orchestration: turning material breakthroughs into ... · orchestration: turning material...

Orchestration: Turning material breakthroughs into application

performance

Jeronimo Castrillon (on behalf of the Orchestration team)

TU Dresden – CfaedChair for Compiler Construction

[email protected]

Orchestration: Materials for performance

© Speaker: J. CastrillonDate: September 21, 2015Page: 2

Cfaed: Research program (revisited)

Goalto explore new technologies for electronic

information processing which overcome the limits of CMOS technology



Goal“to explore new technologies for electronic

information processing which overcome the limits of CMOS technology”

Cfaed: Research program (revisited)

Materials &Functions

Devices &Circuits

Information Processing

CMOS

(industry.focus)

A!!Silicon!Nanowires

B!!Carbon

C!!Organic

D!!BiomolecularAssembly

E!!Chemical

F Orchestration

G Resilience

HHAEC:

Highly Adaptive

ComputingEnergy6Efficient

IBiologicalSystems

Material research for post-CMOS technologies

Systems research to handle heterogeneity

Biology to inspire solutions



Orchestration Path

MissionTurn materials breakthroughs into application

performanceF Orchestration



Orchestration Path

MissionTurn materials breakthroughs into application

performanceF Orchestration

Materials)Inspired Paths (Paths A2– E)

HW/SW%StackHardware

Operating2system

SkeletonsApp.2Algorithms

Databaseapplication

High2PerformanceComputing

Mobile2Communi)cation

Underlying algorithms: self-adjusting

Programming abstractions

Compilers

Formal2Analysis

CoordinationCode synthesis: variant

generation“Micro-kernels”

Heterogeneous “template”

System analysis: modeling, verification &

reasoningCross-layer strategies



Orchestration Path: the team

! Investigators– Prof. Uwe Aßmann– Prof. Franz Baader– Prof. Christel Baier– Prof. Jeronimo Castrillon– Prof. Gerhard Fettweis– Prof. Christof Fetzer– Prof. Jochen Fröhlich

– Prof. Hermann Härtig– Prof. Wolfgang Lehner– Prof. Wolfgang E. Nagel– Dr. Marcus Völp– Prof. Axel Voigt



Orchestration Path: the team (2)

! PhD/Postdocs– Nils Asmußen– Andres Goens– Sebastian Haas– Dirk Habich– Immo Huismann– Tomas Karnagel– Sven Karol– Sascha Klüppelholz– Linda Leuschner

– Matthias Lieber– Siqi Ling– Steffen Märker– Johannes Mey– Benedikt Nöthen– Michael Raitza– Norman Rink– Annett Ungethüm



Outline

! Introduction

! HW/SW stacks

! Orchestration stack

! Outlook

! Concluding remarks



What is a stack

! Software components that form a complete platform (where you can run applications on)– Web app: Operating system, web server, database,

programming language

! Provide means to handle complexity



HW/SW Stack: Example 1

Source:%https://source.android.com/source/index.html

Android%sack



HW/SW Stack: Example 2

Source:%Heterogeneous%System%Architecturehttp://developer.amd.com/resources/heterogeneous=computing/what=is=heterogeneous=system=architecture=hsa/

HSA%Stack



Stacks and wild heterogeneity

! Abstract/hide complexity! Leave as much visible as needed for efficiency

! Emerging techs.: rethink established layers– Languages, compilers, operating systems, …



Example: Compiler – Runtime

Compiler

Runtimeenvironment

“protocol”assumptions

0x0

0xF..F

Code

Data

Heap

Stack

Endurance problem: try not to use same locations that often!



Example: Flash FTL

File%system

Hard%disk

File%system

Flash%drive

Flash%Translation%Layer%(FTL)

Mapping

Wear%leveling



Outline

! Introduction

! HW/SW stacks


! Outlook




Recall: orchestration stack

! Heterogeneous materials– In Cfaed: Along with orchestration

(see other DMA presentations)! Research plan

– Start with heterogeneous CMOS– Tiled for scalability, integrability– Pilot projects with materials as

they become availableMaterials)Inspired Paths (Paths A2– E)

HardwareOperating2system

SkeletonsApp.2Algorithms

Databaseapplication



Compilers

Formal2Analysis

Coordination



Applications/Drivers

! Mobile communication: typically run on heterogeneous

! Data-bases: benefits of heterogeneity (see below)! HPC: high level languages, abstractions for

scalability, productivity and portability– Adaptive Finite Element Methods (FEM)– Computational Fluid Dynamics (CFD)

HardwareOperating% system

SkeletonsApp.%Algorithms

Applications

Compilers

Formal%Analysis

Coordination



Applications/Drivers (2)

! Adaptive Finite Element Methods (FEM)– Working with multiple-meshes – Communication across subdomains– Requires repartitioning " load balancing

– Working on! Portable templatized library! First results on heterogeneous platforms



Applications

Compilers

Formal%Analysis

Coordination

Dendritic%growth[Witkowski15]



Applications/Drivers (3)

! Computational Fluid Dynamics (CFD)! Large applications (1010 unknowns)! Communication/computation is key

! Working on ! Heterogeneous implementation! More versatile fundamental concepts



Applications

Compilers

Formal%Analysis

Coordination

[Huismann15]



Hardware

! Tomahawk: heterogeneous CMOS

CM/µK

…

…

…



Applications

Compilers

Formal%Analysis

Coordination

! Tiled (with NoC) for scalability! Allows to integrate wildly

heterogeneous components! CoreManager (CM): HW

support for fine-grained task dispatching

[Arnold13]



Hardware

! DB-processor for sorting algorithms HardwareOperating% system


Applications

Compilers

Formal%Analysis

Coordination

Selectivity:

[Arnold14]



Operating Systems

! Microkernel for heterogeneity– Isolation (at NoC level)

! Dynamic compartments! Isolation at network boundary

– Remote control: no need for OS everywhere! Simplify integration of novel materials! OS services on remote cores



Applications

Compilers

Formal%Analysis

Coordination



Operating Systems

! Microkernel for heterogeneity

Isolation & remote control



Applications

Compilers

Formal%Analysis

Coordination

CM/ µK

…

…

…App22

App21

File2System

[Asmussen15]



Compilers

! Parallel dataflow programming languages and compilers



Applications

Compilers

Formal%Analysis

Coordination

[Castrillon14]

__PNkpn AudioAmp __PNin(short A[2]) __PNout(short B[2]) __PNparam(short boost){

while (1)__PNin(A) __PNout(B) {for (int i = 0; i < 2; i++)B[i] = A[i]*boost;

}}__PNprocess Amp1 = AudioAmp __PNin(C) __PNout(F) __PNparam(3);__PNprocess Amp2 = AudioAmp __PNin(D) __PNout(G) __PNparam(10);



Compilers

! SW synthesis for Tomahawk– Static/dynamic mapping– Automatic code generation

P1

P2

P6P3

P4

P5

CM/ µK



Applications

Compilers

Formal%Analysis

Coordination



Skeletons

! Basic building blocks for parallelism! “Heterogeneous” programming

– Different possible implementations of same principles (CUDA, Fortran, …)

– Advanced “pre-compiler” to generate and manage variants

" Productivity



Applications

Compilers

Formal%Analysis

Coordination



Skeletons

! Basic building blocks for parallelism HardwareOperating% system


Applications

Compilers

Formal%Analysis

Coordination

Sequential*Program

do*concurrent*(i =*1:n,*j =*1:m)

mat(i,j))=)x), y(i,j,k))*)2end*do

1

!$acc*loop*private(#PRIVATE_VARS#)

#INNER#

!$acc*end*do

Style*Definitions

Style:*OpenACCType:*parallelization

AttributeLoop.private_vars()

Fragment Target:DoConcurrent

2

RECIPE*{parallelization:OpenACC}

OpenMP

Style*Application**Recipe2

Parallel*Program

!$acc*parallel

!$acc*loop*private(i,*j)

do*k*=*1,*num_ele

do*i*=*0,*po

mat(i,j))=)x), y(i,j,k))*)2end*do

end*do

!$acc*end*loop

!$acc*end*parallel

3

[Karol15]



Algorithms

! Raise the level of abstraction– High-level domain specific languages (more later)– Algorithmic transformations: more optimization room– Algorithmic specification: allow for adaptability



Applications

Compilers

Formal%Analysis

Coordination



Formal analysis

! Probabilistic model checking (PMC)– Analyze approaches across layers of the stack– Work on models for heterogeneous systems– Examples:

! Probability of finishing N tasks in T time! Probability of finishing N tasks with energy budget B! Trade-off analysis cost-utility (energy vs. performance)

– Model protocols: Spinlock, barriers, eBond, …



Applications

Compilers

Formal%Analysis

Coordination



Formal analysis

! Probabilistic model checking (PMC) HardwareOperating% system


Applications

Compilers

Formal%Analysis

Coordination

1.3$

1.5$

1.7$

1.9$

2.1$

2.3$

2.5$

2.7$

2.9$

200$ 300$ 400$ 500$ 600$ 700$ 800$ 900$ 1000$ 1100$ 1200$ 1300$ 1400$ 1500$ 1600$ 1700$ 1800$ 1900$ 2000$ 2100$ 2200$ 2300$ 2400$ 2500$ 2600$ 2700$ 2800$ 2900$ 3000$ 3100$ 3200$ 3300$ 3400$ 3500$ 3600$ 3700$ 3800$ 3900$ 4000$ 4100$ 4200$ 4300$ 4400$ 4500$ 4600$ 4700$ 4800$ 4900$ 5000$ 5100$ 5200$ 5300$ 5400$ 5500$ 5600$ 5700$ 5800$ 5900$ 6000$ 6100$ 6200$ 6300$ 6400$ 6500$ 6600$ 6700$ 6800$ 6900$ 7000$ 7100$ 7200$

1.3$

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7.3$

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$

minim

al&expected&en

ergy&[W

a2s]

maximal&required&eBOND+&bandwidth&[MBit/s]Minimal%expected%energy

Maximal%required%bandwidth

eBond

[Baier14]



Outline

! Introduction

! HW/SW stacks


! Outlook




Current and future work

! Further work on languages and methodologies– Computational biology– Chemical information processing

! Technology analysis at system-level– Pros (USPs) and cons– Attempt skip incremental improvement – System-level benefit based on assumptions



Language for computational biology

! Particle and mesh-based Simulations– Discrete models of physical phenomena

! Goal: Abstract programming language + optimization for heterogeneous HPC-clusters

Particle0Mesh DSL Analysis Static

Opt.Runtimeopt.

[Karol15]



Language for computational biologyclient grayscottinteger, dimension(6) :: bcdef = …real(..), dimension(:,:), pointer :: displaceinteger :: interval

add_arg(k_rate,<#real(mk)#>,1.0_mk,0.0_mk,'k_rate',’’)add_arg(F,<#real(mk)#>,1.0_mk,0.0_mk,'F_param',’')add_arg(D_u,<#real(mk)#>,1.0_mk,0.0_mk,'Du_param',’’)add_arg(D_v,<#real(mk)#>,1.0_mk,0.0_mk,'Dv_param',’')

ppm_init(1)

U = create_field(1, "U")V = create_field(1, "V”)topo = create_topology(bcdef)c = create_particles(topo)

allocate(displace(ppm_dim,c%Npart))call random_number(displace)call c%move(displace, info)call c%apply_bc(info)

global_mapping(c, topo)discretize(U,c)discretize(V,c)ghost_mapping(c)

foreach p in particles(c) with … sca_fields(U,V)U_p = 1.0_mkV_p = 0.0_mkif (((x_p(1)-0.5)**2+(x_p(2)-0.5_mk)**2).lt.0.01)

thenU_p = 0.5_mk + 0.01_mk*noiseV_p = 0.25_mk + 0.01_mk*noise

end ifend foreach

n = create_neighlist(c,cutoff=<#4._mk * c%h_avg#>)Lap = define_op(2, [2,0, 0,2], [1.0_mk, 1.0_mk], "Lap")

W = discretize_op(Lap, c, ppm_param_op_dcpse, [order=>2,c=>1.0_mk])

o,nstag = create_ode([U,V],grayscott_rhs,[U=>c,V], rk4)interval = 1 t = timeloop()

do istage=1,nstagghost_mapping(c)ode_step(o, t, time_step, 1)

end doprint([U=>c, V=>c],interval )

end timeloop

ppm_finalize()end client

rhs grayscott_rhs(U=>parts,V)get_fields(dU,dV)

dU = apply_op(W, U)dV = apply_op(W, V)

foreach p in particles(parts) with sca_fields(U,V,dU,dV)dU_p = D_u*dU_p - U_p*(V_p**2) + F*(1.0_mk-U_p)dV_p = D_v*dV_p + U_p*(V_p**2) - (F+k_rate)*V_p

end foreach

end rhs

[Karol15]



SiNW: Components and reconfigurability

! Edge of SiNW over other technologies– P/N reconfigurability– Multi-gate devices (with low latency penalty)

! Components based on assumptions– New uArchitectures?– New pipeline structures and compiler

optimizations?



Outline

! Introduction

! HW/SW stacks


! Outlook




Summary

Materials)Inspired Paths (Paths A2– E)

Hardware

Operating2system

Skeletons

App.2Algorithms

Databaseapplication



Compilers

Formal2Analysis

Coordination

! Orchestration path: Multi-disciplinary effort to turn materials breakthroughsinto application performance

! HW/SW stack to handle heterogeneity– Using CMOS as vehicle– Prepare for “wildly” heterogeneous systems

! Interact with material experts, understand technologies and analyze at system-level



Thanks!

Retreat:%Orchestration%and%resilience%paths%(03.2015)



References[Witkowski15] Witkowski, T., Ling, S., Praetorius, S., & Voigt, A. (2015). Software concepts and numerical algorithms for a scalable adaptive parallel finite element method. Advances in Computational Mathematics, 1-33[Huismann15] Huismann, I., Stiller, J., & Fröhlich, J. (2015). Two-level parallelization of a fluid mechanics algorithm exploiting hardware heterogenity. Computers & Fluids[Arnold13] Arnold, O., Matus, E., Noethen, B., Winter, M., Limberg, T., & Fettweis, G. (2014). Tomahawk: Parallelism and heterogeneity in communications signal processing MPSoCs. ACM Transactions on Embedded Computing Systems (TECS), 13(3s), 107[Arnold14] Arnold, O., Haas, S., Fettweis, G., Schlegel, B., Kissinger, T., Karnagel, T., & Lehner, W. (2014). HASHI: An Application-Specific Instruction Set Extension for Hashing. ADMS@ VLDB, 25-33[Asmussen15] Asmussen, N., Volp, M., Nothen, B., & Ungethum, A. (2015, April). Demo abstract: Taming many heterogeneous cores. In Real-Time and Embedded Technology and Applications Symposium (RTAS), 2015 IEEE (pp. 329-329)[Castrillon14] J. Castrillon and R. Leupers, Programming Heterogeneous MPSoCs: Tool Flows to Close the Software Productivity Gap. Springer, 2014[Karol15] Sven Karol, Well-Formed and Scalable Invasive Software Composition. PhD thesis. May 2015[Baier14] Baier, C., Dubslaff, C., & Klüppelholz, S. (2014, July). Trade-off analysis meets probabilistic model checking. In Proceedings of the Joint Meeting of the Twenty-Third EACSL Annual Conference on Computer Science Logic (CSL) and the Twenty-Ninth Annual ACM/IEEE Symposium on Logic in Computer Science (LICS)[Karol15] S. Karol, P. Incardona, Y. Afshar, I. Sbalzarini, and J. Castrillon. Towards a Next-Generation Parallel Particle-Mesh Language, in Domain-Specific Language Design and Implementation (DSLDI). July 2015

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