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HPCToolkit Multi-platform Tools for Analyzing
Node Performance
John Mellor-Crummey Robert Fowler Nathan
Tallent Gabriel Marin Department of Computer
Science Rice University
http//hipersoft.cs.rice.edu/hpctoolkit/
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What Makes a Program Fast?

• Good algorithm
• Good data structure
• Efficient code

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Analysis and Tuning Questions

• How can we tell if a program has good
  performance?
• How can we tell that it doesnt?
• If performance is not good, how can we pinpoint
  where?
• How can we tell why?
• What can we do about it?

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What about Parallel Codes?

• Even partitioning of computation
• Minimum communication
• Low communication overhead
• Good parallelism

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Tuning a Parallel Code

• Analyzing parallelism
• Get the parallelism right
• Analyze node performance
• Tune that as well

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A Digression Parallel Line Sweep

• Good parallel performance requires suitable
  partitioning
• Tightly-coupled computations are problematic
• Line-sweep computations ADI integration among
  others

do j 1, n do i 2,n
a(i,j) a(i-1,j)
recurrences make parallelization difficult with
BLOCK partitionings
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Parallelizing Line Sweepswith Block Partitionings
Approach 1 Only compute along local dimensions
Local Sweeps along x and z
Local Sweep along y
Transpose
Transpose back

• Fully parallel computation
• High communication volume transpose ALL data

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Coarse-Grain Pipelining
Approach 2 Compute along partitioned dimensions
Partial serialization induces wavefront
parallelism with block partitioning
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Coarse-Grain Pipelining
Approach 2 Compute along partitioned dimensions
Partial serialization induces wavefront
parallelism with block partitioning
Processor 0
Processor 1
Processor 2
Processor 3
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Multipartitioning

• Style of skewed-cyclic distribution
• Each processor owns a tile between each pair of
  cuts along each distributed dimension

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Multipartitioning

• Enables full parallelism for a sweep along any
  partitioned dimension

Processor 0
Processor 1
Processor 2
Processor 3
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Parallelizing Line Sweeps

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Understanding Node Performance

• The rest of this talk will focus on this topic.

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The Setting Modern Computer Systems

• Microprocessor-based architectures
• Deeply-pipelined processors with internal
  parallelism
• out of order superscalar Alpha
• multiple functional units
• circuitry to dynamically determine dependences
  and dispatch instructions
• many instructions can be in flight at once
• VLIW Itanium
• issue a fixed size bundle of instructions each
  cycle
• bundles tailored to mix of available functional
  units
• compiler pre-determines what instructions execute
  in parallel
• Complex memory hierarchy
• non-blocking, multi-level caches
• TLB

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The Setting Modern Scientific Applications

• Multi-lingual programs
• Many source files
• Complex build process
• Typical
• Multiple directories
• Multiple makefiles
• Incomplete automation
• External libraries in binary-only form

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The Problem Programming Modern Microprocessor
Systems Efficiently

• Architectural sweet spot often differs from
  applications
• Example
• Architecture modest cache sizes, long cache
  lines
• Irregular particle application
• access large amounts of data in irregular access
  pattern
• most of long cache lines go unread
• almost no temporal reuse
• Gap between peak and typical performance is
  growing
• 5-10 of peak is common today
• Gap between processor speed and memory speed is
  growing
• Performance analysis and tuning is necessary!

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Performance Monitoring Hardware

• Purpose
• capture information about performance critical
  details that is otherwise inaccessible
• cycles in flight, TLB misses, mispredicted
  branches, etc
• What it does
• Characterize events and measure durations
• record information about an instruction as it
  executes.
• Two flavors of performance monitoring hardware
• aggregate performance event counters
• sample events during execution cycles, board
  cache misses
• limitation out of order execution smears
  attribution of events
• ProfileMe instruction execution trace hardware
• a set of boolean flags indicating occurrence of
  events (e.g., traps, replays, etc) cycle
  counters
• limitation not all sources of delay are counted,
  attribution is sometimes unintuitive

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Performance Tool Goals

• Support large, multi-lingual applications
• a mix of of Fortran, C, C
• external libraries
• thousands of procedures
• hundreds of thousands of lines
• we must avoid
• manual instrumentation
• significantly altering the build process
• frequent recompilation
• Multi-platform
• Scalable data collection
• Analyze both serial and parallel codes
• Effective presentation of analysis results
• intuitive enough for physicists and engineers to
  use
• detailed enough to meet the needs of compiler
  writers

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HPCToolkit System Overview
application source
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HPCToolkit System Overview
application source
binary object code
compilation
linking
source correlation
profile execution
binary analysis
program structure
hyperlinked database
performance profile
interpret profile
hpcviewer

• launch unmodified, optimized application binaries
• collect statistical profiles of events of interest

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HPCToolkit System Overview

• decode instructions and combine with profile data

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HPCToolkit System Overview

• extract loop nesting information from executables

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HPCToolkit System Overview

• synthesize new metrics by combining metrics
• relate metrics, structure, and program source

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HPCToolkit System Overview

• support top-down analysis with interactive viewer
• analyze results anytime, anywhere

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HPCToolkit System Overview
application source
binary object code
compilation
linking
source correlation
profile execution
binary analysis
program structure
hyperlinked database
performance profile
interpret profile
hpcviewer
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Data Collection

• Support analysis of unmodified, optimized
  binaries
• Inserting code to start, stop and read counters
  has many drawbacks, so dont do it!
• nested measurements skew results
• Use hardware performance monitoring to collect
  statistical profiles of events of interest
• Different platforms have different capabilities
• event-based counters MIPS, IA64, Pentium
• ProfileMe instruction tracing Alpha
• Different capabilities require different
  approaches

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Sample-based Performance Analysis

• Events sampled when
• aggregate performance counter exceeds threshold
• instruction selected for ProfileMe tracing
• Each time a sample occurs
• note the program counter
• record information in a histogram
• Map sampled PC values back to source lines
• Advantages
• provides a high-level view of where events
  happen during execution
• can be started at launch time without prior
  preparation

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Data Collection papirun for Linux

• PAPI Performance API
• interface to hardware performance monitors
• supports many platforms
• papirun open source sample-based profiling
• preload monitoring library before launching
  application
• inspect load map to set up sampling for all load
  modules
• record PC samples for each module along with load
  map
• Linux IA64 and IA32
• papiprof prof-like tool
• output styles
• XML for use with hpcview
• plain text

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Data Collection DCPI and ProfileMe

• Alpha ProfileMe
• EV67 records info about an instruction as it
  executes
• mispredicted branches, memory access replay traps
• more accurate attribution of events
• DCPI (Digital) Continuous Profiling
  Infrastructure
• sample processor counters and instructions
  continuously during execution of all code
• all programs
• shared libraries
• operating system
• support both on-line and off-line data analysis
• to date, we use only off-line analysis

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HPCToolkit System Overview
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Linux papiprof

• prof-like tool for use with papirun
• based on Curtis Janssens vprof
• uses GNU binutils to perform PC ? source mapping
• interpret profiles collected with papirun
• Map counts associated with instruction addresses
  back to (file, function, source line) triples
• output styles
• ascii profile format
• XML-based profile format for use with HPCView

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Metric Synthesis with xprof (Alpha)

• Interpret DCPI samples into useful metrics
• Transform low-level data to higher-level metrics
• DCPI ProfileMe information associated with PC
  values
• project ProfileMe data into useful equivalence
  classes
• decode instruction type info in application
  binary at each PC
• FLOP
• memory operation
• integer operation
• fuse the two kinds of information
• Retired instructions instruction type
• retired FLOPs
• retired integer operations
• retired memory operations
• Map back to source code like papiprof

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HPCToolkit System Overview
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Why Binary Analysis?

• Problems
• Line-level performance statistics may be
  inaccurate, and offer a myopic view of program
  performance
• Interesting performance for scientific programs
  is at the loop level
• Approach
• recover loop information from an application
  binary

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Program Structure Recovery with bloop

• Parse instructions in an executable using GNU
  binutils
• Analyze branches to identify basic blocks
• Construct control flow graph using branch target
  analysis
• be careful with machine conventions and delay
  slots!
• Use interval analysis to identify natural loop
  nests
• Map machine instructions to source lines with
  symbol table
• dependent on accurate debugging information!
• Normalize output to recover source-level view
• Platforms AlphaTru64, MIPSIRIX, LinuxIA64,
  LinuxIA32, SolarisSPARC

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Sample Flowgraph from an Executable

• Loop nesting structure
• blue outermost level
• red loop level 1
• green loop level 2

Observation optimization complicates program
structure!
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Normalizing Program Structure
Constraint each source line must appear at most
once

• Coalesce duplicate lines
• (1) if duplicate lines appear in different loops
• find least common ancestor in scope tree merge
  corresponding loops along the paths to each of
  the duplicates
• purpose re-rolls loops that have been split
• (2) if duplicate lines appear at multiple levels
  in a loop nest
• discard all but the innermost instance
• purpose handles loop-invariant code motion
• apply (1) and (2) repeatedly until a fixed point
  is reached

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Recovered Program Structure

• ltLM n"/apps/smg98/test/smg98"gt
• ...
• ltF n"/apps/smg98/struct_linear_solvers/smg_rel
  ax.c"gt
• ltP n"hypre_SMGRelaxFreeARem"gt
• ltL b"146" e"146"gt
• ltS b"146" e"146"/gt
• lt/Lgt
• lt/Pgt
• ltP n"hypre_SMGRelax"gt
• ltL b"297" e"328"gt
• ltS b"297" e"297"/gt
• ltL b"301" e"328"gt
• ltS b"301" e"301"/gt
• ltL b"318" e"325"gt
• ltS b"318" e"325"/gt
• lt/Lgt
• ltS b"328" e"328"/gt
• lt/Lgt
• ltS b"302" e"302"/gt

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HPCToolkit System Overview
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Data Correlation

• Problem
• any one performance measure provides a myopic
  view
• some measure potential causes (e.g. cache misses)
• some measure effects (e.g. cycles)
• cache misses not always a problem
• event counter attribution is inaccurate for
  out-of-order processors
• Approaches
• multiple metrics for each program line
• computed metrics, e.g. cache miss rate
• eliminate mental arithmetic
• serve as a key for sorting
• hierarchical structure
• line level attribution errors give good
  loop-level information

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HPCToolkit System Overview
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HPCViewer Screenshot
Annotated Source View
Metrics
Navigation
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Flattening for Top Down Analysis

• Problem
• strict hierarchical view of a program is too
  rigid
• want to compare program components at the same
  level as peers
• Solution
• enable a scopes descendants to be flattened to
  compare their children as peers

Current scope
flatten
unflatten
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Using HPCTools Toolkit on Linux
source
a.out
mpiexec papirun -e PAPI_L2_TCM a.out
bloop a.out
Raw profile data
program structure
papiprof
portable profile
...
hpcview
portable profile
XML database
configuration file
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hpcview Configuration File
ltHPCVIEWgt   ltTITLE name"POP 4-way shmem,
model_sizemedium" /gt   ltPATH name"." /gt  
ltPATH name"./compile" /gt   ltPATH
name"../sshmem" /gt   ltPATH name"../source" /gt
- ltMETRIC name"pcc" displayName"Cycles"gt 
ltFILE name"pop.fcy_hwc.pxml" /gt   lt/METRICgt
ltMETRIC name"dc" displayName"L1 miss"gt 
ltFILE name"pop.fdc_hwc.pxml" /gt   lt/METRICgt
ltMETRIC name"dsc" displayName"L2 miss"gt 
ltFILE name"pop.fdsc_hwc.pxml" /gt   lt/METRICgt
ltMETRIC name"fp" displayName"FP insts"gt 
ltFILE name"pop.fgfp_hwc.pxml" /gt   lt/METRICgt
ltMETRIC name"rat" displayName"cy per FLOP"gt-
ltCOMPUTEgt ltmathgt ltapplygt ltdivide/gt
ltcigtpcclt/cigt ltcigtfplt/cigt lt/applygt lt/mathgt
lt/COMPUTEgt  lt/METRICgt  lt/HPCVIEWgt
Heading on Display
Paths to interesting source directories
Metrics defined by Platform Independent Profile
Files
Expression for derived metric
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Some Uses for HPCToolkit

• Identifying unproductive work
• where is the program spending its time not
  performing FLOPS
• Memory hierarchy issues
• bandwidth utilization misses x line size/cycles
• exposed latency ideal vs. measured
• Cross architecture or compiler comparisons
• what program features cause performance
  differences?
• Gap between peak and observed performance
• loop balance vs. machine balance?
• Evaluating load balance in a parallelized code
• how do profiles for different processes compare

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Assessment of HPCToolkit Functionality

• Top down analysis focuses attention where it
  belongs
• sorted views put the important things first
• Integrated browsing interface facilitates
  exploration
• rich network of connections makes navigation
  simple
• Hierarchical, loop-level reporting facilitates
  analysis
• more sensible view when statement-level data is
  imprecise
• Binary analysis handles multi-lingual
  applications and libraries
• succeeds where language and compiler based tools
  cant
• Sample-based profiling, aggregation and derived
  metrics
• reduce manual effort in analysis and tuning cycle
• Multiple metrics provide a better picture of
  performance
• Multi-platform data collection
• Platform independent analysis tool

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csprof/csbuild/csview

• Collect call stack (gprof-like) profiles of
  application binaries without prior arrangement
• no special compiler options or link step
• any compiled language or mix thereof
• Collect call tree annotated with sample counts
  at each node in the tree
• Build call graph using call tree data
• Use viewer for interactive exploration of call
  graph profile data

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Whats Next?

• Research
• collect and present dynamic content
• what path gets us to expensive computations?
• accurate call-graph profiling of unmodified
  executables
• analysis and presentation of dynamic content
• communication in parallel programs
• statistical clustering for analyzing large-scale
  parallelism
• performance diagnosis why rather than what
• Development
• harden toolchain
• new platforms Opteron and PowerPC
• data collection with oprofile on Linux