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

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

• Eric M. Dashofy
• Computer Systems Research Department
• The Aerospace Corporation

• CSRD/CSTS/ETG
• March 8, 2011

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Ill give you the answer up front

• It comes in many different sizes
• Small
• Medium
• Large
• Super-Size Me!
• Its cheap and it doesnt last long
• Nearly any alternative is healthier

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Outline

• Ill 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

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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 SIMD Inst. 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)?
• Ill 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

• Moores 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?

• Moores law the number of transistors on a chip
  will double every 18 months

Multicore VLIW/EPIC
Multicore SMT
GPUs
VLIW/EPIC
Heterogeneous Multiprocessor
Multicore
SMT, multi-issue
SMT, in-order
Superscalar, multi-issue
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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
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Medium Fries

• Kinda-Embedded Computing

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Kinda-Embedded Computing

• New computing platforms and processors emerging
  in a niche we havent 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 Processing

• Are 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 implementations

• Comparing Gumstix implementation strategies to
  Mac Mini

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Energy Use

• Its 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.

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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
Base Station PC

• Drop a bunch of motes in an area
• Distance between them 50-100 meters
• One distinguished mote is the gatewaythis is
  connected to an ordinary PC bya serial
  connection or Ethernettransceiver for data
  collection

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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 gatewaythis 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 parentin the network
• Base station ultimately gets all data

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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
XMesh forms and maintains ad-hoc network also
provides interface for other TinyOS components to
send and receive messages.
Map UI
Tables
Web UI
XServe receives data from base station mote and
makes itavailable to user-space apps
XMesh
Timers/Utilities
Device Drivers
Device Control
User App
Socket
TinyOS
XServe
Mote software based on open-source TinyOS. A
thin component model is built on top of that.
Motes communicateover 802.15.4 protocol(used in
ZigBee devices) Long-range, low data rate.
Serial
USB
Ethernet
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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 -gt BlinkM.StdControl 
Main.StdControl -gt SingleTimer.StdControl 
BlinkM.Timer -gt SingleTimer.Timer  BlinkM.Leds
-gt 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 costbenefit ratio

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