Multicore SALSA: Parallel Computing and Web 2 0 for Cheminformatics and GIS Analysis

1

Multicore S

A

LS

A

Parallel Computing and Web 2.

for Cheminformatics and GIS Analysis

2007 Microsoft eScience Workshop at RENCI

The Friday Center for Continuing Education UNC - Chapel Hil October 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 Nielsen

Microsoft Research, Redmond WA

http://www.infomall.org/multicore

Too much Computing?

n 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

n 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

n Gaming and Generalized decision support (data mining) are two

obvious ways of using these cycles

• Intel RMS analysis

Too much Data to the Rescue?

n Multicore servers have clear “universal parallelism” as many

users can access and use machines simultaneously

n Maybe also need application parallelism as needed on client

machines

n 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

n 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?

CICC Chemical Informatics and Cyberinfrastructure Collaboratory Web Service Infrastructure

Portal Services

RSS Feeds User Profiles

Collaboration as in Sakai

Core Grid Services

Service Registry

Job Submission and Management

Local Clusters

IU Big Red, TeraGrid, Open Science Grid

Varuna.net

Quantum Chemistry

OSCAR Document Analysis InChI Generation/Search

Computational Chemistry (Gamess, Jaguar etc.)

Deterministic Annealing for Data Mining

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

n Developed first by Durbin as Elastic Net for TSP

n Extended by Rose (my student then; now at UCSB)) and Gurewitz

(visitor to C3_{P) at Caltech for signal processing and applied later to}

many optimization and supervised and unsupervised learning

methods.

n See K. Rose, "Deterministic Annealing for Clustering, Compression,

High Level Theory

n

Deterministic Annealing

can be looked at from a

Physics, Statistics and/or Information theoretic point of

view

n

Consider a function (e.g. a

likelihood

)

L({y})

that we

want to operate on (e.g.

maximize

)

n

Set

L

({y

},T) =



L({y}) exp(- ({y

} - {y})

/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

n

Minimize

Free Energy F = -Ln L

({y

},T)

rather than

energy E = -Ln L ({y})

• Use mean field approximation to avoid Monte Carlo

Deterministic Annealing for Clustering I

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

Deterministic Annealing for Clustering II

n This is an extended K-means algorithm

n Start with a single cluster giving as solution y₁ as centroid n For some annealing schedule for T, iterate above algorithm

testing correlation matrix in xi about each cluster center to see if

“elongated”

n Split cluster if elongation “long enough”; splitting is a phase

transition in physics view

n You do not need to assume number of clusters but rather a final

resolution T or equivalent

n Minimum evolving as temperature decreases n 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

Deterministi

Annealing

F({y}, T)

Clustering Data

n Cheminformatics was tested successfully with small datasets and

compared to commercial tools

n Cluster on properties of chemicals from high throughput

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

n Applying to PubChem (and commercial databases) that have

6-20 million compounds

• Comparing traditional fingerprint (binary properties) with real-valued

properties

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

Census aggregated in 200,000 Census Blocks covering Indiana

• 100MB of data

n Initial clustering done on simple attributes given in this data

• Total population and number of Asian, Hispanic and Renters

n 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

Where are we?

n We have deterministically annealed clustering running well on

8-core (2-processor quad 8-core) Intel systems using C# and Microsoft Robotics Studio CCR/DSS

n 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

n 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)

n Applied to several Cheminformatics problems and have parallel

efficiency but visualization harder as in 150-1024 (or more) dimensions

n Will develop a family of such parallel annealing data-mining

tools where basic approach known for

• Clustering

Clustering algorithm annealing by decreasing distance scale and gradually finds more clusters as resolution improved

Renters Total

Asian

Hispanic

Renters

IUB Purdue

10 Clusters

Total

Asian

Hispanic

Renters

Multicore S

A

LS

A

at CGL

•

Service

A

ggregated

L

inked Sequential

A

ctivities

− http://www.infomall.org/multicore

•

Ai

ms 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

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

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 CC

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

“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

Inside the SALSA Services

n

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

.

n

We use term “activities” in

SALSA

to allow one to build

services from either

threads,

processes

(usual MPI choice)

or even just other

services.

n

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.

n

There are several engineering and research issues for

SALSA

•

There is the critical

communication optimization

problem area for communication inside chips, clusters

and Grids.

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

25.8 4

Thread CCR

XP Intel4(4 core 2.8 Ghz)

16.3 4 Thread CCR XP 39.3 4 Process MPICH2 99.4 4 Process mpiJava 152 4 Process MPJE Redhat 185 4 Process MPJE XP AMD4

(4 core 2.19 Ghz)

20.2 8 Thread CCR (C#) Vista 100 8 Process mpiJava Fedora 142 8 Process MPJE Fedora 170 8 Process MPJE Vista Intel8b

(8 core 2.66 Ghz)

64.2 8 Process MPICH2 111 8 Process mpiJava 157 8 Process MPJE Fedora Intel8c:gf20

(8 core 2.33 Ghz)

4.21 8 Process Nemesis 39.3 8 Process MPICH2: Fast 40.0 8 Process MPICH2 (C) 181 8 Process MPJE (Java) Redhat Intel8c:gf12

(8 core 2.33 Ghz) (in 2 chips)

MPI Exchange Latency Parallelism

Grains Runtime

OS Machine

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

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

Parallel Multicore GI

Deterministic Annealing

Clustering

Parallel Overhea on 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 threa runtime fluctuations

10 Clusters

Parallel Multicore

Deterministic Annealing

Clustering

“Constant1”

Increasing number of clusters decreases communication/memory bandwidth overheads

Parallel Overhead for large (2M points) Indiana Census clusterin on 8 Threads Intel 8

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

MPI Parallel Divkmeans clustering of PubChem

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

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

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

CCR Overhead for a computation of

23.76 µs between messaging

Rende vous 20.16 18.78 13.3 11.22 6.94 Exchange 35.62 31.86 14.16 11.64 7.4 Exchange As Two Shifts 11.74 10.86 5.86 6.42 4.46 Shift 7.18 6.82 5.78 4.52 3.96 2.48 Pipeline MPI 19.44 14.32 6.84 5.9 4.94 Two Shifts 5.14 5.26 3.38 3.2 2.42 Shift 5.06 4.5 2.94 3 2.44 1.58 Pipeline Spawned 8 7 4 3 2 1 (μs)

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

Stages (millions) Time

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

Stages (millions) Time

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)

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

Inter-Service Communication

n

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

n

Note bandwidth and latency requirements reduce as

one increases the grain size of services

• Suggests the smaller services inside closely coupled cores and

43

Mashups v Workflow?

n Mashup Tools are reviewed at

http://blogs.zdnet.com/Hinchcliffe/?p=63

n Workflow Tools are reviewed by Gannon and Fox

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

n Both include scripting

in PHP, Python, sh etc. as both implement

distributed

programming at level of services

n Mashups use all types

of service interfaces and perhaps do not have the potential

robustness (security) of Grid service approach

n Mashups typically