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Performance of a Multi-Paradigm Messaging Runtime on Multicore Systems

Poster at Grid 2007Omni Austin Downtown Hotel Austin Texas

September 19 2007

Xiaohong QiuResearch Computing UITS, Indiana University Bloomington IN

Geoffrey Fox, H. Yuan, Seung-Hee BaeCommunity Grids Laboratory, Indiana University Bloomington IN 47404

George Chrysanthakopoulos, Henrik Frystyk Nielsen

Microsoft Research, Redmond WA

Presented by Geoffrey Fox gcf@indiana.eduhttp://www.infomall.org

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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)

• Choose algorithms that can be parallelized well

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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 model built from CCR and CCR supports MPI or Dynamic threads

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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/

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 Windows (limiting speed-up to 7 not 8!)

Machines UsedAMD4: HPxw9300 workstation, 2 AMD Opteron CPUs Processor 275 at 2.19GHz, 4 coresL2 Cache 4x1MB (summing both chips), Memory 4GB, XP Pro 64bit , Windows Server, Red HatC# Benchmark Computational unit: 1.388 µs

Intel4: Dell Precision PWS670, 2 Intel Xeon Paxville CPUs at 2.80GHz, 4 coresL2 Cache 4x2MB, Memory 4GB, XP Pro 64bitC# Benchmark Computational unit: 1.475 µs

Intel8a: Dell Precision PWS690, 2 Intel Xeon CPUs E5320 at 1.86GHz, 8 coresL2 Cache 4x4M, Memory 8GB, XP Pro 64bit C# Benchmark Computational unit: 1.696 µs

Intel8b: Dell Precision PWS690, 2 Intel Xeon CPUs E5355 at 2.66GHz, 8 coresL2 Cache 4x4M, Memory 4GB, Vista Ultimate 64bit, Fedora 7C# Benchmark Computational unit: 1.188 µs

Intel8c: Dell Precision PWS690, 2 Intel Xeon CPUs E5345 at 2.33GHz, 8 coresL2 Cache 4x4M, Memory 8GB, Red Hat 5.0, Fedora 7

AMD4: 4 Core Number of Parallel Computations

(μs) 1 2 3 4 7 8

Spawned

Pipeline 1.76 4.52 4.4 4.84 1.42 8.54

Shift 4.48 4.62 4.8 0.84 8.94

Two Shifts 7.44 8.9 10.18 12.74 23.92

(MPI)

Pipeline 3.7 5.88 6.52 6.74 8.54 14.98

Shift 6.8 8.42 9.36 2.74 11.16

Exchange As Two Shifts

14.1 15.9 19.14 11.78 22.6

Exchange 10.32 15.5 16.3 11.3 21.38

CCR Overhead for a computation of 27.76 µs between messaging

Rendezvous

CCR Overhead for a computation of 29.5 µs between messaging

Rendezvous

Intel4: 4 Core Number of Parallel Computations

(μs) 1 2 3 4 7 8

Spawned

Pipeline 3.32 8.3 9.38 10.18 3.02 12.12

Shift 8.3 9.34 10.08 4.38 13.52

Two Shifts 17.64 19.32 21 28.74 44.02

MPI

Pipeline 9.36 12.08 13.02 13.58 16.68 25.68

Shift 12.56 13.7 14.4 4.72 15.94

Exchange AsTwo Shifts

23.76 27.48 30.64 22.14 36.16

Exchange 18.48 24.02 25.76 20 34.56

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

MPI Exchange Latency in µs with 500,000 stages (20-30 µs computation between messaging)

Machine OS Runtime Grains Parallelism MPI Exchange Latency

Intel8c:gf12 Redhat MPJE Process 8 181

MPICH2 Process 8 40.0

MPICH2: Fast Process 8 39.3

Nemesis Process 8 4.21

Intel8c:gf20 Fedora MPJE Process 8 157

mpiJava Process 8 111

MPICH2 Process 8 64.2

Intel8b Vista MPJE Process 8 170

Fedora MPJE Process 8 142

Fedora mpiJava Process 8 100

Vista CCR Thread 8 20.2

AMD4 XP MPJE Process 4 185

Redhat MPJE Process 4 152

Redhat mpiJava Process 4 99.4

Redhat MPICH2 Process 4 39.3

XP CCR Thread 4 16.3

Intel4 XP CCR Thread 4 25.8

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 as 2 Shifts

AMD Shift

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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 Shift

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MPICH mpiJava MPJE MPI Exchange Latency on AMD4

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WindowsXP (MPJE)

RedHat (MPJE)

RedHat (mpiJava)

RedHat (MPICH2)

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Stages (millions)

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

CCR 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

Intel 8a

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 Redhat 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

• 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)

Cache Line Interference

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

Clustering by Deterministic Annealing • Use Physics Analogy for Clustering

Deterministically find cluster centers yj using “mean field approximation” – could use slower Monte Carlo

Annealing avoids local minima

Parallel MulticoreDeterministic Annealing Clustering

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Parallel Overheadon 8 Threads Intel 8b

Speedup = 8/(1+Overhead)

10000/(Grain Size n = points per core)

Overhead = Constant1 + Constant2/nConstant1 = 0.05 to 0.1 (Client Windows)

10 Clusters

20 Clusters

Parallel Multicore Deterministic Annealing Clustering

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#cluster

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“Constant1”

Increasing number of clusters decreases communication/memory bandwidth overheads

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

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

Number of Threads

Run Time Secs

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

Number of Threads

Run Time Secs

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

Number of Threads

Standard Deviation/Run Time

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

Number of Threads

Run Time Secs

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

Number of Threads

Standard Deviation/Run Time

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

Number of Threads

Standard Deviation/Run Time

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

• DSS is convenient as built on CCR

PC07Intro gcf@indiana.eduPC07Intro gcf@indiana.edu 3030

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Round trips

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Timing of HP Opteron Multicore as a function of number of simultaneous two-way service messages processed (November 2006 DSS Release)

CGL Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better

DSS Service Measurements

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