A Performance Estimator for Parallel Hierarchical Memory Systems -- PetaSIM

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A Performance Estimator for Parallel Hierarchical
Memory Systems -- PetaSIM

• Yuhong Wen and Geoffrey C. Fox
• Northeast Parallel Architecture Center (NPAC)
• Syracuse University
• wen,gcf_at_npac.syr.edu

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Outlines

• Performance Estimation Process
• PetaSIM Motivation and Idea
• Help to design computer architecture and
  applications
• Java applet friendly user interface
• Performance Specification Language Features
• Design and Implementation of PetaSIM
• Experiment Results

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Why Performance Prediction?

• Application complexities
• A lot of processors required
• Large amount of data involved
• Time-consuming processing
• Performance prediction to fasten
• new parallel computer architecture design
• application model design

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Performance Prediction Approaches

• Concept design level performance prediction
• aim to provide a quick and roughly correct
  performance prediction at the early stage of
  model design
• PetaSIM based on this level
• Detailed performance prediction
• to provide detailed information of a given
  application running on specific computer system
  -- Simulation

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PetaSIM Motivation

• PetaSIM was designed to allow qualitative
  performance estimates where in particular the
  design of machine is particularly easy to change
• Applications are to be derived by hand or by
  automatic generation from Maryland Application
  Emulators
• Special attention to support of hierarchical
  memory machines and data intensive applications
• Support simulation of pure data-parallel and
  composition of linked modules

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Peta-Computing Hierarchy
Full Heterogeneous MetaProblem
Module
Module
Module
Components
Components
Module
Module
Task Parallelism
Aggregate
Aggregate
Data Parallelism
Simulate
Loosely Synchronize Computation
Splitting into Lower Level Memory Hierarchy
PetaSIM
Real Computing
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Performance Prediction Model
Application Domain
Software / Operating System Domain
Hardware Domain
Multi Domain model
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Three Domain Performance Prediction

• Application Domain
• to extract the data aggregates
• to give abstract data movement and computation
  behavior
• Software / Operating System Domain
• to provide the methods for task process and
  memory management, communication and parallel
  file access
• Hardware Domain
• to provide the model of processor and memory
  components, includes cache as well

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Performance Specification Language
Because of the complexities of performance
prediction

• Various different kinds of applications
• Different kinds of parallel architectures

Its very important to design a general
performance specification language (PSL) to
represent all the features of the different
aspects in the performance process. PetaSIM
shows an initial step to suggest that
characteristics of such a Performance
Specification Language (PSL).
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Performance Specification Language
Application Domain

• The size of each data block
• the number of data blocks
• the amount of data operations in the data block
• data distribution model
• data processing sequence / flow of the data
  blocks -- the application algorithm

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Performance Specification Language
Software / Operating System Domain

• The memory management approach
• the cache management approach
• parallel task schedule method
• parallel file access pattern
• computing, communication overlap approach

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Performance Specification Language
Hardware Domain

• Computing capability of each processor, which
  include the CPU speed and the bandwidth
• memory size and cache size
• architecture of each processing node
• inter-communication topology of the parallel
  machines, which is to provide the information of
  communication between the processors

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Petasim Estimator Emulator
Applications
Hand Code Applications
UMD Emulators
Execution Script
Dataset Distribution
Nodeset Linkset
PetaSIM
Performance Estimation
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Emulators

• Extract the applications computational and data
  access patterns
• A simplified version of the real application,
  contains all the necessary communication,
  computation and I/O characteristics
• less accurate than full application, but more
  robust
• fast performance prediction for rapid prototyping

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PetaSIM Design

• We define an object structure for computer
  (including network) and data
• Architecture Description
• nodeset linkset
• (describe the architecture memory hierarchy)
• Data Description
• dataset distribution
• Application Description
• execution script
• System / Software Description

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Architecture Description

• A nodeset is a collection of entities with types
  liked
• memory with cache disks
• CPU where results can be calculated
• pathway such as bus, switch or network
• A linkset connects nodesets together in various
  ways

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Application Description

• An application consists of dataset objects
• dataset implementation is controled by the
  distribution objects
• application behavior is represented by execution
  script
• a set of command statements
• data movements
• data computation
• synchronization

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Nodeset Object Structure

• Name one per nodeset object
• type choose from memory, cache, disk, CPU,
  pathway
• number number of members of this nodeset in the
  architecture
• grainsize size in bytes of each member of this
  nodeset (for memory, cache, disk)
• bandwidth maximum bandwidth allowed in any one
  member of this nodeset
• floatspeed CPUs float calculating speed
• calculate() method used by CPU nodeset to
  perform computation
• cacherule controls persistence of data in a
  memory or cache
• portcount number of ports on each member of
  nodeset
• portname ports connected to linkset
• portlink name of linkset connecting to this
  port
• nodeset_member_list list of nodeset members in
  this nodeset (for nodeset member identification)

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Linkset Object Structure

• Name one per linkset object
• type choose from updown, across
• nodesetbegin name of initial nodeset joined by
  this linkset
• nodesetend name of final nodeset joined buy this
  linkset
• topology used for across networks to specify
  linkage between members of a single nodeset
• duplex choose from full or half
• number number of members of this linkset in the
  architecture
• latency time to send zero length message across
  any member of linkset
• bandwidth maximum bandwidth allowed in any link
  of this linkset
• send() method that calculates cost of sending a
  message across the linkset
• distribution name of geometric distribution
  controlling this linkset
• linkset_member_list list of linkset members in
  this linkset ( for linkset member identification )

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Dataset Object Structure

• Name one per dataset object
• choose from grid1dim, grid2dim, grid3dim,
  specifies type of dataset
• bytesperunit number of bytes in each unit
• floatsperunit update cost as a floating point
  arithmetic count
• operationsperunit operations in each unit
• update() method that updates given dataset which
  is contained in a CPU nodeset and a grainsize
  controlled by last memory nodeset visited
• transmit() method that calculates cost of
  transmission of dataset between memory levels
  either communication or movement up and down
  hierarchy
• Methods can use other parameters or be custom

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Execution Script

• Currently a few primitives which stress (unlike
  most languages) movement of data through memory
  hierarchies
• send DATAFAMILY from MEM-LEVEL-L to MEM-LEVEL-K
• These reference object names for data and memory
  nodesets
• move DATAFAMILY from MEM-LEVEL-L to MEM-LEVEL-K
• Use distribution DISTRIBUTION from MEM-LEVEL-L to
  MEM-LEVEL-K
• compute DATAFAMILY-A, DATAFAMILY-B, on
  MEM-LEVEL-L
• synchronize (synchronizes all processors ---
  loosely synchronous barrier)
• loop operation

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PetaSIM Estimation Schedule

• Each nodeset member has its usage control to
  record when dataset arrives and when to send out
  to next nodeset member
• Each linkset member has its usage control to
  record at what time the linkset member is free or
  occupied
• Data Driven model Ready ---gt Go (First come,
  First Service)
• Support Both data parallel mode and individual
  operation on each nodeset, linkset member mode

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Architecture of PetaSIM
C Simulator
Multi-User Java Server
StandardJava AppletClient
StandardJava AppletClient
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PetaSIM Experiments
Typical SP2 configuration of nodeset and linkset
components
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Pathfinder Performance Estimation Results
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Pathfinder Estimation Results II
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Titan Estimation Results (Fixed)
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VMScope Estimation Results
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PetaSIM Features

• Accurate estimation
• Friendly user interface
• Easy to modify the architecture design
• Easy to monitor the effect of the design change
• Fast Estimation
• Detail performance estimation
• Provide detail usage of each individual nodeset
  and linkset member in the memory hierarchy

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Compare with some other Simulators

• Different Simulation Approach
• PetaSIM not real run the application, estimate
  the execution script (operation abstraction)
• PetaSIM running on single processor
• Similar performance estimation results
• PetaSIM can easily deal with different kinds of
  computer architecture
• PetaSIM can get detailed information of any part
  of the architecture

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PetaSIM Current Progress Summary

• Architecture Description (nodeset linkset)
• Application Description (dataset execution
  script)
• Link to Application Emulators
• Jacobi hand-written example
• Pathfinder, Titan, VMScope real applications
  (Generated by UMDs Emulator)
• Easy modified Architecture and Application
  description
• Fast and relatively Accurate performance
  estimation (PetaSIM running on single processor)
• Java applet based user Interface
• Data Parallel Model Individual Control

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Possible Future Work

• Richer set of applications using standard
  benchmarks and DoD MSTAR
• Relate object model to those used in seamless
  interfaces / metacomputing i.e. to efforts to
  establish (distributed) object model for
  computation
• Review very simple execution script -- should we
  add more complex primitives or regard
  application emulators as this complex script
• Binary format (compiled PetaSIM) of
  architecture and application description ( ASCII
  format will make execution script very large)
• Translation tool from ASCII format to binary
  format (to retain the friendly user interface)
• Upgrade performance evaluation model
• Run performance simulation in parallel (i.e.
  PetaSIM running on multi-processors)

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PetaSIM Web-Site URL
http//kopernik.npac.syr.edu4096/petasim/V1.0/Pet
aSIM.html
-- PetaSIM Java Applet front user interface and
demo -- Related PetaSIM documents
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Interface of PetaSIM Client