The journey to Memory-Driven Computing

– Enrique Matorras SER CME Chief Tecnologist

– July 3rd, 2019

Agenda

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HPE Memory driven approach

Gen-Z in the path to ultimate composability

HPE memory driven journey

The need

The needData Explosion

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If you thought you were having trouble keeping up today, it’s only going to get worse

Exponentially-increasing data

But conventional compute improving only incrementally

Growing capability gap

DATACOMPUTE

2006

COMPUTE

DATA

2008 2010 2012 2014 2016 2018 2020

0.3ZB

0.8ZB

1.2ZB

1.8ZB

4.4ZB

7.9ZB

15.8ZB

31.6ZB

44ZB

Explodingdata sourcesX X Shrinking

time to action

Massive advancesin computing power

needed everywhere=

What’s driving the data explosion? Structured data

40 petabytesWalmart’s transaction

database (2017)

Human interaction data

4 petabytes a dayPer-day posting to Facebook

across 2 billion users (2017)

2MB per active user

Digitization of analog reality

40,000 petabytes a day*10m connected cars by 2020

Front camera20MB / sec Front ultrasonic sensors

10kB / secInfrared camera

20MB / sec

Side ultrasonic sensors

100kB / sec

Front, rear and top-view cameras

40MB / sec

Rear ultrasonic cameras

100kB / secRear radar sensors100kB / sec

Crash sensors100kB / sec

Front radar sensors

100kB / sec

* Driver assistance systems only

The New Normal: Compute is not keeping up

6

0,3 0,8 1,2 1,84,4

7,9

15,8

31,6

44

0

5

10

15

20

25

30

35

40

45

50

2006 2008 2010 2012 2014 2016 2018 2020

Data(Zettabytes)Data nearly doubles

every two years(2013-2020)

Data growthTransistors(thousands)

Single-threadPerformance(SpecINT)

Frequency(MHz)

Typical Power(Watts)

Number of Cores

1975197719791981198319851987198919911993199519971999200120032005200720092011201320152017

107

106

105

104

103

102

101

100

Microprocessors

Source: K. Rupp. 42 Years of Microprocessor Trend Data Source: Data Age 2025 study, sponsored by Seagate, April 2017

HPE Composable approachGen-Z the path to ultimate composability

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Infrastructure is stretching to it’s limits

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Circa 2008Circa 2019

Two socket server architecture is stretching to its limits

• Relentless drive for performance, greater core counts

• Processors are more complex than ever§ DDR data rates and DDR channel counts are increasing …

§ … but DIMMs per channel are decreasing

§ PCIe data rates and lane counts are increasing

§ Storage data rates and lane counts are increasing

• More complex system designs drive up costs

PCIe Gen 3

PCIe Gen 4PCIe

Gen 5

DDR3

DDR4

DDR5

SATA Gen 2SAS Gen 3

Inter-Processor Links(many variants)

SATA Gen 3

SATA Gen 4

SAS Gen 4NVMeCC

IX

NVLinkOpenCAPI

PCIe Gen 2

SAS Gen 2

Protocols on the horizon (in orange)

Infrastructure is stretching to it’s limits

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Infrastructure networks & fabrics have challenges

• Still maintain a wide variety of networks/fabrics § Each has its special purposes

• None are designed to support native CPU load/store§ To support memory/storage convergence

PCIe Gen 4PCIe

Gen 5

DDR3

DDR4

DDR5

SATA 6GSAS 12G

Inter-Processor Links(many variants)

SATA 12G

SATA 24G

SAS 24GNVMeCC

IX

NVLinkOpenCAPI

Ethernet

The Tower of Protocol Babel

Omni-PathInfiniBandFibre Channel

PCIe Gen 3PCIe Gen 2

SAS 6G

The Solution:

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Memory Semantic Fabric (language of compute)

Gen-Z!

IT evolution towards composability

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Appl

icat

ion

optim

ized

Operations optimized

Improve staff productivity– Masks some complexity

with people and software– Preconfigured physical IT– Hardware defined– Targeted workloads

Simplify deployments– Remove complexity by

eliminating SAN– Fluid virtual IT– Software defined storage– Virtual workloads

Simplify Infrastructure as a Service– Hardware & Software

architected as one– Fluid IT – Software defined everything– Physical, virtual and

containerized workloads

Siloed Infrastructure– Complex processes– Static, siloed IT– Silo defined– Physical, virtual, and

containerized workloads

Traditional

Converged

Hyper-converged

Composable

Composability

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HPE and Channel Partners - Internal Use Only

DEVDEVDEV

TESTTEST

ANALYTICS

HANAHANAHANAHANAHANAHANA

VDI

Oracle

App Dev/Test environmentis needed now that peak season is over

Modeling & Analytics Run at night

SAP HANA for running production mission-critical workloads

VDI / CAD applications run during the day

and Oracle database

HPE Synergy composable infrastructureToday’s photonic ready solution for your traditional and cloud-native workloads

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HPE Memory driven approach

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We are radically rethinking our approach to computingAdvancing computing without relying on Moore’s Law

Unconventional accelerators

Unconventional architectures

Unconventional programming

Unconventional architectures

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GPU DSP

x86A

SIC

Phot

onic

Neuro

Quantum

POWER

SPARC

RIS

CV

ARM

FPGA

Memory+

Fabric

MemoryMemory

Mem

ory

Mem

oryM

emory

Memory

MemoryMemory

Mem

ory

Mem

ory

Mem

ory

Memory

SoC SoC

SoCSoC

SoC

SoCSoC

SoC

SoC

SoC

SoC

SoC

Future architectureMemory-Driven Computing

Today’s architecture From processor-centric computing

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Traditional vs. Memory-Driven Computing architecture

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Today’s architectureis constrained by the CPU

DDR

Ethernet

PCI

If you exceed the what can be connected to one CPU, you need another CPU

Memory-Driven Computing:Mix and match at the speed of memory

SATA

Memory-Driven Computing delivers ultimate composability

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Processors Memory

Storage Class Memory/Persistent MemoryNon-volatile, fast

FlashNon-volatile, but slow

DRAMVolatile, but fast

Accelerators

GPUAccelerates images manipulation

FPGAArray of logic gates that can be hardware-

programmed to fulfill user-specific tasks

Dot Product Engine/Neuromorphic Computinghigh performance per-watt task-specific

analog accelerator computing using

Memristor Arrays

Chaos ComputingSupercharged version of the

Dot Product Engine, creating an

Analog Computation Engine (ACE)

to take advantage of chaos

principles for tackling a class of

non-deterministic compute problems

Optical ComputingComputing circuits operated solely

using light

Logical systems composed of physical components

Or subparts or sub regions of

components (e.g. memory/storage)

Logical systems match exact workload requirements

No stranded resources

overprovisioned to workloads

Facilitates data-centric computing via shared memory

Reduces data movement

Do more with less, reduces cost

x86

Arm

RISC-V

Fabric

Specialized processors designed to perform

specific functions more efficiently

Memory-Driven Computing is the future for every kind of computing

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Edge deviceCloud

infrastructureBig memory

machineExascalecomputer

‒ Near-zero power‒ Persistent memory‒ AI task-specific

accelerator

‒ Composable infrastructure from every edge to any cloud

‒ Microservices in microseconds at massive scale

‒ High-performance data analytics

‒ Large shared memory

‒ Monte Carlo, graph analytics applications, etc.

‒ 100,000+ components‒ Ultra-fast message passing

and checkpointing

‒ 20x more energy-efficient than state-of-the-art

Fabric

Photonic interconnects “destroy distance” and makememory-speed communications practical over multi-kilometer distances

How big can Memory-Driven Computing go?

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4,096 yottabytes

292bytes

Memory space:

16 million devices

224addresses

Fabric size:

1,600 exascale computers

270FLOPs

Processing power:

Unconventional programming

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Transform performance with Memory-Driven programming

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In-memory analytics

15xfaster

New algorithms Completely rethinkModify existing

frameworks

Similarity search

40xfaster

Financial models

10,000xfaster

Large-scalegraph inference

100xfaster

DZNE discovered HPE’s Memory-Driven Computing — and saw unprecedented computational speed improvements that hold new promise in the race against Alzheimer’s

60% power reduction cuts research costs

101x increase in analytics speed blasts research bottlenecks, leading to shorter processing time — from 22 minutes to

13 seconds

Memory-Driven Computing helps outpace the global time bomb of neurodegenerative disease

Unconventional accelerators

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Beyond General Purpose. Beyond Moore’s LawUnconventional accelerators designed for Memory-Driven Computing

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Analog neuromorphic computing Massive speedup for AI training and inference

Optical ComputingDesigned for “unsolvable” optimization problems

– Harnessing the properties of light at the microscale

– Aims to solves NP-hard problems in milliseconds– Airline optimization– Logistics networks– Vehicle routing– Graph analytics

– Prototype has world record1,000 optical components

– Easily scalable to100,000 components

– Complex matrix calculations in one step instead of thousands

– 10-100x faster

– 10-1000x more energy efficient(Compared to GPU)

– Simple to program

– Simple to manufacture

Memory-Driven Computing – built for accelerators

Combining memory and storage in a stable environment to increase processing speed and improve energy efficiency

Using photonics where necessary to eliminate distance and create otherwise impossible topologies

Optimizing processing from general to specific tasks

Radically simplifying programming and enabling new applications that we can’t even begin to build today

Fast, persistentmemory

Fast memoryfabric

Task-specificprocessing

New and adaptedsoftware

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HPE memory driven journey

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Accomplishments and highlightsDelivering proof points, capturing our customers’ attention and gaining traction across the industry

November 2016Memory-Driven Computing proven

2017November 2017DZNE gets 100x faster results– The Machine User Group 600+ users– Continued leadership in IEEE

Rebooting Computing initiative

January –March 2017The Machine prototype scales up

June 2017Exascale ComputingDoE PathForward award

2017Memory-Driven Computing principles across HPE portfolio– HPE ProLiant Gen 10

servers with Silicon Root of Trust

– HPE Persistent Memory– HPE Superdome Flex

January 2018Gen-Z major milestones– Spec 1.0 release– 52+ consortium

members

June 2018Memory-Driven

Computing Sandbox– 48TB of shared memory

Pointnext Memory-Driven Computing capabilities

– Enabling proof-of-concept use cases with customers

April 2018Catalyst UK program– HPE Apollo 70 HPC with ARM– Partnership with 3 UK

universities, ARM, Cavium, SuSE

Vanguard Astra- Apollo 70 HPC- World’s Largest ARM

Supercomputer

2016 2018

June 2017First collaboration announced with DZNE

May 2017World’s largest single-memory computer system announced– The Machine prototype with

160 TB of shared memory– 1280 ARM cores

Real-timeEnterprise

High-performance Data analytics

SAP HANAin-memory

Project “Kraken”Co-innovation with SAP

Superdome X

Memory-Driven ComputingThe ultimate composable infrastructure

Extreme scalabilityfor transactions & analytics

Superdome FlexDesigned with Memory-Driven Computing principles

HPE is leading the industryFrom in-memory to Memory-Driven Computing

Memory-Driven Computing prototype

160TBThe world’s biggest single memory computer

HPE: your right mix today, your right mix tomorrow

Memory-Driven Computing bridges the widening gap between our goals and our capabilities.

Address tomorrow’s AI-driven workloads much faster, using much less energy, than conventional systems.

Memory-Driven Computing is the ultimate composable infrastructure from every edge to any cloud

Memory-Driven Computing will power the data-driven future

Thank youenrique.Matorras@hpe.com

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IT 109-"GenZ:ultimate composability the path to Memory Compute"