11

Multicore SALSAParallel Computing and Web 2.0

for Cheminformatics and GIS Analysis2007 Microsoft eScience Workshop at RENCI

The Friday Center for Continuing Education UNC - Chapel HillOctober 22 2007

Geoffrey Fox, Seung-Hee Bae, Neil Devadasan, Rajarshi Guha, Marlon Pierce, Xiaohong Qiu, David Wild, Huapeng Yuan

Community Grids Laboratory, Research Computing UITS, School of informatics and POLIS Center Indiana University

George Chrysanthakopoulos, Henrik Frystyk NielsenMicrosoft Research, Redmond WA

http://www.infomall.org/multicore gcf@indiana.edu, http://www.infomall.org

Too much Computing? Historically one has tried to increase computing capabilities by

• Optimizing performance of codes

• Exploiting all possible CPU’s such as Graphics co-processors and “idle cycles”

• Making central computers available such as NSF/DoE/DoD supercomputer networks

Next Crisis in technology area will be the opposite problem – commodity chips will be 32-128way parallel in 5 years time and we currently have no idea how to use them – especially on clients

• Only 2 releases of standard software (e.g. Office) in this time span

Gaming and Generalized decision support (data mining) are two obvious ways of using these cycles

• Intel RMS analysis

• Note even cell phones will be multicore

Intel’s Projection

Too much Data to the Rescue? Multicore servers have clear “universal parallelism” as many

users can access and use machines simultaneously Maybe also need application parallelism as needed on client

machines Over next years, we will be submerged of course in data

deluge• Scientific observations for e-Science• Local (video, environmental) sensors• Data fetched from Internet defining users interests

Maybe data-mining of this “too much data” will use up the “too much computing” both for science and commodity PC’s• PC will use this data(-mining) to be intelligent user

assistant?• Must have highly parallel algorithms

Intel’s Application Stack

CICC Chemical Informatics and Cyberinfrastructure Collaboratory Web Service Infrastructure

Portal ServicesRSS FeedsUser ProfilesCollaboration as in Sakai

Core Grid ServicesService RegistryJob Submission and Management

Local ClustersIU Big Red, TeraGrid, Open Science Grid

Varuna.netQuantum Chemistry

Statistics Services Database Services

Core functionality Computation functionality 3D structures byFingerprints Regression CIDSimilarity Classification SMARTSDescriptors Clustering 3D Similarity2D diagrams Sampling distributionsFile format conversion

Docking scores/poses byApplications Applications CID

Docking Predictive models SMARTSFiltering Feature selection Protein

2D plots Docking scoresToxicity predictions

Anti-cancer activity predictionsCID, SMARTS

Cheminformatics Services

DruglikenessArbitrary R code (PkCell)

Mutagenecity predictionsPubChem related data by

Pharmacokinetic parametersOSCAR Document AnalysisInChI Generation/SearchComputational Chemistry (Gamess, Jaguar etc.)

Need to make all this parallel

Deterministic Annealing for Data Mining We are looking at deterministic annealing algorithms because

although heuristic• They have clear scalable parallelism (e.g. use parallel BLAS)• They avoid (some) local minima and regularize ill defined

problems in an intuitively clear fashion• They are fast (no Monte Carlo)• I understand them and Google Scholar likes them

Developed first by Durbin as Elastic Net for TSP Extended by Rose (my student then; now at UCSB)) and Gurewitz

(visitor to C3P) at Caltech for signal processing and applied later to many optimization and supervised and unsupervised learning methods.

See K. Rose, "Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems," Proceedings of the IEEE, vol. 80, pp. 2210-2239, November 1998

High Level Theory Deterministic Annealing can be looked at from a Physics,

Statistics and/or Information theoretic point of view Consider a function (e.g. a likelihood) L({y}) that we

want to operate on (e.g. maximize)

Set L ({y},T) = L({y}) exp(- ({y} - {y})2 /T ) d{y}• Incorporating entropy term ensuring that one looks for most

likely states at temperature T• If {y} is a distance, replacing L by L corresponds to smearing

or smoothing it over resolution T Minimize Free Energy F = -Ln L ({y},T) rather than

energy E = -Ln L ({y}) • Use mean field approximation to avoid Monte Carlo

(simulated annealing)

Deterministic Annealing for Clustering I

Illustrating similarity between clustering and Gaussian mixtures Deterministic annealing for mixtures replaces by

and anneals down to mixture size

))2/(),(exp()(

with centers)(K MixtureGaussian Simple Compare

))(lnEnergy Free

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where)(/)/),(exp()Pr(

CentersCluster and Points

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Deterministic Annealing for Clustering II

This is an extended K-means algorithm Start with a single cluster giving as solution y1 as centroid For some annealing schedule for T, iterate above algorithm testing

correlation matrix in xi about each cluster center to see if “elongated” Split cluster if elongation “long enough”; splitting is a phase

transition in physics view You do not need to assume number of clusters but rather a final

resolution T or equivalent At T=0, uninteresting solution is N clusters; one at each point xi

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Minimum evolving as temperature decreases Movement at fixed temperature going to local

minima if not initialized “correctly

Solve Linear Equations for each temperature

Nonlinearity removed by approximating with solution at previous higher temperature

DeterministicAnnealing

F({y}, T)

Configuration {y}

Clustering Data Cheminformatics was tested successfully with small datasets and

compared to commercial tools Cluster on properties of chemicals from high throughput

screening results to chemical properties (structure, molecular weight etc.)

Applying to PubChem (and commercial databases) that have 6-20 million compounds• Comparing traditional fingerprint (binary properties) with real-valued

properties GIS uses publicly available Census data; in particular the 2000

Census aggregated in 200,000 Census Blocks covering Indiana• 100MB of data

Initial clustering done on simple attributes given in this data• Total population and number of Asian, Hispanic and Renters

Working with POLIS Center at Indianapolis on clustering of SAVI (Social Assets and Vulnerabilities Indicators) attributes at http://www.savi.org) for community and decision makers• Economy, Loans, Crime, Religion etc.

Where are we? We have deterministically annealed clustering running well on 8-

core (2-processor quad core) Intel systems using C# and Microsoft Robotics Studio CCR/DSS

Could also run on multicore-based parallel machines but didn’t do this (is there a large Windows quad core cluster on TeraGrid?)• This would also be efficient on large problems

Applied to Geographical Information Systems (GIS) and census data• Could be an interesting application on future broadly deployed PC’s• Visualize nicely on Google Maps (and presumably Microsoft Virtual Earth)

Applied to several Cheminformatics problems and have parallel efficiency but visualization harder as in 150-1024 (or more) dimensions

Will develop a family of such parallel annealing data-mining tools where basic approach known for• Clustering• Gaussian Mixtures (Expectation Maximization)• and possibly Hidden Markov Methods

Clustering algorithm annealing by decreasing distance scale and gradually finds more clusters as resolution improvedHere we see 10 clusters increasing to 30 as algorithm progresses

Renters

Total

Asian

Hispanic

Renters

IUB

Purdue

10 Clusters

Total

Asian

Hispanic

Renters

30 Clusters

In detail, different groups have different cluster centers

Multicore SALSA at CGL Service Aggregated Linked Sequential Activities

• http://www.infomall.org/multicore Aims to link parallel and distributed (Grid) computing by

developing parallel applications as services and not as programs or libraries• Improve traditionally poor parallel programming development

environments Can use messaging to link parallel and Grid services but

performance – functionality tradeoffs different• Parallelism needs few µs latency for message latency and thread

spawning

• Network overheads in Grid 10-100’s µs This presentation describes first of set of services (library)

of multicore parallel data mining algorithms

Parallel Programming Model If multicore technology is to succeed, mere mortals must be able to build

effective parallel programs There are interesting new developments – especially the Darpa HPCS

Languages X10, Chapel and Fortress However if mortals are to program the 64-256 core chips expected in 5-7

years, then we must use today’s technology and we must make it easy• This rules out radical new approaches such as new languages

The important applications are not scientific computing but most of the algorithms needed are similar to those explored in scientific parallel computing• Intel RMS analysis

We can divide problem into two parts:• High Performance scalable (in number of cores) parallel kernels or

libraries• Composition of kernels into complete applications

We currently assume that the kernels of the scalable parallel algorithms/applications/libraries will be built by experts with a

Broader group of programmers (mere mortals) composing library members into complete applications.

Scalable Parallel Components There are no agreed high-level programming environments for

building library members that are broadly applicable. However lower level approaches where experts define

parallelism explicitly are available and have clear performance models.

These include MPI for messaging or just locks within a single shared memory.

There are several patterns to support here including the collective synchronization of MPI, dynamic irregular thread parallelism needed in search algorithms, and more specialized cases like discrete event simulation.

We use Microsoft CCR http://msdn.microsoft.com/robotics/ as it supports both MPI and dynamic threading style of parallelism

Composition of Parallel Components The composition step has many excellent solutions as this does not

have the same drastic synchronization and correctness constraints as for scalable kernels• Unlike kernel step which has no very good solutions

Task parallelism in languages such as C++, C#, Java and Fortran90; General scripting languages like PHP Perl Python Domain specific environments like Matlab and Mathematica Functional Languages like MapReduce, F# HeNCE, AVS and Khoros from the past and CCA from DoE Web Service/Grid Workflow like Taverna, Kepler, InforSense KDE,

Pipeline Pilot (from SciTegic) and the LEAD environment built at Indiana University.

Web solutions like Mash-ups and DSS Many scientific applications use MPI for the coarse grain composition

as well as fine grain parallelism but this doesn’t seem elegant The new languages from Darpa’s HPCS program support task

parallelism (composition of parallel components) decoupling composition and scalable parallelism will remain popular and must be supported.

“Service Aggregation” in SALSA Kernels and Composition must be supported both inside

chips (the multicore problem) and between machines in clusters (the traditional parallel computing problem) or Grids.

The scalable parallelism (kernel) problem is typically only interesting on true parallel computers as the algorithms require low communication latency.

However composition is similar in both parallel and distributed scenarios and it seems useful to allow the use of Grid and Web 2.0 composition tools for the parallel problem. • This should allow parallel computing to exploit large

investment in service programming environments Thus in SALSA we express parallel kernels not as traditional

libraries but as (some variant of) services so they can be used by non expert programmers

For parallelism expressed in CCR, DSS represents the natural service (composition) model.

Inside the SALSA Services We generalize the well known CSP (Communicating

Sequential Processes) of Hoare to describe the low level approaches to fine grain parallelism as “Linked Sequential Activities” in SALSA.

We use term “activities” in SALSA to allow one to build services from either threads, processes (usual MPI choice) or even just other services.

We choose term “linkage” in SALSA to denote the different ways of synchronizing the parallel activities that may involve shared memory rather than some form of messaging or communication.

There are several engineering and research issues for SALSA• There is the critical communication optimization

problem area for communication inside chips, clusters and Grids.

• We need to discuss what we mean by services

23

Microsoft CCR• Supports exchange of messages between threads using named

ports• FromHandler: Spawn threads without reading ports• Receive: Each handler reads one item from a single port• MultipleItemReceive: Each handler reads a prescribed number of

items of a given type from a given port. Note items in a port can be general structures but all must have same type.

• MultiplePortReceive: Each handler reads a one item of a given type from multiple ports.

• JoinedReceive: Each handler reads one item from each of two ports. The items can be of different type.

• Choice: Execute a choice of two or more port-handler pairings• Interleave: Consists of a set of arbiters (port -- handler pairs) of 3

types that are Concurrent, Exclusive or Teardown (called at end for clean up). Concurrent arbiters are run concurrently but exclusive handlers are

• http://msdn.microsoft.com/robotics/

MPI Exchange Latency in µs (20-30 µs computation between messaging)

Machine OS Runtime Grains Parallelism MPI Exchange Latency

Intel8c:gf12

(8 core 2.33 Ghz)

(in 2 chips)

Redhat MPJE (Java) Process 8 181

MPICH2 (C) Process 8 40.0

MPICH2: Fast Process 8 39.3

Nemesis Process 8 4.21

Intel8c:gf20

(8 core 2.33 Ghz)

Fedora MPJE Process 8 157

mpiJava Process 8 111

MPICH2 Process 8 64.2

Intel8b

(8 core 2.66 Ghz)

Vista MPJE Process 8 170

Fedora MPJE Process 8 142

Fedora mpiJava Process 8 100

Vista CCR (C#) Thread 8 20.2

AMD4

(4 core 2.19 Ghz)

XP MPJE Process 4 185

Redhat MPJE Process 4 152

mpiJava Process 4 99.4

MPICH2 Process 4 39.3

XP CCR Thread 4 16.3

Intel4 (4 core 2.8 Ghz) XP CCR Thread 4 25.8

Preliminary Results• Parallel Deterministic Annealing Clustering in

C# with speed-up of 7.8 (Chemistry) and 7 (GIS) on Intel 2 quad core systems

• Analysis of performance of Java, C, C# in MPI and dynamic threading with XP, Vista, Windows Server, Fedora, Redhat on Intel/AMD systems

• Study of cache effects coming with MPI thread-based parallelism

• Study of execution time fluctuations in Windows (limiting speed-up to < 8)

DSS as Service Model

• We view system as a collection of services – in this case– One to supply data– One to run parallel clustering– One to visualize results – in this by spawning a

Google maps browser– Note we are clustering Indiana census data

• DSS is convenient as built on CCR• Messaging overhead around 30-40 µs

Parallel Multicore GISDeterministic Annealing Clustering

0

0.05

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Parallel Overheadon 8 Threads Intel 8b

Speedup = 8/(1+Overhead)

10000/(Grain Size n = points per core)

Overhead = Constant1 + Constant2/n

Constant1 = 0.02 to 0.1 (Client Windows) due to threadruntime fluctuations

10 Clusters

20 Clusters

Parallel Multicore Deterministic Annealing Clustering

0.000

0.050

0.100

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#cluster

over

head

“Constant1”

Increasing number of clusters decreases communication/memory bandwidth overheads

Parallel Overhead for large (2M points) Indiana Census clustering on 8 Threads Intel 8bThis fluctuating overhead due to 5-10% runtime fluctuations between threads

Parallel Multicore Deterministic Annealing Clustering

“Constant1”

Increasing number of clusters decreases communication/memory bandwidth overheads

Parallel Overhead for subset of PubChem clustering on 8 Threads (Intel 8b)

The fluctuating overhead is reduced to 2% (under investigation!)40,000 points with 1052 binary properties (Census is 2 real valued properties)

250

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0 10 20 30 40 50 60 70 80 90

Number of processors

Ru

nti

me

(sec

on

ds)

Minsize 1 Minsize 100 Minsize 1000

MPI Parallel Divkmeans clustering of PubChem

AVIDD Linux cluster, 5,273,852 structures (Pubchem compound collection, Nov 2005)

min_size ncpus wall_mins walltime1 20 676 11:16:061 40 444 7:24:241 60 379 6:18:411 80 353 5:53:00

100 20 462 7:41:58100 40 356 5:56:01100 40 356 5:55:47100 60 339 5:38:44100 80 337 5:36:53

1000 20 513 8:32:391000 40 376 6:16:251000 60 346 5:46:221000 80 346 5:45:40

Scaled Speed up Tests• The full clustering algorithm involves different values of the

number of clusters NC as computation progresses• The amount of computation per data point is proportional to NC

and so overhead due to memory bandwidth (cache misses) declines as NC increases

• We did a set of tests on the clustering kernel with fixed NC

• Further we adopted the scaled speed-up approach looking at the performance as a function of number of parallel threads with constant number of data points assigned to each thread– This contrasts with fixed problem size scenario where the number of

data points per thread is inversely proportional to number of threads• We plot Run time for same workload per thread divided by

number of data points multiplied by number of clusters multiped by time at smallest data set (10,000 data points per thread)

• Expect this normalized run time to be independent of number of threads if not for parallel and memory bandwidth overheads– It will decrease as NC increases as number of computations per points

fetched from memory increases proportional to NC

Intel 8-core C# with 80 Clusters: Vista Run Time Fluctuations for Clustering Kernel

• 2 Quadcore Processors

• This is average of standard deviation of run time of the 8 threads between messaging synchronization points

Number of Threads

Standard Deviation/Run Time

Intel 8 core with 80 Clusters: Redhat Run Time Fluctuations for Clustering Kernel

• This is average of standard deviation of run time of the 8 threads between messaging synchronization points

Number of Threads

Standard Deviation/Run Time

Basic Performance of CCR

CCR Overhead for a computation of 23.76 µs between messaging

Rendezvous

Intel8b: 8 Core Number of Parallel Computations

(μs) 1 2 3 4 7 8

Spawned

Pipeline 1.58 2.44 3 2.94 4.5 5.06

Shift 2.42 3.2 3.38 5.26 5.14

Two Shifts 4.94 5.9 6.84 14.32 19.44

MPI

Pipeline 2.48 3.96 4.52 5.78 6.82 7.18

Shift 4.46 6.42 5.86 10.86 11.74

Exchange As Two Shifts

7.4 11.64 14.16 31.86 35.62

Exchange 6.94 11.22 13.3 18.78 20.16

Overhead (latency) of AMD4 PC with 4 execution threads on MPI style Rendezvous Messaging for Shift and Exchange implemented either as two shifts or as custom CCR pattern

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AMD Exch

AMD Exch as 2 Shifts

AMD Shift

Stages (millions)

Time Microseconds

Overhead (latency) of Intel8b PC with 8 execution threads on MPI style Rendezvous Messaging for Shift and Exchange implemented either as two shifts or as custom CCR pattern

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Intel Exch

Intel Exch as 2 Shifts

Intel Shift

Stages (millions)

Time Microseconds

Cache Line Interference

Cache Line Interference• Early implementations of our clustering algorithm

showed large fluctuations due to the cache line interference effect discussed here and on next slide in a simple case

• We have one thread on each core each calculating a sum of same complexity storing result in a common array A with different cores using different array locations

• Thread i stores sum in A(i) is separation 1 – no variable access interference but cache line interference

• Thread i stores sum in A(X*i) is separation X

• Serious degradation if X < 8 (64 bytes) with Windows– Note A is a double (8 bytes)– Less interference effect with Linux – especially Red Hat

Time µs versus Thread Array Separation (unit is 8 bytes)

1 4 8 1024 Machine

OS

Run Time Mean Std/

Mean Mean Std/

Mean Mean Std/

Mean Mean Std/

Mean Intel8b Vista C# CCR 8.03 .029 3.04 .059 0.884 .0051 0.884 .0069 Intel8b Vista C# Locks 13.0 .0095 3.08 .0028 0.883 .0043 0.883 .0036 Intel8b Vista C 13.4 .0047 1.69 .0026 0.66 .029 0.659 .0057 Intel8b Fedora C 1.50 .01 0.69 .21 0.307 .0045 0.307 .016 Intel8a XP CCR C# 10.6 .033 4.16 .041 1.27 .051 1.43 .049 Intel8a XP Locks C# 16.6 .016 4.31 .0067 1.27 .066 1.27 .054 Intel8a XP C 16.9 .0016 2.27 .0042 0.946 .056 0.946 .058 Intel8c Red Hat C 0.441 .0035 0.423 .0031 0.423 .0030 0.423 .032 AMD4 WinSrvr C# CCR 8.58 .0080 2.62 .081 0.839 .0031 0.838 .0031 AMD4 WinSrvr C# Locks 8.72 .0036 2.42 0.01 0.836 .0016 0.836 .0013 AMD4 WinSrvr C 5.65 .020 2.69 .0060 1.05 .0013 1.05 .0014 AMD4 XP C# CCR 8.05 0.010 2.84 0.077 0.84 0.040 0.840 0.022 AMD4 XP C# Locks 8.21 0.006 2.57 0.016 0.84 0.007 0.84 0.007 AMD4 XP C 6.10 0.026 2.95 0.017 1.05 0.019 1.05 0.017

Cache Line Interference

• Note measurements at a separation X of 8 (and values between 8 and 1024 not shown) are essentially identical

• Measurements at 7 (not shown) are higher than that at 8 (except for Red Hat which shows essentially no enhancement at X<8)

• If effects due to co-location of thread variables in a 64 byte cache line, the array must be aligned with cache boundaries

– In early implementations we found poor X=8 performance expected in words of A split across cache lines

Inter-Service Communication Note that we are not assuming a uniform implementation of

service composition even if user sees same interface for multicore and a Grid• Good service composition inside a multicore chip can require

highly optimized communication mechanisms between the services that minimize memory bandwidth use.

• Between systems interoperability could motivate very different mechanisms to integrate services.

• Need both MPI/CCR level and Service/DSS level communication optimization

Note bandwidth and latency requirements reduce as one increases the grain size of services • Suggests the smaller services inside closely coupled cores and

machines will have stringent communication requirements.

4343

Mashups v Workflow? Mashup Tools are reviewed at

http://blogs.zdnet.com/Hinchcliffe/?p=63 Workflow Tools are reviewed by Gannon and Fox

http://grids.ucs.indiana.edu/ptliupages/publications/Workflow-overview.pdf Both include scripting

in PHP, Python, sh etc. as both implement distributed programming at level of services

Mashups use all types of service interfaces and perhaps do not have the potential robustness (security) of Grid service approach

Mashups typically “pure” HTTP (REST)