Research @ Northeastern University

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Research _at_ Northeastern University

• I/O storage modeling and performance
• David Kaeli
• Soft error modeling and mitigation
• Mehdi B. Tahoori

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I/O Storage Research at Northeastern University

• David Kaeli
• Yijian Wang
• Department of Electrical and Computer Engineering
  
• Northeastern University
• Boston, MA
• kaeli_at_ece.neu.edu

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Outline

• Motivation to study file-based I/O
• Profile-driven partitioning for parallel file I/O
• I/O Qualification Laboratory _at_ NU
• Areas for future work

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Important File-base I/O Workloads

• Many subsurface sensing and imaging workloads
  involve file-based I/O
• Cellular biology in-vitro fertilization with
  NU biologists
• Medical imaging cancer therapy with MGH
• Underwater mapping multi-sensor fusion with
  Woods Hole Oceanographic Institution
• Ground-penetrating radar toxic waste tracking
  with Idaho National Labs

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The Impact of Profile-guided Parallelization on
SSI Applications

• Reduced the runtime of a single-body Steepest
  Descent Fast Multipole Method (SDFMM) application
  by 74 on a 32-node Beowulf cluster
• Hot-path parallelization
• Data restructuring
• Reduced the runtime of a Monte Carlo
• scattered light simulation by 98 on
• a 16-node Silicon Graphics Origin 2000
• Matlab-to-C compliation
• Hot-path parallelization
• Obtained superlinear speedup of Ellipsoid
• Algorithm run on a 16-node IBM SP2
• Matlab-to-C compliation
• Hot-path parallelization

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Limits of Parallelization

• For compute-bound workloads, Beowulf clusters can
  be used effectively to overcome computational
  barriers
• Middlewares (e.g., MPI and MPI/IO) can
  significantly reduce the programming effort on
  parallel systems
• Multiple clusters can be combined, utilizing Grid
  Middleware (Globus Toolkit)
• For file-based I/O-bound workloads, Beowulf
  clusters and Grid systems are presently
  ill-suited to exploit the potential parallelism
  present on these systems

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Outline

• Motivation to study file-based I/O
• Profile-driven partitioning for parallel file I/O
• I/O Qualification Laboratory _at_ NU
• Areas for future work

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Parallel I/O Acceleration

• The I/O bottleneck
• The growing gap between the speed of processors,
  networks and underlying I/O devices
• Many imaging and scientific applications access
  disks very frequently
• I/O intensive applications
• Out-of-core applications
• Work on large datasets that cannot fit in main
  memory
• File-intensive applications
• Access file-based datasets frequently
• Large number of file operations

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Introduction

• Storage architectures
• Direct Attached Storage (DAS)
• Storage device is directly attached to the
  computer
• Network Attached Storage (NAS)
• Storage subsystem is attached to a network of
  servers and file requests are passed through a
  parallel filesystem to the centralized storage
  device
• Storage Area Network (SAN)
• A dedicated network to provide an any-to-any
  connection between processors and disks

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I/O Partitioning
P
An I/O intensive application
Disk
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I/O Partitioning

• I/O is parallelized at both the application level
  (using MPI and MPI-IO) and the disk level (using
  file partitioning)
• Ideally, every process will only access files on
  local disk (though this is typically not possible
  due to data sharing)
• How to recognize the access patterns?
• Profile-guided approach

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Profile Generation
Run the application
Capture I/O execution profiles
Apply our partitioning algorithm
Rerun the tuned application
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I/O traces and partitioning

• For every process, for every contiguous file
  access, we capture the following I/O profile
  information
• Process ID
• File ID
• Address
• Chunk size
• I/O operation (read/write)
• Timestamp
• Generate a partition for every process
• Optimal partitioning is NP-complete, so we
  develop a greedy algorithm
• We have found we can use partial profiles to
  guide partitioning

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Greedy File Partitioning Algorithm
for each IO process, create a partition for each
contiguous data chunk total up the of
read/write accesses on a process-ID basis if
the chunk is accessed by only one
process assign the chunk to the associated
partition if the chunk is read (but never
written) by multiple processes duplicate the
chunk in all partitions where read if the chunk
is written by one process, but later read by
multiple assign the chunk to all partitions
where read and broadcast the updates on
writes else assign the chunk to a shared
partition For each
partition sort chunks based on the earliest
timestamp for each chunk
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Parallel I/O Workloads

• NASA Parallel Benchmark (NPB2.4)/BT
• Computational fluid dynamics
• Generates a file (1.6 GB) dynamically and then
  reads it back
• Writes/reads sequentially in chunk sizes of 2040
  Bytes
• SPEChpc96/seismic
• Seismic processing
• Generates a file (1.5 GB) dynamically and then
  reads it back
• Writes sequential chunks of 96 KB and reads
  sequential chunks of 2 KB
• Tile-IO
• Parallel Benchmarking Consortium
• Tile access to a two-dimensional matrix (1 GB)
  with overlap
• Writes/reads sequential chunks of 32 KB, with 2KB
  of overlap
• Perf
• Parallel I/O test program within MPICH
• Writes a 1 MB chunk at a location determined by
  rank, no overlap
• Mandelbrot
• An image processing application that includes
  visualization
• Chunk size is dependent on the number of processes

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RAID Node
Beowulf Cluster
P2-350Mhz
P2-350Mhz
P2-350Mhz
10/100Mb Ethernet Switch
Local PCI-IDE Disk
Local PCI-IDE Disk
P2-350Mhz
P2-350Mhz
P2-350Mhz
RAID Node
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Hardware Specifics

• DAS configuration
• Linux box, Western Digital WD800BB (IDE), 80GB,
  7200RPM
• Beowulf cluster (base configuration)
• Fast Ethernet 100Mbits/sec
• Network Attached RAID - Morstor TF200 with 6-9GB
  drives Seagate SCSI disks, 7200rpm, RAID-5
• Local attached IDE disks IBM UltraATA-350840,
  5400rpm
• Fibre channel disks
• Seagate Cheetah X15 ST-336752FC, 15000rpm

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Write/Read Bandwidth
NPB2.4/BT
SPECHPC/seis
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Write/Read Bandwidth
MPI-Tile
Perf
Mandelbrot
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Profile training sensitivity analysis

• We have found that IO access patterns are
  independent of file-based data values
• When we increase the problem size or reduce the
  number of processes, either
• the number of IOs increases, but access patterns
  and chunk size remain the same (SPEChpc96,
  Mandelbrot), or
• the number of IOs and IO access patterns remain
  the same, but the chunk size increases (NBT,
  Tile-IO, Perf)
• Re-profiling can be avoided

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Execution-driven Parallel I/O Modeling

• Growing need to process large, complex datasets
  in high performance parallel computing
  applications
• Efficient implementation of storage architectures
  can significantly improve system performance
• An accurate simulation environment for users to
  test and evaluate different storage architectures
  and applications

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Execution-driven I/O Modeling

• Target applications parallel scientific programs
  (MPI)
• Target machine/Host machine Beowulf clusters
• Use DiskSim as the underlying disk drive
  simulator
• Direct execution to model CPU and network
  communication
• We execute the real parallel I/O accesses and
  meanwhile, calculate the simulated I/O response
  time

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Validation Synthetic I/O Workload on DAS
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Simulation Framework - NAS
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Simulation Framework SAN direct

• A variety of SAN where disks are distributed
  across the network and each
• server is directly connected to a single device
• File partitioning
• Utilize I/O profiling and data partitioning
  heuristics to distribute portions of
• files to disks close to the processing nodes

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Hardware Specifications
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Publications

• Profile-guided File Partitioning on Beowulf
  Clusters, Journal of Cluster Computing, Special
  Issue on Parallel I/O, to appear 2005.
• Execution-Driven Simulation of Network Storage
  Systems, Proceedings of the 12th ACM/IEEE
  International Symposium on Modeling, Analysis and
  Simulation of Computer and Telecommunication
  Systems (MASCOTS), October 2004, pp. 604-611.
• Profile-Guided I/O Partitioning, Proceedings of
  the 17th ACM International Symposium on
  Supercomputing, June 2003, pp. 252-260.
• Source Level Transformations to Apply I/O Data
  Partitioning, Proceedings of the IEEE Workshop
  on Storage Network Architecture And Parallel IO,
  Oct. 2003, pp. 12-21.
• Profile-Based Characterization and Tuning for
  Subsurface Sensing and Imaging Applications,
  International Journal of Systems, Science and
  Technology, September 2002, pp. 40-55.

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Summary of Cluster-based Work

• Many imaging applications are dominated by
  file-based I/O
• Parallel systems can only be effectively utilized
  if I/O is also parallelized
• Developed a profile-guided approach to I/O data
  partitioning
• Impacting clinical trials at MGH
• Reduced overall execution time by 27-82 over
  MPI-IO
• Execution-driven I/O model is highly accurate and
  provides significant modeling flexibility

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Outline

• Motivation to study file-based I/O
• Profile-driven partitioning for parallel file I/O
• I/O Qualification Laboratory _at_ NU
• Areas for future work

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I/O Qualification Laboratory

• Working with Enterprise Strategy Group
• Develop a state-of-the-art facility to provide
  independent performance qualification of
  Enterprise Storage systems
• Provide a quarterly report to ES customer base on
  the status of current ES offerings
• Work with leading ES vendors to provide them with
  custom early performance evaluation of their beta
  products

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I/O Qualification Laboratory

• Contacted by IOIntegrity and SANGATE for product
  qualification
• Developed potential partners that are leaders in
  the ES field
• Initial proposals already reviewed by IBM,
  Hitachi and other ES vendors
• Looking for initial endorsement from industry

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I/O Qualification Laboratory

• Why _at_ NU
• Track record with industry (EMC, IBM, Sun)
• Experience with benchmarking and IO
  characterization
• Interesting set of applications (medical,
  environmental, etc.)
• Great opportunity to work within the cooperative
  education model

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Outline

• Motivation to study file-based I/O
• Profile-driven partitioning for parallel file I/O
• I/O Qualification Laboratory _at_ NU
• Areas for future work

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Areas for Future Work

• Designing a Peer-to-Peer storage system on a Grid
  system by partitioning datasets across
  geographically distributed storage devices

Head node
Head node
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Areas for Future Work

• Reduce simulation time by identifying
  characteristic phases in I/O workloads
• Apply machine learning algorithms to identify
  clusters of representative I/O behavior
• Utilize K-Means and Multinomial clustering to
  obtain high fidelity in simulation runs utilizing
  sampled I/O behavior

A Multinomial Clustering Model for Fast
Simulation of Architecture Designs, submitted to
the 2005 ACM KDD Conference.