1
Multicore Sals
Parallel Programming 2.0
Peking University
October 31 2007
Geoffrey Fox, Huapeng Yuan, Seung-Hee Bae
Community Grids Laboratory, Indiana University Bloomington IN 47404Xiaohong Qiu
Research Computing UITS
,
Indiana University Bloomington INGeorge Chrysanthakopoulos, Henrik Frystyk Nielsen
Microsoft Research, Redmond WA2
Abstract of Multicore Sals
Parallel Programming 2.0
• Multicore or manycore systems are probably not architecturally that
different from parallel machines with which we are familiar. However in next 5-8 years the basic commodity (PC) chips will have 64-256 cores and currently there is little understanding of how to use them. It is
clearly essential (at least for major US technology companies) that we effectively use such cores on broadly deployed machines.
• This constraint makes multicore chips an exciting and different
problem
We describe general issues in context of the SALSA project at
http://www.infomall.org/multicore. This is using Service Aggregated Linked Sequential Activities where we are looking at a suite of parallel datamining applications as one important broadly useful capability for future multicore-based systems that will offer users navigation and advice based on the ever increasing data from sensors and the
Internet. A key idea is using services not libraries as the basic building block so that we can offer productive user interfaces (Parallel
Programming 2.0) by adapting workflow and mashups for composing parallel services. We still imagine that services will be constructed by experts using extensions of current threading and MPI models.
Too much Computing?
• Historically both grids and parallel computing have tried to
increase computing capabilities by
− Optimizing performance of codes at cost of re-usability − Exploiting all possible CPU’s such as Graphics
co-processors and “idle cycles” (across administrative domains)
− Linking central computers together such as NSF/DoE/DoD
supercomputer networks without clear user requirements
• 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 so need solutions that can be implemented in next 3-5 years
• Note that even cell phones will be multicore
• There is “Too much data” as well as “Too much computing” and
maybe processing the data deluge will “solve” the “Too much computing” problem
− Quite plausible on servers where we naturally will have lots of
data
− Less clear on clients but short of other ideas
− Intel RMS analysis: Gaming and Generalized decision support
Tomorrow
What
is …? it …?Is Whatif …?
Recognition Mining Synthesis
Create a model instance
RMS: Recognition Mining Synthesis
Model-based multimodal recognition Find a model instance Model
Real-time analytics on dynamic, unstructured, multimodal datasets Photo-realism and physics-based animation Today
Model-less Real-time streaming andtransactions on static – structured
datasets
What is a tumor? Is there a tumor here? What if the tumor progresses?
It is all about dealing efficiently with complex multimodal datasets
R
ecognition
M
ining
S
ynthesis
Images courtesy:
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
(e.g. datamining) 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
?
Broad Parallelism Issues and
Data-mining Algorithms
n
Looking at Intel list of algorithms (and all previous experience),
we find there are two styles of “micro” parallelism
• Dynamic search as in integer programming, Hidden Markov Methods
(and computer chess); irregular synchronization with dynamic threads
• “MPI Style” i.e. several threads running typically in SPMD (Single
Program Multiple Data); collective synchronization of all threads together
n
Most Intel RMS
are “MPI Style” and very close to
scientific
algorithms
even if applications are not science
n
Note
MPI
historically runs with
processes not threads
but likely
that threads will be implementation of choice for commodity
applications
n
Most “
commodity experience
” is for
few way concurrency
to
support Windows/Linux O/S in “
dynamic thread
” paradigm
“Space-Time” Picture
•
Data-parallel
applications map
spatial structure
of problem on parallel structure of both CPU’s
and memory
•
However
“left over” parallelism has to map into
time on computer
•
Data-parallel languages support this
Application Time Application Space t 0 t 1 t 2 t 3 t 4 Compute Time 4-wa Paralle Compute (CPU’s) T0 T1 T2 T3 T4
Data Parallel Time Dependence
• A simple form of data parallel applications are synchronous with all elements of the application space being evolved with essentially the same instructions
• Such applications are suitable for SIMD computers and run well on vector supercomputers (and GPUs but these are more general than just
synchronous)
• However synchronous applications also run fine on MIMD machines
• SIMD CM-2 evolved to MIMD CM-5 with same data parallel language
CMFortran
• The iterative solutions to Laplace’s equation are synchronous as are many full matrix algorithms
Synchronization on MIMD machines is accomplished by messaging
Local Messaging for Synchronization
• MPI_SENDRECV is typical primitive
• Processors do a send followed by a receive ora receive followed by a send
• In two stages (needed to avoid race conditions), one has a complete left shift
• Often follow by equivalent right shift, do get a complete exchange
• This logic guarantees correctly updated data is sent to processors that have their data at same simulation time
………
8 Processors
Loosely Synchronous Applications
•
This is
most common
large scale science and engineering
and one has the traditional data parallelism but now
each data point has in general a different update
–
Comes from
heterogeneity
in problems that would be
synchronous if homogeneous
• Time steps typically uniform but sometimes need to support variable time steps across application space – however ensure small time steps are t = (t1
-t0)/Integer so subspaces with finer time steps do synchronize with full domain
• The time synchronization via messaging is still valid
• However one no longer load
balances (ensure each processor does equal work in each time step) by putting equal number of points in each processor
• Load balancing although NP complete is in practice
surprisingly easy Application Time Application Space t 0 t 1 t 2 t 3 t 4
Dynamic (search/Thread) Applications
Application Time
Application Space
Application Space
Application Time
•
Here there is no natural universal ‘time’ in the application as
there is in science algorithms where an iteration number or
Mother Nature’s time gives global synchronization
•
Loose (zero) coupling or special features
of application needed for successful
parallelization
Some links
n
See
http://www.connotea.org/user/crmc
for
references
-- select tag
oldies
for venerable links; tags like
MPI
Applications Compiler
have obvious significance
n
h
ttp://www.infomall.org/salsa fo
r recent work
including publications
n
My tutoria
http://grids.ucs.indiana.edu/ptliupages/presentations/PC2007/index.html
Multicore SALSA at CGL
•
S
ervice
A
ggregated
L
inked
S
equential
A
ctivities
•
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
•
Use
low latency where performance needed
; use
high
latency where productivity needed
•
Developing set of
services (library)
of
multicore parallel
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 new
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 • Remember that the important applications are not scientific
computing but most of the algorithms needed are similar to those explored in scientific parallel computing
• We can divide problem into two parts:
− Micro-parallelism: High Performance scalable (in number of
cores) parallel kernels or libraries
− Macro-parallelism: 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
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
Good and Bad about MPI
•
MPI
(or equivalent locks on shared memory
machine) has a bad reputation as the “
machine-code
” approach to parallel computing
−
User must break problem into parts
−User must program each part
−
User must generate synchronization/messaging
between parts
•
However these defects imply a
very clear
performance
model as user needs to make explicit
both application and machine structure
•
Thus if you can do this, one
expects reliable
Other Parallel Programming Models
•
OpenMP
annotation or
Automatic Parallelism
of
existing
software
is practical way to use those pesky cores with
existing code
− As parallelism is typically not expressed precisely, one needs luck to
get good performance
− Remember writing in Fortran, C, C#, Java … throws away information
about parallelism
•
HPCS
Languages should be able to properly express
parallelism but we do not know how efficient and reliable
compilers will be
− High Performance Fortran failed as language expressed a subset of
parallelism and compilers did not give predictable performance
•
PGAS
(Partitioned Global Address Space) like UPC,
Co-array Fortran, Titanium, HPJava
− One decomposes application into parts and writes the code for each
component but use some form of global index
− Compiler generates synchronization and messaging
− PGAS approach should work but has never been widely used –
Summary of micro-parallelism
•
On
new applications
, use MPI/locks with
explicit user decomposition
•
A subset of applications can use “
data
parallel
” compilers which follow in HPF
footsteps
−
Graphics Chips and Cell processor motivate
such special compilers but not clear how many
applications can be done this way
•
OpenMP and/or Compiler-based Automatic
Parallelism for
existing codes
in
Composition of Parallel Components
• The composition (macro-parallelism) step has many excellent
solutions as this does not have the same drastic
synchronization and correctness constraints as one has for scalable kernels
− Unlike micro-parallelism 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
23
Mashups v Workflow?
•
Mashup Tools are reviewed at
http://blogs.zdnet.com/Hinchcliffe/?p=63
•
Workflow Tools are reviewed by Gannon and Fox
h ttp://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
Grid Workflow Data Assimilation in Earth Science
• Grid services triggered by abnormal events and controlled by workflow
process real time data from radar and high resolution simulations for tornado forecastsTypical
graphical interface to service
composition
Taverna another well known Grid/Web Service workflow tool
“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 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
Parallel Programming 2.0
•
Web 2.0 Mashups
will (by definition the largest
market) drive
composition tools
for Grid, web and
parallel programming
•
Parallel Programming 2.0
will build on Mashup
tools like Yahoo Pipes and Microsoft Popfly
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
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 − The requirements of multi-language support
• Further it seems useful to re-examine MPI and define a simpler
model that naturally supports threads or processes and the full set of communication patterns needed in SALSA (including
dynamic threads).
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
n 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 n Developed first by Durbin as Elastic Net for TSP
n 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.
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})
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
n
Minimize
Free Energy F = -Ln L
({y
},T)
rather than
energy E = -Ln L ({y})
Deterministic Annealing for Clustering I
n
Illustrating similarity between clustering and Gaussian mixtures
nDeterministic 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
y
1as centroid
nFor some
annealing schedule
for T, iterate above algorithm
testing correlation matrix in
x
iabout 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
nMovement 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://w
ww.savi.org) for com
munity 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
37
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
Preliminary Results
•
Parallel Deterministic Annealing Clustering
in
C# with
speed-up of 7
on Intel 2 quadcore
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
Machines Used
Intel8b: Dell Precision PWS690, 2 Intel Xeon CPUs E5355 at 2.66GHz, 8 cores L2 Cache 4x4M, Memory 4GB,
Vista Ultimate 64bit, Fedora 7
C# Benchmark Computational unit: 1.188 µs
Intel8c: Dell Precision PWS690, 2 Intel Xeon CPUs E5345 at 2.33GHz, 8 cores L2 Cache 4x4M, Memory 8GB,
Red Hat 5.0, Fedora 7
Intel8a: Dell Precision PWS690, 2 Intel Xeon CPUs E5320 at 1.86GHz, 8 cores L2 Cache 4x4M, Memory 8GB,
XP Pro 64bit
C# Benchmark Computational unit: 1.696 µs
Intel4: Dell Precision PWS670, 2 Intel Xeon Paxville CPUs at 2.80GHz, 4 cores L2 Cache 4x2MB, Memory 4GB,
XP Pro 64bit
C# Benchmark Computational unit: 1.475 µs
AMD4: HPxw9300 workstation, 2 AMD Opteron CPUs Processor 275 at 2.19GHz, 4 cores L2 Cache 4x1MB (summing both chips), Memory 4GB,
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
DSS Section
•
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
44
Timing of HP Opteron Multicore as a function of number of simultaneous two-way service messages processed (November 2006 DSS Release)
n Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better
Deterministic Annealing
•
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
•
Parallelization
is similar to ordinary K-Means as we are
calculating global sums which are decomposed into local
averages and then summed over components calculated in
each processor
•
Many similar data mining algorithms (such as annealing for
E-M
expectation maximization
) which have high parallel
efficiency and avoid local minima
•
For more details see
–
http
://grids.ucs.indiana.edu/ptliupages/presentations/Grid
2007PosterSept19-07.ppt and
Parallel Multicor
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 = 0.05 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
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
MPI Exchange Latency in µs (20-30 µs computation between messaging)
SALSA Performance
The macroscopic inter-service DSS Overhead is about 35µs
DSS is composed from CCR threads that hav
4µs
overhead for spawning threads in dynamic search applications
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
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
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
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.
• We need to discuss what we mean by services
• The requirements of multi-language support
n Further it seems useful to re-examine MPI and define a simpler model
that naturally supports threads or processes and the full set of communication patterns needed in SALSA (including dynamic threads).
