1

Uri weiser

Assignment by Yale

“Your vision of the future of computer architecture.

From the man who gave us MMX, refused to kill the

golden goose, and worked for a time in the same box

with Mark McDermott”

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Situation

Flew 11,482 km to greet Yale

Have to fight again with Bob

What can I fill-in after this extraordinary speaker?

Defiantly a challenge

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My moto:

Sailing - wind shift

wind

Buoy

4

a

a

a

Sailing competitiongetting first

Boat 1

Boat 2

wind

Buoy

- What is the Strategy of Boat 1?

- What is the Strategy of Boat 2?

My Moto: Do not follow Invent5

Uri WeiserProfessor

Technion

Haifa, Israel

Future Architecture Research

Big Data environment6

Outline

Big Data need reduction in energy/task

Power/Energy - the opportunities

Heterogeneous systems – past thoughts

Resource allocation in a Heterogeneous system

Efficient computation reduction of Data

movements

Avoid-the-Valley – past thoughts, deferent perspective

Big Data execution – where should we preform

execution of “Funnel” functions

– The Funnel (MASHPECH)

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Big Data

reduction in energy/task

Hadoop/Spark Calls for multiple computing engines

taking care of “ONE TASK”

Computing Centers’ attention was shifted from

Performance toward energy saving

The need for huge amount of processing

huge consumption of energy

See Google centers…

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Power/Energy the opportunities

Heterogeneous Systems –Past findings

Resource allocation in a Heterogeneous system - MA

Efficient computation reduction of Data

movements

Avoid-the-Valley – past thoughts, deferent perspective

Big Data execution – where should we preform

execution of “Funnel” functions

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Heterogeneous Computing:

Application Specific Accelerators

Performance/power

Apps range

Continue performance trend using Heterogeneous computing to

bypass power and energy hurdles

Accelerators

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Heterogeneous Computing

Pe

rfo

rman

ces/

Po

we

r

General Purpose

Accelerator

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MultiAmdahl:

Optimization using Lagrange

multipliersMinimize execution time (T)

under a Area (a) constraint

t2 t3 tnt1

F1(p1) F2(p2) Fn(pn)

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tj F’j(pj) = ti F’i(pi)

F’= derivation of the accelerator function

pi = Power of the i-th accelerator

ti = Execution time on reference computer

Power/Energy the opportunities

Heterogeneous Systems –Past findings

Resource allocation in a Heterogeneous system

Efficient computation reduction of Data

movements

Avoid-the-Valley – past thoughts, deferent perspective

Big Data execution – where should we preform

execution of “Funnel” functions

9

Power/Energy the opportunities

Efficient computation reduction of Data

movements

Avoid-the-Valley – past research power implications

The Funnel PreProcessing (FPP):

ak’a “In-Place-Computing” =

Compute at the most energy effective place

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Avoid-the-valley:

Many cores behind a common cacherunning many threads

Three regions: MC Region , the valley, MT Region

Threads

Perf

orm

an

ce

MC

reg

ion MT regionThe valley

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Avoid the ValleyParameter: Cache Size

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PS

Number Of Threads

Performance for Different Cache Sizes

no cache

16M

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perfect $

* At this point: unlimited BW to memory 16

Perf^2/Power

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Perf/P

ower pure compute

mem inst =1%

mem inst =5%

mem inst =10%

mem inst =20%

mem inst =30%

Performance/Power 1/(Energy Per Instruction)

Perf^2/Power

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mem inst =1%

mem inst =5%

mem inst =10%

mem inst =20%

mem inst =30%

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Number of Threads

Pe

rfo

rma

nc

e/p

ow

er

PER/PWR decline!

Input: Unstructured data

Big Data Data usage message

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Structuring

Input: Unstructured data

Structured data (aggregation)

A

ML Model creation

Data structuring

C

B

C Model usage @ client

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Existing Big data:

Data movements

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CPU

Cache

Memory

IO

Bridge

Memory related:

Reuse distance: >1G access

Cache related:

Reuse distance: >1M access

Cache/Memory are not effective if:

NIC or Disk/SSD

Copy of data

~nJoules/Byte

1. Why used-once data should move all the way to the “BIG” CPU?

2. Why use-once data is copied to memory?

DMA

Initial analysis: Hadoop-grep memory access

• Analysis of memory Hadoop-grep memory

accesses was performed

• Unique addresses have been identifies

• In each pack (10M memory accesses), we counted;• number of unique addresses that have been single accessed

• number of unique addresses that have been accessed multiple

times

• About 50% of Hadoop-grep memory references

have been single access

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Big DataSuggestion: Data movements reduction and free-up resources

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CPU

Cache

Memory

IO Bridge

NIC or Disk/SSD

Process Read-Once data close-to-IO(Funnel PreProcessing FPP)

Implications:

Free huge amount of memory for useful

work (think Hadoop/Spark)

Process funnel functions by small

efficient engines

Save Read/Write DRAM energy

Think about Big Data…

FPP

Open issues for research

SW and OS

Co-Processor or

Heterogeneous system

Compatibility

Application awareness

…

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Summary

The Funnel functions – execute close to the

data source

Free up system’s memory

Reduction of Data movement

Simple energy efficient engines at the front end

Issues

Compatibility issue: Apps, OS,

Amount of energy saving…

….

24

Thank You

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