"Distributed Computing and Grid-technologies in Science and Education "

"Distributed Computing and Grid-technologies in Science and Education"

PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS

Klimov Georgy

Dubna, 2012

AGENDA

• Grid & GPU• GPU architecture• CUDA technologies• Grid-projects with GPU using• Monotonic Basin Hopping method• CUDA-realization of MBH• Further investigations plan• Summary

Grid & GPU

PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMSKlimov G., CMC MSU 2012

GPU advantages:• ~33% of all PCs are equipped

with modern GPU (~60% - Nvidia)

• Common usage of GPU resources <5% (HD film)

• GPU optimized for working with huge textures arrays

• Modern GPUs consist of tens or even hundreds cores. It means great performance for some kinds of tasks

Problems, solving by Grid:

• effective using of existing resources

• working with huge data arrays

• providing high performance

GPU architecture

•scalable array of ТРС •with it’s own DRAM


• 8 Scalar Processors• 2 Special Functions Units• Double Precision Unit• Register File• Shared Memory• Texture Memory Cache• Constant Memory Cache

CUDA technology

CUDA – Compute Unified Device Architecture

• Supports all NVidia GPUs starting from GeForce 8-x series

• Low level access to the hardware - graphics API knowledge not required

• CUDA programming language is based on C/C++ syntax – easier porting of existing code

• Greater performance comparing to OpenCL (50-100% performance increase in different researches)


CUDA technology


CUDA programming model

CUDA technology


CUDA threads hierarchy• Threads groups in Blocks (1, 2 or 3-dim)

• Blocks groups in Grid (1 or 2-dim)

• Treads within Block:Sharing data through shared memory

Synchronizing their execution

• Threads from different blocks operate independently

• Built variables threadIdx, blockIdx etc.

CUDA technology

Memory type Access Level SpeedRegisters R/W Per-thread High (on chip)Local R/W Per-thread Low (DRAM)Shared R/W Per-block High (on chip)Global R/W Per-grid Low (DRAM)Constant R/O Per-grid High (L1 cache)Texture R/O Per-grid High (L1 cache)


CUDA memory hierarchy

Grid-projects with GPU using

GPUgrid.net - volunteer distributed computing project for biomedical research from the Universitat Pompeu Fabra in Barcelona (Spain)

Collatz Conjecture - research in mathematics, specifically testing the Collatz Conjecture also known as 3x+1 or HOTPO (half or triple plus one).

PrimeGrid - to bring the excitement of prime finding


Monotonic Basin Hopping method


1. Start from point x0 2. Repeat until the stop condition:

2.1. generate point Φ(x) 2.2. apply the local minimization algorithm to the point Φ (x) → get point x1. 2.3. if f (x1 ) < f (x) , then x = x1

3. Return x

Algorithm steps:

* Gradient descent was used as local minimization algorithm

CUDA-realization of MBH


Ymin

Ymax

I, j

Xmin Xmax

• Divide the research area into equal square areas

• Each thread implements the algorithm in it’s area

• Find minimum among the results of each thread



GPU1 - Tesla 10:max threads per block = 512max threads per dim = 512max blocks per dim = 65535number of multiproc = 30

GPU2 - GeForce GT 525M:max threads per block = 1024 max threads per dim = 1024 max blocks per dim = 65535 number of multiproc = 2

CPU - Intel core2duo T6400 number of cores = 2 Clock speed = 2 GHz

Used hardware:



• Four parameters: the radius of the “jump” of the algorithm MBH - r, the maximum number of steps in the cycle - N, the number of blocks launched - Nb and the number of threads per block - Nt

• Set Nb and Nt• The radius r is calculated as half of a square area diametr• The number of cycle’s steps N is determined a result of the experiment *• 4 test functions were selected: Ackley, Griewank, Rastrigin, Shubert

Methodology of the experiment

1. The result is considered valid if it differs from the tabular less than 0.001

2. The result is considered valid if an average of 9 times out of 10 gives the right within the specified accuracy of the answer

3. The time averaged over 20 runs of the program



AVG executing timeCPU 160 sec

GeForce GT 525M 35 sec

Tesla 10 1.5 sec

Results for Ackley function

Number of treads per block Number of treads per block

block

blocks

blocks

blocks

block

blocks

blocks

blocksM

inim

al ti

me

of fi

ndin

g ex

trem

um, s

ec

Min

imal

tim

e of

find

ing

extr

emum

, sec



Results for Griewank function

AVG executing time

CPU 155 sec


Tesla 10 2.2 sec

Number of treads per blockNumber of treads per block

block

blocks

blocks

blocks

block

blocks

blocks

blocksM

inim

al ti

me

of fi

ndin

g ex

trem

um, s

ec

Min

imal

tim

e of

find

ing

extr

emum

, sec



Results for Rastrigin function

AVG executing time

CPU 125 sec

GeForce GT 525M 28.5 sec

Tesla 10 2.0 sec

Number of treads per blockNumber of treads per block

block

blocks

blocks

blocks

block

blocks

blocks

blocksM

inim

al ti

me

of fi

ndin

g ex

trem

um, s

ec

Min

imal

tim

e of

find

ing

extr

emum

, sec



Results for Shubert function

AVG executing time

CPU 300 sec


Tesla 10 4.3 sec

block

blocks

blocks

blocks

Number of treads per block Number of treads per block

block

blocks

blocks

blocksM

inim

al ti

me

of fi

ndin

g ex

trem

um, s

ec

Min

imal

tim

e of

find

ing

extr

emum

, sec

Further investigations plan

• Use more complicated and accurate local optimization methods

• Uprgrade method of parallization• Improve algorithm of MBH “jump” set-up• Build solution for Molecular cluster modeling

based on MBH method• Integrate CUDA-solution to BNB-Grid project• Describe class of functions that can be

effectively processed on GPUs


Summary

• There are huge share of GPUs among PCs• GPU is a multicore system• CUDA is one of the technologies that provides

great performance of GPU calculations• There are a number of Grid-projects that

already use CUDA• Tests shows that in some cases GPU perform

5-100 times better than CPU


THANKS FOR YOUR ATTENTION!

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"Distributed Computing and Grid-technologies in Science and Education "