Dr. Frederica Darema

1
Research and Technology Advances in Systems
Software for Emerging Computer Systems EDGE
2006 Workshop
Dr. Frederica Darema Senior Science and
Technology Advisor NSF
2
Outline

• The BIG PICTURE
• Applications Directions
• Computing Platforms Directions
• Research and Technology Directions
• Examples of some advances
• Future Challenges and Opportunities

3
Science, Engineering, and Commercial
Applications Environments how are they
shaping in the future

• What does it entail forMulticore Processors
• and
• for Computing in the Larger-Scale

4
Applications Directions
Past

• Computation Intensive
• Batch
• Hours/days

• Mostly monolithic
• Mostly one programming language

Present / Future

• Computation Intensive
• Data Intensive
• Real Time
• Few Minutes/hours
• Visualization
• Interactive Steering
• Integrated SimulationsExperiments Dynamic Data
  Driven Applications Systems

• Multi-Modular
• Multi-Language
• Multi-Developers
• Multi-Source Data

5
Platforms Directions

• Vector Processors
• SIMD MPPs
• Distributed Memory MPs
• Shared Memory MPs

Past

• Distributed Platforms,
  Heterogeneous Computers and Networks
• Heterogeneity
• architecture
• (computer network)
• node power
  (supernodes, MCP)

Present/Future

• Latencies
• variable (internode, intranode)
• Bandwidths
• different for different links
• different based on traffic

GiBs
Grids
Petaflops Platform (Grid-in-a-Box)
Distributed Platform
.
MPP
NOW
SP
6
EXAMPLE OF EMERGING DIRECTIONS Dynamic Data
Driven Application Systems (DDDAS) New Direction
for applications/simulations and measurement
methodology Multi-agency DDDAS program NSF,
NIH, NOAA with cooperation with the EU/IST
e-Sciences Programs (www.cise.nsf.gov/dddas)
7
What is DDDAS
(Symbiotic MeasurementSimulation Systems)
Simulations (Math.Modeling Phenomenology Observati
on Modeling Design)
Theory (First Principles)
Simulations (Math.Modeling Phenomenology)
Theory (First Principles)
Experiment Measurements Field-Data (on-line/archiv
al) User
Measurements Experiments Field-Data User
Dynamic Feedback Control Loop
Challenges Application Simulations
Development Algorithms Measurement Instruments
Interfaces Computing Systems Support
8
Beyond Grid Computing Extended Grid the
Application Platform is the computationalmeasure
ment system
Applications
Archival/ Stored Data
Computational Platforms
Instruments
Sensors
Measurements
Computational Grids
9
LEAD Users INTERACTING with Weather
Interaction Level II Tools and People Driving
Observing Systems Dynamic Adaptation
NWS National Static Observations Grids
Virtual/Digital Resources and Services
ADaM
ADAS
Mesoscale Weather
Tools
Remote Physical (Grid) Resources
Local Physical Resources
Local Observations
10
(No Transcript)
11
Examples of Computational Reqs - examples from
DDDAS applications - often results needed in
Real-Time or near-RT

• Water Pollution/Contaminant Transport/Detection
• Todays problem 500nodes- 4.4Pflops1.2GBmem.02G
  B/s -gt
• Large/Projected problem 10,000nodes-212Pflops
  10.2GBmem .9GB/sec)
• Chemical Pollution/Contaminant Transport/Detection
  
• Todays problem 2000nodes(Lemieux) 4TBmem
  5hrs -gt
• Large/Projected problem 10Knodes 20TBmem 1hr
• results in Real-Time (or near RT)
  50-100Knodes
• Protein Folding
• Todays problem 1024nodes(IBM-BlueGeneL)
  6/7days for 1 protein
• (w 150aminoacids)
• ElectricPowerGrid
• Todays problem 100Gflops 50MBmem
• Aircraft modeling
• Todays problem Full FEMCFD 384,000cpu-hrs
  320GBmem
• ROM 72secs 78KB
• Fire Propagation
• Todays problem FireModel 100procs(BG,
  Teragrid clusters) 30GBmem 1hr-5hrs
• Coupled Weather/Fire 100-1000nodes
  200-400GBmem

12

• So
• Processing at multiple levels
• Computation and data processing both at the
  application and the instruments/sensors side
• Multicores in high-end platforms, workstations,
  visualization servers, data servers, etc,
• Multicores at application side
• Multicores at the data collection side
• . MULTICORES EVERYWHERE!!!

13
Some Challenges

• Programmability
• models of concurrency, multiple heterogeneous
  models
• optimized performance
• application
• system
• scalability across multiple levels
• application algorithms
• systems software
• fault tolerance, recovery, reliability, security
• power management
• verification, validation
• ..
• These challenges have been articulated for years
  on the past and present platforms
• Multicores add to the complexity of all the above

14
The need for a holistic approach

• Large-Scale Systems does not entail only flops
  (Giga-, Tera-, Peta-, Zetta-,)
• Large-scale parallel systems are the POWERFUL
  nodes/platforms - in balance with other resources
  in the system
• Analogy the stars and the galaxy within the
  cosmos
• Methods andTools needed at all levels, and they
  need to work together synergistically

15
Large-Scale Systems (e.g. Enabling DDDAS)
Applications Modeling Measurements
Dynamic Data-Driven Application
Systems -- Symbiotic MeasurementSimulation Syste
ms
Systems Software (NGS 1998-2004) (CSR/AESSMA
2004-todate)
Dynamic Compilers Application Composition
Performance Engineering
16
System Modeling and Analysis (SMA)(a component
of the Computer Systems Research Program)(CSR
Program)

• Develop methods and tools for modeling,
  measuring, analyzing, evaluating, and predicting
  the performance and dependability of complex
  computing and communications systems taking a
  system level view
• Topics of Interest
• Hardware and Software modeling
• methods tools and measurements, providing
  multimodal, hierarchical or multilevel modeling
  and analysis capabilities of such systems
• methods that describe components of the system,
  but also the system as a total, and enable
  assessment of the effects of individual hardware
  and software layers and components of these
  systems
• ability to describe the system in multiple levels
  of detail (characteristics and time-scales)
• combine different methods of describing
  components and layers

17
System Modeling and Analysis (SMA)

• Topics of Interest (contd)
• Novel modeling and measurement approaches
• Develop capabilities to describe, analyze and
  predict the behavior of the components as well as
  the systems Analysis and prediction due to
  changes in the application, system software,
  hardware multilevel approaches and multi-modal
  approaches
• Performance Frameworks
• combine tools in plug-and-play fashion
• multiple views of the system

18
Multiple views of the system The applications
view
Distributed Applications
. . .
Collaboration
Visualization
Environments
Authenication
/
Scalable I/O
Data Management
Authorization
IO / File
Archiving/Retrieval
Dependability
Models
Services
Services
. . .
Other Services
OS
Scheduler
Distributed Systems Management
Models
Architecture /
Distributed, Heterogeneous, Dynamic, Adaptive
Network
Computing Platforms and Networks
Models
Memory
Device
CPU
Memory
. . .
Models
Technology
Technology
Technology
19
Advanced Execution Systems (AES) (a component of
the Computer Systems Research Program)(CSR
Program)

• Seeks to create systems software to facilitate
  the development and runtime support of complex
  applications executing on large, heterogeneous
  high-end computing and grid platforms
• AES emphasizes runtime compiling systems and
  application composition systems interface with
  the underlying operating systems services and
  incorporating systems modeling and analysis
  methods and tools. 
• Topics of Interest
• Novel Compiler Technology that go beyond the
  standard static notion of a compiler
• for example by embedding a portion of the
  compiler in the runtime and endowing the system
  with resource awareness and adaptive mapping
  capabilities 
• new compiler techniques for determining
  functional and data dependencies across multiple
  levels of memory hierarchy and across platforms
• mechanisms for matching an applications resource
  needs to underlying resources when both are
  changing as the application executes

20
Advanced Execution Systems (AES)

• Topics of Interest
• Programming models and tools
• expressing application partitioning across
  distributed, heterogeneous computing platforms
  application-level checkpointing and recovery
• Application composition system (ACS) technology
• constructing applications to fit the available
  resources and to adapt to changes in the
  underlying execution environment
• methods for automatically selecting application
  components
• creating knowledge bases for application
  components interfacing with the underlying
  computing platform models to determine suitable
  application components
• and developing appropriate application component
  libraries and interfaces so the run-time portion
  of the RCS can link to such libraries.

21
The AES component develops technology for
integrated feedback control Runtime Compiling
System (RCS) and Dynamic Application Composition
Application Model

Dynamic Analysis Situation
Distributed Programming Model
Application Program
Compiler Front-End
Application Intermediate Representation
Compiler Back-End
Launch Application (s)
Performance Measuremetns Models
Dynamically Link Execute
Application Components Frameworks
Distributed Computing Resources
Distributed Platform
Adaptable computing Systems Infrastructure
22
Examples of areas funded

• Programming Models, Languages, Environments
• legacy models (MPI), to high-level, domain,
  hierarchical multithreading, software component
  libraries, dynamic workflow, streaming
  environments (languages/compilers),
• Compiler methods and tools
• program analysis methods program transformation
  methods
• program Phase detection dynamic detection
• combine static, dynamic, and feedback methods
  Continuous optimization methods
• scheduling, scalability across hierarchies
• checkpoint recovery (system level, application
  level)
• Real-Time systems and integration (with server,
  high-end, etc environments)
• Systems management including power-management
• optimization constraints ( performancepower
  optimization)
• Validation, Verification, Testing
• System Modeling and Analysis
• Modeling of applications, algorithms, platforms
  (at all levels)
• performance, dependability (performability),
  reliability
• multi-modal modeling, power modeling (at all
  levels application, computational platforms,
  processor/multicores), Performance specification
  (languages, compilers) performance frameworks
• Fast real-time or near-real-time simulation
  methods

Have seen an increase in all these areas with
respect to MULTICORES
23
Summary Thoughts

• MultiCoreProcessors provide an opportunity for
  enhanced capabilities in computation,
  communication and data management
• Multicores present the promise of populating all
  levels of computational platforms and
  environments
• They should be viewed in the presence of other
  resources heterogeneity, dynamicity, adaptivity
• Multicores cannot exist in isolation they will
  be nodes in other systems, high-end platforms,
  servers, real-time systems, instruments, and
  grids (InformationPowerGrid, TeraGrid)
• Complexity of applications and platforms presents
  a significant opportunity for innovative research
  and technology in systems software (methods
  tools)
• Multicores will resurrect and build upon
  ideas/methods started in 80s shared memory
  parallel processing and the recent advances for
  distributed systems
• Need to advance the technologies that will
  automate the mapping of such complex and dynamic
  applications on complex platforms with multiple
  and heterogeneous levels of processors, memory,
  and networks
• An important item do we nurture a critical mass
  of people that will work on these challenges?
• (where are the compiler people to
  address/contribute to these challenges?!!!)
• I personally hope that the opportunities of
  MultiCoreProcessors will attract the attention
  and the people needed