1
Performance of a Multi-Paradigm
Messaging Runtime on Multicore Systems
Poster at Grid 2007
Omni Austin Downtown Hotel Austin Texas
September 19 2007
Xiaohong Qiu
Research Computing UITS
,
Indiana University Bloomington IN
Geoffrey Fox, H. Yuan, Seung-Hee Bae
Community Grids Laboratory, Indiana University Bloomington IN 47404
George Chrysanthakopoulos, Henrik Frystyk Nielsen
Microsoft Research, Redmond WA
2
Motivation
•
Exploring possible applications for tomorrow’s
multicore chips (especially clients) with
64 or
more cores
(about 5 years)
•
One plausible set of applications is data-mining
of Internet and local sensors
•
Developing Library of efficient
data-mining
algorithms
–
Clustering (
GIS, Cheminformatics
) and Hidden
Markov Methods (
Speech Recognition
)
3
Approach
•
Need 3 forms of parallelism
–
MPI Style
–
Dynamic threads
as in pruned search
–
Coarse Grain
functional
parallelism
•
Do not use an integrated language approach as in
Darpa HPCS
•
Rather use “
mash-ups
” or “
workflow
” to link
together modules in optimized parallel libraries
•
Use
Microsoft CCR/DSS
where DSS is mash-up
4
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,
21.38
11.3
16.3
15.5
10.32
Exchange
22.6
11.78
19.14
15.9
14.1
Exchange As
Two
Shifts
11.16
2.74
9.36
8.42
6.8
Shift
14.98
8.54
6.74
6.52
5.88
3.7
Pipeline
(MPI
23.92
12.74
10.18
8.9
7.44
Two Shifts
8.94
0.84
4.8
4.62
4.48
Shift
8.54
1.42
4.84
4.4
4.52
1.76
Pipeline
Spawned
8
7
4
3
2
1
(μs)
Number of Parallel Computations
AMD4: 4 Core
CCR Overhead for a computation
of 27.76 µs between messaging
CCR Overhead for a computation of
29.5 µs between messaging
Rende
vous
34.56
20
25.76
24.02
18.48
Exchange
36.16
22.14
30.64
27.48
23.76
Exchange As
Two Shifts
15.94
4.72
14.4
13.7
12.56
Shift
25.68
16.68
13.58
13.02
12.08
9.36
Pipeline
MPI
44.02
28.74
21
19.32
17.64
Two Shifts
13.52
4.38
10.08
9.34
8.3
Shift
12.12
3.02
10.18
9.38
8.3
3.32
Pipeline
Spawned
8
7
4
3
2
1
(μs)
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)
25.8 4 Thread CCR XP Intel4 16.3 4 Thread CCR XP 39.3 4 Process MPICH2 Redhat 99.4 4 Process mpiJava Redhat 152 4 Process MPJE Redhat 185 4 Process MPJE XP AMD4 20.2 8 Thread CCR Vista 100 8 Process mpiJava Fedora 142 8 Process MPJE Fedora 170 8 Process MPJE Vista Intel8b 64.2 8 Process MPICH2 111 8 Process mpiJava 157 8 Process MPJE Fedora Intel8c:gf20 4.21 8 Process Nemesis 39.3 8 Process MPICH2: Fast 40.0 8 Process MPICH2 181 8 Process MPJE Redhat Intel8c:gf12
MPI Exchange Latency Parallelism
Grains Runtime
OS Machine
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
MPICH mpiJava MPJE
MPI Exchange Latency on AMD4
0
2
4
6
8
10
•
One thread on each core
•
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 < 64 bytes (8 words) and Vista or XP
•
A is a double (8 bytes)
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
Clustering by Deterministic Annealing
Deterministically find cluster centers y
j
using “mean field
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)
10 Clusters
Parallel Multicore
Deterministic Annealing
Clustering
“Constant1”
Increasing number of clusters decreases
communication/memory bandwidth overheads
Intel 8b C# with 1 Cluster: Vista
Scaled Run Time for Clustering
Kernel
•
Run time for same workload per thread normalized by number of
data points
•
Expect Run Time independent of Number of threads if not for
parallel and memory bandwidth overheads
•
Work per data point proportional to number of clusters
Intel 8b C# with 80 Clusters: Vista
Scaled Run Time for Clustering
Kernel
•
Work per data point proportional to number of
clusters so memory bandwidth and parallel
overheads decrease as # clusters increase
Intel 8c C 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
Intel 8c C with 80 Clusters: Redhat
Scaled Run Time for Clustering
Kernel
•
Work per data point proportional to number of
clusters so memory bandwidth and parallel
overheads decrease as # clusters increase
Intel 8b C# with 1 Cluster: Vista Run
Time Fluctuations for Clustering
Kernel
•
This is average of standard deviation of run time
of the 8 threads between messaging
synchronization points
Intel 8b C# with 80 Clusters: Vista
Run Time Fluctuations for Clustering
Kernel
•
This is average of standard deviation of run time
of the 8 threads between messaging
synchronization points
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
PC07Intro [email protected] 30
Timing of HP Opteron Multicore as a function of number of simultaneous
two-way service messages processed (November 2006 DSS Release)
n
