LYU0703 Parallel Distributed Programming on PS3 1 Huang Hiu Fung 05700512 Wong Chung Hoi05596742...

LYU0703 Parallel Distributed Programming on PS3

LYU0703Parallel Distributed Programming on PS3

Huang Hiu Fung 05700512Wong Chung Hoi 05596742

Supervised by Prof. Michael R. Lyu

Department of Computer Science and Engineering, CUHK2007-2008 Final Year Project Presentation (1st term)

Agenda

• Background Information• Architecture of PlayStation®3• Principals of Parallel Programming• Optimization of the ADVISER program:

1. Sequential Approach2. Parallel Approach

• Conclusion• Future Works• Q&A

Background Information

Limitation of single-core processor:

1. Memory Access Latency

2. Wire Delays

3. Power Consumption

Power Consumption

P = powerC = capacitance V = voltageF = processor frequency (cycles per second)

Development of Multi-Core Processor

Fig. 1.4 Growth of No. of Cores in Processors

Development of Multi-Core Processor

• Reduce power consumption- use multiple cores with low frequency instead of one with high frequency

• Efficient processing of multiple tasks- divide the computation work- execute among the cores concurrently

Project Objectives

• Need of parallel programming to optimize intensive-computation applications

• Study features of parallel programming, compare sequential and parallel approach

• Optimize an application, showing great improvement by parallel programming

Architecture of PlayStation®3 (PS3)

• A multi-core machine produced by Sony, with the Cell Broadband Engine

• Strong Computation Power

• Opened platform for other applications and development

Cell Broadband Engine (Cell BE)

PPE – Power Processor Element

SPE – Synergistic Processor Element

EIB – Element Interconnect Bus

Power Processor Element (PPE)

• 64-bit PowerPC architecture based

• General purpose operation• Designed as control-

intensive• Control I/O of main

memeory and other devices by the OS

• Control over all 8 SPEs

Fig. 2.5 Design of PPE

Synergistic Processor Element (SPE)

• Designed to provide computation performance

• SPU – perform allocated task• LS – the only memory• MFC – control data transfer• Totally 8 SPEs in Cell• Only 6 acessisble• 1 reserved for system software

1 disabled Fig. 2.6 Design of a SPE

Element Interconnect Bus (EIB)

• Internal communication bus inside Cell

• Connect different elements: PPE, SPEs. Memory controller

Fig. 2.7 Data Flow and Program Control

Principal of Parallel Programming

Parallel algorithm Serial algorithm

multiple processing units single processing unit

communication overhead no communication overhead

higher complexity in code straight forward code

ensure load balance between PU everything is done by CPU

Concept of Load Balance

• Distribute data evenly

• Total runtime depends on

the busiest processing

element

• Wasting computation

time on idling processing

element

Method of parallelism

• Data parallelism

• Task parallelism

Parallel Architecture

Flynn's taxonomy

Single

InstructionMultiple

Instruction

Single Data

SISD MISD

Multiple Data

SIMD MIMD

• Traditional Computer

• von Neumann model

• Same instruction on all data

• SIMD intrinsic function

• No well known system

• Mention for completeness

• Different instruction on

different data

• Task parallelism

• Further break down to– Shared Memory System– Distributed Memory System

Shared Memory System

• Access to central

memory for data

• PS3 :Achieve by

MFC issuing DMA

command

Distributed Memory System

• Each PE has its

own memory

• PS3: Each SPE

has 256KB Local Store

• PS3 is hybrid shared-distributed memory system

ADVISER

• Comparing 2 video clips

1.Generating meaningful data (in form of numbers) of frames from the video

2.Comparing and looking for the most similar frames

3.Locating the similar segment which consist of a series of very similar frames

• 2 Folder, “Repository” & “Target”

• hl3 file = vector of 1024 double precision values

Input No. of hl3 files

Target directory 5473

Repository directory 7547

Processing

• hl3 file = vector of 1024 double precision values

• File P

• File Q

• Similarity =

• Smaller the better

Output

• M “Target”, N “Repository”

• O ( M * N )

• Computation time = 633 sec

• Flash demo

target hl3 1 most match repository A difference value = ??target hl3 2 most match repository B difference value = ??target hl3 3 most match repository C difference value = ??

Parallel Version

• Split data to 6 SPEs evenly

• Computation time for 6 SPEs = 330 sec

• Flash demo

Parallel Version

• Expected speed up 6X

• Actual speed up 2X

• PC and PPU, SPE all run at different speed

• Computation time with CPU = 633 sec

• Computation time with 1 SPE = 1928 sec

• Computation time with PPU = 3119 sec

• CPU > SPE > PPULYU0703 Parallel Distributed Progra

mming on PS328

Time Attack

1. SIMD intrinsic function

2. Changing data type

3. Double Buffering

4. Parallel Read

5. Distributing Job to idling PPE

6. SIMD on loop counter

7. Loop unrollingLYU0703 Parallel Distributed Progra

mming on PS329

SIMD intrinsic function

• Addition, subtraction,

multiplication, etc.

• Operates on 128 bits

registers

• Date type: double (64 bits)

• Speed up 2X

Changing Data Type to int

• Precision not important

• Major speed up from

SIMD intrinsic

• Data type: int (32 bits)

• Total Speed up 4X

• Computation time

= 71 sec

Running Time of Parallel PS3, with SIMD Version

1 2 3 4 5 6No. of SPU used

Parallel version

Parallel + SIMDversion

Changing Data Type to float

• SPE specified for high

precision computation

• No intrinsic for int data

type at all

• Data Type: float (32 bits)

• Save data conversion time

• Speed up by 30%• Computation time = 49 sec

Running Time of Parallel, with SIMD, float input PS3version

1 2 3 4 5 6

No. of SPU used

Parallel + SIMD + int

Parallel + SIMD +float

Double buffering

• Save communication time

• MFC and SPU

• 2 buffers– Prefetching– Processing

• Not heavy in communication

• Minor speed up

Parallel Reading for All Files

• Read “Target” and “Repository” concurrently

• Share file reading job among SPEs

• Not improve as predicted, even slower

• Reason: hard disk cannot cannot handle concurrent request

• Failed Attempt

Distributing Job to Idling PPE

• PPE current job: read files, distribute files, collect result

• Use stall time to do some computation

• Relatively low computation power of PPE

• No significant improvement

• Increase program complexity

• Abandon this approach

Applying SIMD for Loop Counter

Major computation power consumed in:

• initialize i = 0, diff = (0, 0, 0, 0).• for i < Number of float numbers in a file / Number of

floats packed in a register

A. temp = SIMD subtraction on vector i in “Target” and “Repository” file.

B. diff = SIMD addition (SIMD multiplication (temp, temp) , diff).

• i = i + 1.• Loop back to 2.

• Try to optimize step 3

• Apply SIMD to the loop counter

• Addition and comparison operations are reduced by 8 times

• initialize i = (0,1,2,3,4,5,6,7) , diff = (0, 0, 0, 0).• for i[0] < Number of float numbers in a file / Number of floats packed in a

registertemp = SIMD subtraction on vector i[0] in “Target” and “Repository” file.

diff = SIMD addition (SIMD multiplication (temp, temp) , diff).temp = SIMD subtraction on vector i[1] in “Target” and “Repository” file.diff = SIMD addition (SIMD multiplication (temp, temp) , diff).temp = SIMD subtraction on vector i[2] in “Target” and “Repository” file.diff = SIMD addition (SIMD multiplication (temp, temp) , diff).temp = SIMD subtraction on vector i[3] in “Target” and “Repository” file.diff = SIMD addition (SIMD multiplication (temp, temp) , diff).temp = SIMD subtraction on vector i[4] in “Target” and “Repository” file.diff = SIMD addition (SIMD multiplication (temp, temp) , diff).temp = SIMD subtraction on vector i[5] in “Target” and “Repository” file.diff = SIMD addition (SIMD multiplication (temp, temp) , diff).temp = SIMD subtraction on vector i[6] in “Target” and “Repository” file.diff = SIMD addition (SIMD multiplication (temp, temp) , diff).temp = SIMD subtraction on vector i[7] in “Target” and “Repository” file.diff = SIMD addition (SIMD multiplication (temp, temp) , diff).

• i = SIMD addition (i, (8, 8, 8, 8, 8, 8, 8, 8)).• Loop back to 2.

Result of the parallel, with SIMD, float input, SIMD for loop counter PS3 version

No. of SPU used

1 2 3 4 5 6

Read input time (sec)

4 5 3 4 4 4

Total Elapsed time (sec)

286 146 97 75 60 51

Net Elapsed time (sec)

282 141 94 71 56 47

Running Time of Parallel, with SIMD, float input, SIMD forloop counter PS3 version

1 2 3 4 5 6

No. of SPU used

Parallel+SIMD+float

Parallel+SIMD+float+SIMD for i

• little improvement (about 4%).

• shows the possibility to have faster performance by further loop unrolling.

• The best performance becomes 47 sec

Loop Unrolling

• Proved that optimizing the loop can improve performance

• Completely loop unrolling

• More obvious speed up

Result of the parallel, with SIMD, float input, loop unrolling PS3 version

No. of SPU used

1 2 3 4 5 6

Read input time (sec)

3 4 3 3 4 3

Total Elapsed time (sec)

159 82 55 42 35 30

Net Elapsed time (sec)

156 78 52 39 31 27

Running Time of Parallel, with SIMD, float input, loopunrolling PS3 version

200250

1 2 3 4 5 6

No. of SPU used

Parallel+SIMD+float+SIMD for i

Parallel+SIMD+float+loop unrolling

• 45% faster

• ultimate best performance becomes 27 sec

Conclusion of Optimization

• PC version:663 sec

• PS3 with 1 SPU (i.e. sequential version on PS3):1928 sec

• Final optimized version of PS3:27 sec

23 times faster than PC version71 times faster than sequential version on PS3

Conclusion of Optimization

Elapsed time change with difference approach applied, in a6 SPU condition

lelSIM

SIMD fo

loop u

nrollin

Elapsed time

Future Works

• Port the whole ADVISER application on PlayStation®3

• Optimization throughout the whole application

The End

LYU0703 Parallel Distributed Programming on PS3 1 Huang Hiu Fung 05700512 Wong Chung Hoi05596742...

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