Exploiting Computing Exploiting Computing Power of GPU for Data Power of GPU for Data

Mining ApplicationMining Application

Wenjing Ma, Leonid Glimcher, GaWenjing Ma, Leonid Glimcher, Gagan Agrawalgan Agrawal

Outline of contentsOutline of contents

Background of GPU computingBackground of GPU computing Parallel data miningParallel data mining Challenges of data mining on GPUChallenges of data mining on GPU GPU implementationGPU implementation

k-meansk-means EMEM kNNkNN AprioriApriori

Experiment resultsExperiment results Results of kmeans and EMResults of kmeans and EM Features of applications that are suitable for GPFeatures of applications that are suitable for GP

U computingU computing Related and future workRelated and future work

Background of GPU computingBackground of GPU computing

Multi-core architectures are Multi-core architectures are becoming more popular in high becoming more popular in high performance computingperformance computing

GPU is inexpensive and fastGPU is inexpensive and fast CUDA is a high level language CUDA is a high level language

that supports programming on that supports programming on GPUGPU

CUDA functionsCUDA functions

Host functionHost function Called by host and executed on hCalled by host and executed on h

ostost Global functionGlobal function

Called by host and executed on dCalled by host and executed on deviceevice

Device functionDevice function Called by device and executed on Called by device and executed on

devicedevice

Architecture of GeForce 8800 Architecture of GeForce 8800 GPU (1 multiprocessor)GPU (1 multiprocessor)

Parallel data miningParallel data mining

Common structure of data Common structure of data mining applications (adopted mining applications (adopted from Freeride)from Freeride)

{ * Outer Sequential Loop * }While () {

{ * Reduction Loop * }Foreach (element e) {

(i,val) = process(e);Reduc(i) = Reduc(i) op val;

}}

Challenges of data mining on Challenges of data mining on GPUGPU

SIMD shared memory programming

3 steps involved in the main 3 steps involved in the main looploopData read Computing updateComputing update Writing updateWriting update

Computing updateComputing updatecopy common variables from device memory to shared memory

nBlocks = blockSize/ thread number

For i=1 to nBlocks{

each thread process 1 data element}

Global reduction

GPU ImplementationGPU Implementation

k-meansk-means Data are points (say, 3 dimension)Data are points (say, 3 dimension) Start with k clustersStart with k clusters Find the nearest cluster for each pFind the nearest cluster for each p

ointointdetermine the k centroids from the

points assigned to the corresponding center

Repeat until the assignments of points don’t change

GPU version of kmeansGPU version of kmeans

Device function:Shared_memory centernBlocks = blockSize / thread_numbertid = thread_IDFor i = 1 to nBlocks min = 0; For j = 1 to k dis = distance(data[tid], center[j]) if (dis < min) min = dis min index = i update[tid][min index] (data[tid],dis) Thread 0 combines all copies of update

Other applicationsOther applications

EMEM E step and M step, different amouE step and M step, different amou

nt of computationnt of computation AprioriApriori

Tree-structured reduction objectsTree-structured reduction objects Large amount of updatesLarge amount of updates

kNNkNN

Experiment resultsExperiment results

k-means and EM has the best pk-means and EM has the best performance when using 512 threerformance when using 512 threads/block and 16 or 32 thread blads/block and 16 or 32 thread blocksocks

kNN and apriori hardly get good kNN and apriori hardly get good speedup with GPUspeedup with GPU

k-meansk-means(10MB points)(10MB points)

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CPU-

Seq

1B,

64T

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128T

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256T

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512T

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512T

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512T

8B,

512T

16B,

512

T32

B, 5

12T

160B

, 51

2T16

00B,

512

T

seco

nds

fi l e copy mem copy computi ng

k-means (continued)k-means (continued)(20MB points)(20MB points)

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1B,

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512T

8B,

512T

16B,

512

T32

B, 5

12T

160B

, 51

2T16

00B,

512

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seco

nds

fi l e copy mem copy computi ng

EM (continued)EM (continued)(512K points)(512K points)

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seq

1B,

64T

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nds

fi l e copy E mem copyE comput i ng M mem copyM comput i ng

EM (continued)EM (continued)(1M points)(1M points)

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CPU-

seq

1B,

64T

1B,

128T

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2B,

256T

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256T

Seco

nds

fi l e copy E mem copyE comput i ng M mem copyM comput i ng

Features of applications that are Features of applications that are suitable for GPU computingsuitable for GPU computing

the time spent on processing the data must dominate the I/O cost

the size of the reduction object needs to be small enough to have a replica for each thread in device memory

using the shared memory to store frequently accessed data

the time spent on processing the data must dominate the I/O cost

I/O

computing

the size of the reduction object needs to be small enough to have a replica for each thread in device memoryNo locking mechanism on GPUThe access to the reductionobjects

are unpredictable

using the shared memory to store frequently accessed data Accessing device memory is very Accessing device memory is very

time consumingtime consuming Shared memory serves as a high Shared memory serves as a high

speed cachespeed cache For non-read-only data elements For non-read-only data elements

on shared memory, we also need on shared memory, we also need replica for each threadreplica for each thread

Related workRelated work

FreerideFreeride Other GPU computing languageOther GPU computing language

ss The usage of GPU computation The usage of GPU computation

in scientific computingin scientific computing

Future workFuture work

Middleware for data mining on Middleware for data mining on GPUGPU

Provide some compilation Provide some compilation mechanism for data mining mechanism for data mining applications on MATLABapplications on MATLAB

Enable tuning of parameters Enable tuning of parameters that can optimize GPU that can optimize GPU computingcomputing

Thank you! Questions?Thank you! Questions?