Talk Final

5/20/2018 Talk Final

1/42

Rethinking Productivity and Performance

for the Exascale Era

Allen D. Malony

Keynote Talk

7thWorkshop on Productivity and Performance (PROPER)*

*

Supported by the Virtual Institute High Productivity Supercomputing (VI-HPS)


2/42

Rethinking Productivity and Performance in the Exascale EraPROPER 2014

Abstract

The push to exascale systems is forcing the parallel computing community to

rethink fundamental notions of productivity and performance. The rapidly

growing degrees of parallelism brought on by manycore processors is just one

aspect of an evolving landscape of architectural, system, and software features

that is increasing the complexity of the application development and

optimization process. It is becoming more apparent that in order to address thecomplexity concerns unfolding in the exascale space, we must think of

productivity and performance in a more connected way and the technology to

support them as being more open, integrated, and intelligent. This talk will

discuss directions for parallel performance research and tools that target the

scalability, optimization, and programmability challenges of next-generation

HPC platforms with high-productivity as an essential outcome.

2


3/42


Outline

! Productivity! Scientific Productivity

! HPC Productivity Factors and Landscape

! Exascale productivity crisis

! Rethinking productivity and performance

! Directions

" Performance knowledge engineering

"

Integration and synthesis (for autotuning)" Dynamic introspection and adaptation

! Conclusions

3


4/42


Productivity a Computing Metric of Merit*

!

Rich measure of quality of the computing experience" Captures key factors that determine overall impact

" Greater productivity, better computing experience! Productivity is strongly related to ease of use

" Less effort for same result in same time

! Expands our notion of computing effectiveness" Focuses attention on important effectiveness contributors" Exposes relationships between

!program development and program execution!time to develop/maintain/ with time to solution

!Productivity unifies usability and performance

" Expresses tradeoff between

!programmability and delivered performance

4

* Courtesy of

Thomas Sterling,

Indiana University


5/42


HPC is about Scientific Productivity

! Scientific productivity is a qualitymeasure of the process of achievingscience results, incorporating:

"Software productivity: developmenteffort, time, maintenance, support

"Execution-time productivity: efficiency,

time, cost to run scientific workloads" Workflow and analysis productivity:

experiment design, results analysis, validation,hypothesis testing

"End-to-end productivity:from science questionsto scientific discovery (i.e., valueof scientific insights)

! Productivity costs" Human resource in development and re-engineering

" Machine and energy resources in runtime (performance)" Utility and correctness of computational results

5


6/42

!"#$%&'%&( *+,-./01%#2 3&- *"+4,+53&/" %& #$" 6738/39" 6+3*!:*6! ; 6Rethinking Productivity and Performance in the Exascale EraPROPER 2014

HPC Productivity Factors

6

!"#$%&'( "* +,"-.' /%&$01234 5261.2. 78


7/42


HPC Productivity Factor Elaboration

7

!"#$%&'( "* +,"-.' /%&$01234 5261.2. 78


8/42


HPC Productivity in Terascale / Petascale Eras

!

Productivity in the terascale era focused on evolving vectorcodes to distributed memory parallelism

" MPI was the major advancement

! Trends in the petascale era rode Moores law and focusedon scalability via large-scale clusters and increasing cores

" Mixed-mode programming with threading (MPI+OpenMP)

" Accelerators for high-throughput (GPUs, manycore)

! Productivity through scalable, evolutionary improvement

" Familiar programming paradigms and environments

" More algorithm sophistication and early runtime coupling

" Development of more robust application libraries

! Performance tools followed along a similar path

" Focused on scalability, robustness, automation,

8


9/42


Performance Trends for Exascale Architectures

! Increasing flopsbecause of corecounts, notclock speed

! Multi-level,massive concurrency

! Declining memory per core

! Tradeoffs in performance,power, and resilience

" Multi-objective

" Hard energy constraints

! Hybrid systems alternatives

9

!"#$%&'( "* 98 :"33&4 ;"%$& ?;>

@!"$&'

A2&$3(B&$C>D9

!0"EF/B&&6

G-.H


10/42


Growing Crisis in Scientific Productivity at Exascale

! HPC is increasingly important in science domains, but HPCapplication development has not advanced as in other fields

! Exascale factors will further affect productivity problems

" Disruptive changes in extreme-scale architectures"New frontiers in modeling, simulation, and analysis of

complex multiscale and multiphysics phenomena! Effects" Significant lag between extreme-scale hardware and

algorithmic innovations and their effective use inapplications

"

Poor support for code coupling of independent components" Lack of agile yet rigorous software engineering practices

for HPC that are both performant and maintainable" Failure to consider the entire lifecycle of large scientific

software efforts, leading to fragile, complicated codes

10


11/42


Exascale Computing Productivity Attention

!

DARPA High Productivity Computing Systems (HPCS)http://en.wikipedia.org/wiki/High_Productivity_Computing_Systems

! Extreme-Scale Scientific Application

Software Productivity: Harnessing the Full

Capacity of Extreme-Scale Computing,

white paper, September 9, 2013.http://www.orau.gov/swproductivity2014/ExtremeScaleScientificApplicationSoftwareProductivity2013.pdf

! Software Productivity for Extreme Scale

Science, DOE ASCR Workshop, January

13-14, 2014.http://www.orau.gov/swproductivity2014/

!

Exascale Computing Systems Productivity,DOE ASCR Workshop, June 3-4, 2014.http://www.orau.gov/ecsproductivity2014/

! ACS Productivity Workshop, DOE Office

of Science, July 2014, Indiana University.

11


12/42


What is Exascale Computing Productivity?

!

Exascale computing productivity is the effective andefficient use of all exascale resources (hardware,application software, runtime, people, processes, energy)in the production of new scientific insights

! Goal

" Productivity awareness embedded in all exascalelifecycle activities from R&D through deployment tooperation and production of scientific insights

" Increase efficiency of overall exascale ecosystem

during research and development by identifying,removing, and ameliorate productivity and

performancebottlenecks

! Sounds good so what is the problem?

12


13/42


Exascale Breaks HPC Programming Paradigm

! Programming model must reflect underlying machine model

! Argues for a more data centric approach in future (not easy)

13


14/42


Difficult Mapping Problems with Exascale

! Multiple levels of mapping to exascale hardware" Mapping decisions affects performance portability

14

;#-&$1E.0

-&%,"6

9$"3$.--12

3

-"6&0

!"-B10&$I

$#2J-&

!"#$%&'( "* =8 /,.0*4 >?;>4 KA!DLG MNOP F&(2"%&


15/42


Multiphysics and Multiscale Science Frontiers

! Single physics simulations are maturing! Coupling of simulations across different types of physics

(multiphysics) and different time and length scales

(multiscale) is becoming increasingly important for

improving model fidelity! Parameters studies and design optimization that employ

ensembles of simulations extend science beyond point

solutions and explore problem space

! Uncertainty quantification and sensitivity analysis! Compounded complexities of code interactions and

collaboration across algorithms and applications

15


16/42


Assumptions of Uniformity are No Longer Valid

!

Assumptions of uniformity are not longer valid" Heterogeneous compute engines

" Fine-grained power management affects homogeneity

"Non-uniformities in process technology cause variation

" Fault resilience introduces inhomogeneity

! Current bulk synchronous model is deficient

" Current focus is on removing sources of performancevariation (jitter) will be increasing impractical

" Huge costs in power/complexity/performance to extend the

life of a purely bulk synchronous model!Embrace performance heterogeneity

" Assume asynchronous execution model as the norm

16


17/42


Exascale Productivity Improvement End-to-End

17

!"#$%&'( "* +,"-.' ;6"#''&QC&R&$4


18/42


Performance Technology Implications

!

Performance technology has evolved to serve the dominantarchitectures and programming models

" Observabilityera (1991 1998)

!instrumentation, measurement, analysis

"Diagnosisera (1998 2007)

!

identifying performance inefficiences

" Complexityera (2008 2012)

!scale, memory hierarchy, network, multicore, GPU

! 20+ years of reasonably stable parallel execution models

" 1stperson application focus

" Productivity somewhat decoupled from performance

!not directly necessary to know application semantics

18


19/42


Exascale Era and Performance Directions

!

Exascale era (2012 ??) is fundamentally different! Productivity and performance are more intimately coupled in

exascale environments

" High productivity high performance applications

" Applications must be mapped to new exascale systems

" Performance awareness is necessary at all levels

" 1stperson (application) + 3rdperson (system resources)performance views necessary

! Directions

" Performance knowledge engineering

" Integration and synthesis (for autotuning)

" Dynamic introspection and adaptation

19


20/42


Extreme Performance (Knowledge) Engineering

Capture all available sources performance data and metadata(collectively knowledge) that can be use to reason aboutapplication performance expectations

Optimization

Measurement / Analysis

Model

Relative

Model

EmpiricalExtreme!scale

Model

Performance

Symbolic

Performance

Expectations

Data

Mining

Expert

System

ProgrammingLanguageModel

Execution Models

Simulation

Insight

Extreme!scale

Application

System

Performance Models

Model

System

Model

Computation

Model

Knowledge Base

Performance

20


21/42


Case Study: MPAS Ocean

!

Use multiscale methods for accurate,efficient, scale-aware earth system models

! MPAS-O uses a variable resolution irregular

mesh of hexagonal grid cells

! Cells assigned to MPI processes" Grouped as blocks

" Each cell has 1-40 vertical layers

! MPAS-O has demonstrated scaling limits

! Look at increasing concurrency

! Focus on role of partitioning

21

l i l I I : ,

l i l l :

i i, l li

l l , l i l

l i i l

i i i l

i i i i i l

ili

l

i

l i l I I ,RBSII-B.'Q6&T831%,#U81"


22/42


MPAS-O Load Imbalance

! Stencil codes introduceload imbalances due tohalo/ghost cells

! Performance measurementin isolution is insufficientto explain source of

imbalance and attribute toperformance factors

!Need to combine:

" Performance measurements" Application metadata

" Correlations betweenmeasurements and metadata

" Visualization of performance data incontext of application domain

22


23/42


Stencil Properties and Metadata Collection

! Capture stencil propertiesin metadata" nCellSolve= cells in partition

" nCell= nCellSolve+ nCellsHalo! Correlate computation balance

with metadata fields! Original partitioning based on

balancing nCellsSolveon MPI processes! Really need to balance with respect to nCell

" This depends on the partitioner, but do not know in

advance!! Apply knowledge to Hindsight partioner

" Create initial Metis partition" Assign nCell weights on graph and iterate to optimize

23

800

900

1000

1100

1200

1300

1400

1500

1.50E+07 2.00E+07 2.50E+07 3.00E+07 3.50E+07 4.00E+07

Metadata Correlated with

dv Timer

nCellsSolve

nCells


24/42


Hindsight Results (Original versus Refined)

24

+"%.0 E&00' V&HB01E1% W,.0"X B&$ U0"EF

+1-& 'B&2%

E"-B#J23

+1-& 'B&2% 12Y95Z[.1%V.0'" $&6#E&6 J-& 12

,.0" $"#J2&'X

;#-U&$ "* 2&13,U"$'B&$ U0"EF

-12SP\]!^]^

-.HS_P`"\\O

-12SO!M-.HS\!a

-12S_8]&\!a8_&\

-.HSM8^&_"M8P&_

-12SM8\&\"a8N&`

-.HSO8a&_"O8^&_

Original Refined240 processors

on NERSC Edison


25/42


Tools/Technology Integration and Synthesis

Performance

Modeling

Reliability

Autotuning

Performance

Optimization

Resilience

Energy

+L7

-B19

9?"#26

LEJT&b.$-"2(

!b1>>

G""c12&

9L95

9A?5>

9/1;%$.E&$

d9+>

Tools /Technologies

G!G+""0F1%

Code analysis

Center of mass for

performance engineerng

GD/A

D$1"

End-to-end

Integration

SciDAC

applications

Prior researchfunding

/79AG 1' . ^Q(&.$ /E1


26/42


Autotuning for Performance Productivity

!

Increasing level of parallelism and heterogeneity inhardware exposes a complex performance landscape

! Can not expect humans or stand-alone tools to be able to

achieve desired performance objectives in the future

" Lack of knowledge and automation (humans)

" Lack of scope and openness (isolated tools)

! Autotuning is promising, but faces two challenges

"Integrationof performance measurement/analysis tools

with code analysis/transformation tools

" Synthesisof information from collective sources and

preservations of knowledge for shared use

?"(+30,& 3&- @2$"8%8 4,+ A.#,53#"- *"+4,+53&/" B.&%&( 26%CA*B ;


27/42


Tools Integration with SUPER Autotuning

! SUPER is applying autotuning to optimize HPC applications" Active Harmony autotuning system (Hollingsworth, UMD)

!software architecture for optimization and adaptation" CHiLL compiler framework (Hall, Utah)

!CPU and GPU code transformations for optimization

" Orio annotation-based autotuning (Norris, ANL)!code transformation with optimization (CUDA, OpenCL)

! Integrate performance tools (TAU) with these frameworks

" Use to gather performance data for autotuning/specialization

" Store performance data with metadata for each experimentvariant and store in performance database (TAUdb)

" Use machine learning and data mining to increase the levelof automation of autotuning and specialization

?"(+30,& 3&- @2$"8%8 4,+ A.#,53#"- *"+4,+53&/" B.&%&( 27A.#,#.&%&( ,4 D*E AFF9%/30,&8


28/42


Orio Empirical-Based Autotuning Process

28?"(+30,& 3&- @2$"8%8 4,+ A.#,53#"- *"+4,+53&/" B.&%&(A.#,#.&%&( ,4 D*E AFF9%/30,&8

Optimized

CodeCUDA

Code withDSL Annotations

DSLParser

CodeTransformations

EmpiricalPerformanceEvaluation

Sequence of (Nested)Annotated Regions

Transfomed Code CodeGenerator

best performing version

TuningSpecification

SearchEngine

Fortran

C

OpenCL

M_


29/42


Orio and TAU Integration

29

Orio Code Generator

Experiment

TAU Metadata Entries

Transformations

Execution TimeWrites

CUPTI callback

measurement library

TAU Profiles

TAUdb

Writes

Uploaded

Links at Runtime

!7

Y&.'#$&-&2%

Y&%$1E B$"e0123 Y&%.6.%. +L76U '%"$.3&

L#%"%#2123 .2.0('1' Y.E,12& 0&.$2123 DBJ-1f.J"2 '&.$E, /B&E1.01f.J"2

?"(+30,& 3&- @2$"8%8 4,+ A.#,53#"- *"+4,+53&/" B.&%&(A.#,#.&%&( ,4 D*E AFF9%/30,&8

D$!7

Ma


30/42


Autotuning Radiation Transport Code

!

Solid fuel ignition solver

! Our goal is to replace two functions which represent alarge proportion of overall execution time and offload

them to the GPU

! The implementations take too long to run to exhaustively

enumerate the search space" Use a Nelder-Mead search

30?"(+30,& 3&- @2$"8%8 4,+ A.#,53#"- *"+4,+53&/" B.&%&(A.#,#.&%&( ,4 D*E AFF9%/30,&8 ]N


31/42


FormJacobian (different sizes, platforms)

?"(+30,& 3&- @2$"8%8 4,+ A.#,53#"- *"+4,+53&/" B.&%&( 31A.#,#.&%&( ,4 D*E AFF9%/30,&8

1,450

1,250

1,300

1,350

1,400

Ge

neratedCodeExecutionTime(milliseconds)

FJ 64x64x64Radeon 7970

Xeon Phi GTX 480

Tesla C2075

Tesla K20c

1,210

1,250

1,300

1,350

1,400

1,450

Ge

neratedCodeExecutionTime(milliseconds)

FJ 75x75x75

1,210

Radeon 7970Xeon Phi

GTX 480

Tesla C2075

Tesla K20c

1,450

1,250

1,300

1,350

1,400

GeneratedCodeExecutio

nTime(milliseconds)

Radeon 7970 Xeon Phi

GTX 480

Tesla C2075Tesla K20c

FJ 100x100x100

1,210 1,210

1,250

1,300

1,350

1,400

1,450

GeneratedCodeExecutio

nTime(milliseconds)

FJ 128x128x128

Radeon 7970Xeon Phi

GTX 480

Tesla C2075

Tesla K20c

+#2123%$.g&E%"$1&'

&HB&$1-&2% %$1.0

&HB&$1-&2% %$1.0&HB&$1-&2% %$1.0

&HB&$1-&2% %$1.0


32/42


Factor Analysis

! Parameter values for the best performing ex14FF andex14FJ kernels across the architectures

" Workgroups, Workitemspergroup, Unrollinner,

Compilerflags, Sizehint, Vechint

32


33/42


TAU and Autotuning in SUPER

TAUdb

OutlinedFunction

Selective InstrumentationFile (specifying parameters

to capture)

InstrumentedVariant

tau_instrumentor

ParameterizedPerformance Profile

execute

PerfDMFTauDB

parametersfrom TauDB

CHiLLRecipes

Search Driver(brute force or Active

Harmony)

code variant

TAUdb

CHiLL

profile dataand metadata

WEKA

decision treeinductionalgorithm

ROSE-basedCode

GenerationTool

Code VariantsCode Variants

Code VariantsCode Variants

Wrapper Function

PerfDMFTauDBTAUdb

Orio Code Generator

Experiment

TAU Metadata Entries

Transformations

Execution TimeWrites

CUPTI callbackmeasurement library

TAU Profiles

TAUdb

Writes

Uploaded

Links at Runtime

!7CHiLL+ AH

Orio

ROSE

Geant4MPAS-O

CESM PerfExplorerhd!O

33


34/42


Synthesis for Automated Performance Tuning

!

Integration of performance measurement and analysiswith autotuning frameworks is important

! However, there is an opportunity for synthesis of 2 types

" Incorporate broader information from all programming,

development, performance, optimization, systems toolsand technologies in the autotuning process

" Preserve performance knowledge and enables higher-

order understanding (learning) of the relationship

between performance factors across tuning dimensions!Need a unified architecture for information synthesis and

performance knowledge preservation

?"(+30,& 3&- @2$"8%8 4,+ A.#,53#"- *"+4,+53&/" B.&%&( 34A.#,#.&%&( ,4 D*E AFF9%/30,&8


35/42


A New Performance Observability

!

Key exascale parallel performance abstraction" Inherent state of exascale execution is dynamic

" Embodies non-stationarity of performance

" Constantly shaped by the adaptation of resources to

meet computational needs and optimize objectives! Requires a fundamentally different performance

observability paradigm

" Designed to support introspective adaptation

" Reflects computation to execution model mapping

" Aware of multiple (performance) objectives

" In-situ analysis of performance state and objectives

35


36/42


Introspection, In Situ Analysis, and Feedback

36

!"#$%&'( Y.$J2 /E,#0f4 >>;> i 91B&$ 9$"g&E%


37/42


XPRESS Project (DOE X-Stack)

! Design and development of exascale software stack to support the

ParalleX execution model" Highly concurrent" Asynchonous

" Message driven" Global address space

! OpenX" XPI programming API

" HPX runtime system" RIOS interface to OS

" APEX performance system

! Team" Universities: IU, LSU, UH, UNC/RENCI, UO

" Laboratories: SNL, LBNL, ORNL

37


38/42


Legacy

Applications

New Model

Applications

MPI

Metaprogramming

FrameworkDomain Specific

Active Library

Compiler

AGASname spaceprocessor

LCOdataflow, futuressynchronization

Lightweight

Threadscontext manager

Parcelsmessage driven

computation

...

OpenMP

XPI

Task recognition Address

space control

Memory bankcontrol

OSthread

Instrumentation

Networkdrivers

Distributed FrameworkOperating System

HardwareArchitecture

OperatingSystem

Instances

RuntimeSystem

Instances

PRIME MEDIUMInterace / Control

{

{

Domain Specific

Language

+106 nodes 103 cores / node + integration network

...

Integrated Software Stack for ParalleX

! OpenX"

XPI programming API" HPX runtime system

" RIOS interface to OS" APEX performance system

! APEX" OS (LXK) tracks system-

level resources" Runtime (HPX) tracks

threads, queues, parcels,remote ops, memory,

concurrency" XPI allows, allow

language-level performancesemantics to be measured

38


39/42


Argo DOE ExaOSR Project

! Exascale OS and runtime research project"

Labs: ANL, LLNL, PNL" Universities: BU, UC, UIUC, UO, UTK

! Philosophy" Whole-system view

!dynamic user environment (functionality, dynamism,flexibility)

!first-class managed resources (performance, power, )

!hierarchical response to faults" Massive concurrency support

! Key ideas

" Hierarchical (control, communication, goals, data resolution)

" Embedded (performance, power, ) feedback and response

" Global system support

39


40/42


Argo Global Information Backplane

!

BEACON: event/action/control notification! Expos: performance observability system

40


41/42


Argo Global Optimization View

# i 3&2&$.01f&6 E"2%$"0 '132.0'T i '&j23' *"$ 2&H% 0&T&0- i B"k&$I$&'101&2E& -"6&0' l -&E,.21'-'

3 i B"k&$4 B&$*"$-.2E& 3".0'* i *&&6U.EF *$"- ?AL!D; .26 Ah9D/m& i &$$"$ U&%k&&2 3".0' .26 *&&6U.EF

41


42/42


Conclusions

!

Exascale brings fundamentally new challenges (plusopportunities!) to performance and to productivity

" ExaFLOPs will require new technology innovations

"New science will require more sophisticated methods

! Performance and productivity are intimately coupled" Achieving scientific productivity requires performance

" Performance can not be considered an afterthought

" Performance depends on integration and synthesis

!

with application development environment

!throughout the exascale software stack

! FLOPs versus Brains

42

Talk Final

Documents

Transcript of Talk Final