rule-based reasoning using gpus university of applied sciences and arts dortmund rule-based...

University of Applied Sciences and Arts Dortmund

Rule-based reasoning using GPUs

Martin Peters

!University of Applied Sciences and Arts Dortmund, Germany

Smart Environments Engineering Lab

CrEDIBLE 2014 - Martin Peters

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Motivation

2

• Reasoning is one key feature when using ontologies • Reasoning means to create new knowledge by inferring facts that are implicitly

given by the existing data !

• But: reasoning can still be a very time consuming task, depending on ‣ the dataset (size, complexity) ‣ the used ontology language (complexity) !

With respect to the growing amount of semantic data, reasoning needs to scale!

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How can the reasoning process be scaled?

1. Optimizing reasoning algorithms (each for a specific ontology language) 2. Scaling the hardware, e.g. in terms of a higher clock frequency 3. Scaling by parallelizing the reasoning process

3

6.3 ausführungsmodell 39

Compute Unit

PE PE PE PE...Compute Unit


PE PE PE PE...

Device

Compute Unit



PE PE PE PE...

Device

Compute Unit



PE PE PE PE...

DeviceHost

Abbildung 5: Abstrakte OpenCL Device-Architektur, nach [GHK+12]

Kernel-Code und führend somit die eigentlichen Berechnungen als Single Instruc-tion, Multiple Data (SIMD) aus. Das heißt, dass alle Prozessoren die gleiche Opera-tion auf unterschiedlichen Daten zur selben Zeit ausführen und so eine Paralleli-sierung stattfindet.

Das vorgestellte Plattformmodell entspricht dem tatsächlichen Hardware-Modellvieler Grafikkarten. Beispielsweise besteht die AMD Readon 7970 Grafikkarte aus32 Recheneinheiten die jeweils mit insgesamt 4 16-fachen Parallelprozessoren (ins-gesamt 64 PEs) bestückt sind [GHK+

12]. Entsprechend kann eine Anweisung aufdem Device auf insgesamt bis zu 2048 PEs gleichzeitig ausgeführt werden (2048

Threads), wobei jede Ausführung auf unterschiedlichen Daten erfolgen kann.

6.3 ausführungsmodell

Das Ausführungsmodell (Execution Model) teilt sich in zwei Bereiche auf, die be-reits in der Einführung in OpenCL erwähnt wurden: Kernel, die auf OpenCL Devi-ces ausgeführt werden und das Host-Programm, dass die Ausführungsumgebungkonfiguriert und bereitstellt. Das Host-Programm kann über API-Aufrufe einenKontext initiieren, der als abstrakter Container die Interaktion zwischen Host undDevice koordiniert und entsprechend der OpenCL Spezifikation [GM14] die fol-genden vier Ressourcen verwaltet:

• Devices: Ein oder mehrere Devices die über die Plattform zur Verfügungstehen

• Kernel: Die OpenCL Methoden samt assoziierten Parametern, die auf einemDevice ausgeführt werden

[ 6. Juni 2014 at 11:29 – classicthesis version 0.0.1 ]

Multicore CPU/GPUCluster / MapReduce

map

reduce

map

map

map

map

reduce output

output

input


Related Work

Large scale parallel reasoning• Cluster-based approaches ‣ Oren et al. (2009): divide-conquer-swap strategy ‣ Maier et al. (2010): MapReduce for EL++ ‣ Liu et al. (2012): MapReduce for Fuzzy pD* ‣ WebPie (Urbani et al. 2009/2010/2012): MapReduce for RDFS and pD*

!• Approaches using a single machine ‣ Kazakov et al. (2011): Classification of EL ontologies ‣ Ren et al. (2011): ABox reasoning of EL ontologies ‣ Urbani et al: (2013): DynamiTE: Parallel Materialization of Dynamic RDF Data ‣ Heino et al. (2012): RDFS reasoning on massively parallel hardware

4


Large scale parallel reasoning: summary

• Current large scale reasoner: ‣ usually rely on a specific ontology language and area of application ‣ rarely make use of the highly parallel (and thus powerful) architecture of

GPUs ‣ provide a weak support for reasoning on single machines !

• How to create a rule-based reasoner that makes use of single computing node and is able to perform large scale reasoning?

5

Rules

RDF DataRule-Engine using GPU(s)

Enriched Data


RETE algorithm

Implementing a rule-engineWhat is the RETE algorithm? • pattern-matching algorithm • introduced by Charles Forgy in 1982 • widely used in many rule-engines as well as for semantic reasoner !

• Basic steps:

6

1. create the RETE network 2. repeat until no new triples are derived

• perform the alpha-matching • perform the beta-matching • fire rules


RETE algorithm

The RETE network

7

R1: (?x ?p ?y) → (?p rdf:type rdf:Property)R2: (?x ?p ?y) (?p rdfs:domain ?c) → (?x rdf:type ?c)

α1 α2

β1

α2(?p rdfs:domain ?c)

α1(?x ?p ?y)

β1?p = ?p


RETE algorithm

alpha- and beta matching

8

T1: [Bob uni:publishes Paper1]T2: [Alice uni:publishes Paper2] T3: [uni:publishes rdfs:domain Researcher]

R1: (?x ?p ?y) → (?p rdf:type rdf:Property)

R2: (?x ?p ?y) (?p rdfs:domain ?c) → (?x rdf:type ?c)

α1 α2

β1

T1T2

T3T3

T3T1T2T3

T4: [uni:publishes rdf:type rdf:Property] [uni:publishes rdf:type rdf:Property]

T5: [rdfs:domain rdf:type rdf:Property]T6: [Bob rdf:type Researcher] T7: [Alice rdf:type Researcher]

α2(?p rdfs:domain ?c)

α1(?x ?p ?y)

β1?p = ?p


• Basically two approaches, to parallelize the RETE algorithm ‣ data partitioning → huge amount of synchronization necessary ‣ rule partitioning → parallelization depends on the number of rules !

• targeting modern GPUs as parallel hardware, both approaches don‘t work out ‣ global synchronization of data can be very expensive ‣ a problem needs to be break down into a high amount (e.g. millions) of small

problems that can be computed independently, thus, a high amount of parallel threads should be executed

RETE algorithm

Parallelization

9


• alpha-matching ‣ for each input triple one thread is created that checks, if that triple matches

to one or more alpha-nodes (n triples → n threads)

• beta-matching ‣ one thread for each match of one of the parent-nodes, that iterates through

all matches of the second parent-node and checks for a match

RETE algorithm

Parallelization: targeting massively parallel Hardware

10

α1 α2

β1

T1...

T2000

T2001...

T2700

T1T2...

T1999T2000

T200

1T2

002... T2

699T2

700

work-load

wor

k-ite

ms


Workload partitioning

How to handle large datasets?alpha-matching: !• the workload can easily be partitioned into smaller chunks that can be processed

independently • the chunk size can be chosen with respect to the target device

11



beta-matching• for beta-matching the workload cannot simply be divided

‣ the triple-references in the working-memories need to be resolved

‣ thus, all triples need to be available during that step

‣ this would limit the size of processabledata to the amount of triples, thatfit into the memory of the GPU !

• to overcome this issue, we use triple-matches: A triple-match m = (s,p,o,r) is a quadruple with s=subject, p=predicate, o=object of a triple and r=triple reference (unique number, that is used for identification in the internal triple store).

12

α1 α2

β1

T1...

T2000

T2001...

T2700


• working-memories need to be transformed to triple-matches which then are transferred to the GPU for beta-matching ‣ the working-memories can be divided to smaller chunks ‣ the transformation of the chunks can be done in parallel, too (using

multithreading)


beta-matching II

13


Evaluation

Test environment: architecture

14

chunks are prepared and submitted to the queue

using java multithreading

one thread for each GPU (each thread has exclusive

rights to a GPU)

GPU 1

GPU n

...

Thread GPU 1

Thread GPU n

...queue

takes a task

submit

read backresults

submit

read backresultshost devices

takes a task


Evaluation

Test environment: setup• Datasets:

‣ Lehigh University Benchmark (LUBM): LUBM125 to LUBM8000 ‣ DBpedia scaled to full, 1/2nd, 1/4th, 1/8th, 1/16th, and 1/32nd !

• Workstation with Ubuntu 12.04: ‣ 2.0 GHz Intel Xeon processor with 6 cores ‣ 64 GB memory ‣ two AMD 7970 gaming graphic cards

with 3GB of memory each

15


Evaluation

Parallelization: using chunks and multiple GPUs• evaluation of the impact of:

‣ using multithreading to prepare workload-chunks ‣ use of a second GPU

!!!!!!!!

• RDFS reasoning benefits primarily from the thread-level parallelization • pD* reasoning benefits significantly from the second GPU

16


• used Hardware: cloud-server with two Tesla M2090 GPUs and 192GB memory • applying ρdf and RDFS to LUMB datasets from 17.6M to more than 1.1B triples !!!!!!!!

• max throughput: ‣ ~ 2.7M triples/sec. for ρdf (WebPIE reported 2.1M triples/sec on 64 computing nodes) ‣ ~ 1.4M triples/sec. for RDFS

Evaluation

Scaleability

17

430 s

1200 s


Conclusion and future work

• we parallelized the RETE-algorithm for semantic reasoning in a way that ‣ the preparation of the workload can be performed in parallel using

multithreading ‣ the matching process can be performed on the GPU ‣ the workload can be distributed to multiple GPUs

• we showed that large scale and rule-based reasoning (to a limited size) is possible on a single computing node

• the new limitation we reached is the main-memory of the computing node itself !

• future work will include: ‣ investigation of concepts to reduce the memory usage ‣ distributing the workload not only to multiple GPUs, but also to multiple hosts

equipped with GPUs

18

CrEDIBLE 2014 - Martin Peters 19

Thank you for your attention!

contact: !Martin Peters [email protected]

rule-based reasoning using gpus university of applied sciences and arts dortmund rule-based...

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