multi-issue

SIMDInst.

SIMD Units

SMT Multi-core

VLIW/EPIC

Multi-Proc.

H.M.P.

486 X

Pentium X X

PII/III X X X

P4 X X X X

Core… X X X X

GPUs X X

Power6 X X X X X

Itanium X X X X

Cell X/X X/_ X/X X X

Not shown: FPGAs, tile-based computing…

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A Challenge Problem

• What is the fastest way to transform a large amount of data through three transformations (“Red”, “Green”, and “Blue”)?– I’ll give you two options (assume both will result in the same output)

Data

Data

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A Challenge Problem: Option 1

• Do all the red, then all the green, then all the blue.

Data

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A Challenge Problem: Option 2

• Chunk up the data and do red, then green, then blue on each chunk.

Data

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Ordinary parallelism is only one side of the story…• Moore’s Law: The number of transistors on a chip will double every ~18 months

Credit: User Wgsimon; used under Creative Commons ShareAlike 3.0 License

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…but what are we doing with those transistors?• Moore’s law: the number of transistors on a chip will double every

~18 months

Superscalar, multi-issue

SMT, in-order

SMT, multi-issueMulticore

VLIW/EPIC

Multicore VLIW/EPIC

GPUs

Heterogeneous Multiprocessor

Multicore + SMT

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Key Challenges

• Parallelism is still hard for people to understand and master– Some techniques – OpenMP, actor model – ease things

• Trying out different parallelization strategies is still expensive and labor-intensive

• Good tooling is hard to get, because the platforms are evolving faster than the tools can be built

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A Little Research

© The Aerospace Corporation 2011

Medium Fries

Kinda-Embedded Computing

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“Kinda-Embedded” Computing

• New computing platforms and processors emerging in a niche we haven’t seen before– The power, programmability, and connectivity of an (old) desktop

computer

– In extremely small form factors

• Largely driven by developments in cell phone technology

• Meet Gumstix Overo

• 600Mhz ARM-based processor w/extensions

• 256MB RAM, 256MB Flash, plus SD card (up to 8GB)

• 10/100 Ethernet, 54mbps WiFi, Bluetooth

• USB, HDMI

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Applications

• Cell phones, tablets, PDAs, other handhelds

• “Plug Computers”

• Car-puters

• Walltops

• Location-aware applications

• Situational Awareness

• Micro web-servers

• Spaceborne(?)

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Research: Gumstix for HPC-style ProcessingAre they viable for future spaceborne applications?

• Worked with a UCSB student team and a summer intern

• Developed two clusters– One homemade

– One on Gumstix “Stagecoach” backplane

• Ported Range-Doppler SAR algorithm to Gumstix and parallelized it

• Compared performance and power usage to a small-form-factor desktop (Mac Mini)

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Performance of various implementationsComparing Gumstix implementation strategies to Mac Mini

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Energy UseIt’s slower, but how much energy does it use?

Gumstix extremely close to Mac Mini in terms of power consumption

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Key Challenges

• Optimization can make performance vary widely– Software engineering gives us few tools to do that optimization

• Biggest performance gains at very low levels of abstraction

• Likely to see increasing diversity in processors at this scale– Will what we learn on one apply to another?

• True mobility requires batteries– Power-aware computing probably increasingly important

– But the lure of these platforms is how similar they are to platforms where we can blithely ignore power use.

© The Aerospace Corporation 2011

Small Fries

Wireless Sensor Networks

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Wireless Sensor Networks

• A wireless network of physically distributed small computing devices (colloquially: “motes”) equipped with tiny sensors

• Additional characteristics:

– Network topology may be fixed, variable, or ad-hoc

• Motes may be mobile, though infrequently

– Devices expected to run for months or years unattended

– Data collected generally forwarded to a central collection point

– Network should survive the loss of numerous motes

• Applications:

– Environmental monitoring

– Factory/industrial monitoring

– Target detection and tracking

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Meet Mica

• MicaZ Mote– 8Mhz Atmel ATMega128 Microcontroller (MIPS-like assembly language)– 4KB of RAM– 512KB of Flash (usually partitioned into 4x128KB program blocks)– CC2420 802.15.4 “Zigbee”-compliant radio

• 7 power usage modes– Several analog inputs, several digital inputs, I2C

• Sensor boards stack on top– Light, temperature, humidity,

orientation (magnetometer),acceleration, location (GPS),microphone (levels only)

Crossbow MicaZ Mote

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How Motes Work

• Drop a bunch of motes in an area

• Distance between them: 50-100 meters

• One distinguished mote is the “gateway;”this is connected to an ordinary PC bya serial connection or Ethernettransceiver for data collection

GW

Base Station PC

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How Motes Work

• Drop a bunch of motes in an area

• Distance between them: 50-100 meters

• One distinguished mote is the “gateway;”this is connected to an ordinary PC bya serial connection or Ethernettransceiver for data collection

• Software on the motes allows themto self-organize, collect data, andreport that data to their ‘parent’in the network– Base station ultimately gets all data

GW

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A sample application we developed

• Signal transmitter tracking application

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A sample application we developed

• Cell-phone tracking application

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The Software Stacks

TinyOS

XM

esh

Tim

ers/

Util

ities

Dev

ice

Driv

ers

Dev

ice

Con

trol

Mote software based on open-source

TinyOS. A thin component

model is built on top of that.

XMesh forms and maintains ad-hoc

network; also provides interface for other TinyOS components to

send and receive messages.

Motes communicateover 802.15.4 protocol

(used in ZigBee devices)Long-range, low data rate.

Serial USB Ethernet

XServe

Socket

User App

XServe receives data

from base station mote and makes itavailable to

user-space apps

Map

UI

Tab

les

Web

UI

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Software on the Motes

• TinyOS developed concurrently with the Mica family motes– Mica motes still probably the #1 platform for TinyOS – Other platforms supported

• Amazingly, there is a thin but useful component model

• Components written in nesC (a dialectof C) with well-defined interfaces

• Interfaces connected through aconfiguration diagram that resemblesearly module interconnectionlanguages (e.g., Polylith)

configuration Blink {}implementation {  components Main, BlinkM, SingleTimer, LedsC;

  Main.StdControl -> BlinkM.StdControl;  Main.StdControl -> SingleTimer.StdControl;  BlinkM.Timer -> SingleTimer.Timer;  BlinkM.Leds -> LedsC;}

…

interface Timer {  command result_t start(char type, uint32_t interval);  command result_t stop();  event result_t fired();}

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Key Challenges

• Extreme power management is key– How can we better integrate higher-level power management

principles into the sensor architecture?• Control flow is low-level and painful

– Can we alleviate this with higher-level models that are “compiled down” to implementations without sacrificing power?

• Are there architectural styles appropriate for these applications?– How do you build applications that use computation and

aggregation in-the-mesh to reduce data transmission?• How do you build debuggable systems?• How do you transition to next-generation systems?

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Conclusion

• We are faced with yet-another “Cambrian Explosion” of– Scales

– Platforms

• Software engineering knowledge and insight lacking in these domains– For the smaller domains, abstraction (the key to software engineering) is

the enemy of performance and battery life

• We are building platforms faster than we can build tools and far faster than we can build skills– Domain experts often have little formal training in software engineering

• Need lightweight “force multipliers” with a very low cost:benefit ratio

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