Fugaku as theCenterpiece ofSociety5.0 Revolution

 Satoshi Matsuoka
 Director, RIKEN Center for Computational Science 20200218 R-CCS Symposium Keynote @ Kobe
1

Science of Computing by Computing for Computing

R-CCS
International core research center in thescience of high performance computing(HPC)

III Achievements
First ‘Exascale’ SC: Fugaku R&D- R&D started since the genesisof the center, to design & build
world’s first “exascale”
supercomputer- Fugaku construction official
approval to build by thegovernment on Nov. 2018
Fugaku/post- K rack, CPU - Installation start Dec. 2019,（prototype）©Fujitsu operations to start early 2021

Top-Tier Awards HPC
High Speed algorithm for analyzing social
networks won Best paper Award at HiPC.
Contribution of Dr. Matsuoka has awarded
at ACM HPDC 2018
Achievement Award also
Asia HPC LeadershipAward atsupercomputing Asia
2019.

Groundbreaking Apps: E.g., Large-scale
Earthquake disaster simulation w/HPC & AI

Modeling of bldgs.,
underground structures,with geological layers©Tokyo Univ.

Ultra large scale
earthquake simulation
using AI for massive scale
earthquake in Tokyo area.
Nominated as finalist of
Gordon bell 2018. Winning
SC16, 17 Best Poster
Award.

Industry outreach e.g. RIKEN Combustion
System CAE Consortium
RIKEN Consortium to develop next generation
CAE for combustors has established with
11 industries and 9
academia members.
It would expand theusers of R-CCS
Software as well as
Fugaku.
Simulation CAE examples3

K computer (decommissioned Aug. 16, 2019)
Specifications






Graph 500 ranking

Massively parallel, general purpose supercomputer “Big Data” supercomputer ranking
No. of nodes: 88,128
Measures the ability of data-intensive loads
Peak speed: 11.28 Petaflops
 No. 1 for 9 consecutive editions
Memory:1.27 PB
since 2015
Network: 6-dim mesh-torus (Tofu)

Top 500 ranking

HPCG ranking

LINPACK measures the speed and efficiency of linear
equation calculations.
Real applications require more complex computations.
 No.1 in Jun. & Nov. 2011
 No.20 in Jun 2019

Measures the speed and efficiency of solving linear
equation using HPCG
Better correlate to actual applications
 No. 1 in Nov. 2017, No. 3 since Jun 2018

First supercomputer in the world to retireas #1 in major rankings (Graph 500)

“Best HPC Application of the Year”
 Winner 2011 & 2012. several finalists

ACM Gordon Bell PrizeK-Computer Shutdown Ceremony 30 Aug 20195

From K to Fugaku: Dramatic Change K: Focus mainly on Basic Science & Simulations
 Pro: Modernized Japanese SC to massively parallel Con: Large-scale simulations are ‘transient’: great

results but leaves success to scientific ‘chance’
Fundamental Change
Towards Society5.0

Impact to IT, industry and society moderate Fugaku: Centerpiece to Society 5.0
 Designed to have continuum to IT infrastructure atlarge (Arm, RHEL Linux, Container, Cloud)Fugaku High impact & ROI to IT, industry & society Can sustain towards future for its value, fosteringboth basic science & Society 5.0 usage

6

The “Fugaku” 富岳 “Exascale”
Supercomputer for Society 5.0
High-Peak --- Acceleration of
Large Scale Application(Capability)

Mt. Fuji representing
the ideal of supercomputing

Broad Base --- Applicability & Capacity
Broad Applications: Simulation, Data Science, AI, …
Broad User Bae: Academia, Industry, Cloud Startups, …
For Society 5.0

Co-Design Activities in Fugaku
Multiple Activities since 2011
Science of Computing
Science by Computing
・9 Priority App Areas: High Concern toGeneral Public: Medical/Pharma,
Environment/Disaster, Energy,
Manufacturing, …

A64fx
For theFugakusupercomputer

Select representatives fr
om 100s of applications
signifying various compu
tational characteristics

Design systems with parameters that consider various
application characteristics

 Extremely tight collabrations between the Co-Design apps centers,

Riken, and Fujitsu, etc.
 Chose 9 representative apps as “target application” scenario
 Achieve up to x100 speedup c.f. K-Computer Also ease-of-programming, broad SW ecosystem, very low power, …

Arm64fx & Fugaku 富岳 /Post-K are:
 Fujitsu-Riken design A64fx ARM v8.2 (SVE), 48/52 core CPU

HPC Optimized: Extremely high package high memory BW(1TByte/s), on-die Tofu-D network BW (~400Gbps), high SVE FLOPS(~3Teraflops), various AI support (FP16, INT8, etc.)
 Gen purpose CPU – Linux, Windows (Word), other SCs/Clouds
 Extremely power efficient – > 10x power/perf efficiency for CFD
benchmark over current mainstream x86 CPU Largest and fastest supercomputer to be ever built circa 2020
 > 150,000 nodes, superseding LLNL Sequoia
 > 150 PetaByte/s memory BW
 Tofu-D 6D Torus NW, 60 Petabps injection BW (10x global IDC traffic)
 25~30PB NVMe L1 storage
 The first ‘exascale’ machine (not exa64bitflops =>apps perf.)9 Acceleration of HPC, Big Data, and AI for Society 5.0Fugaku’s FUjitsu A64fx Processor is…
 an Many-Core ARM CPU…

48 compute cores + 2 or 4 assistant (OS) cores Brand new core design Near Xeon-Class Integer performance core
 ARM V8 --- 64bit ARM ecosystem
 Tofu-D + PCIe 3 external connection
 …but also an accelerated GPU-like processor

SVE 512 bit x 2 vector extensions (ARM & Fujitsu)
 Integer (1, 2, 4, 8 bytes) + Float (16, 32, 64 bytes)
 Cache + scratchpad-like local memory (sector cache) HBM2 on package memory – Massive Mem BW (Bytes/DPF ~0.4)Streaming memory access, strided access, scatter/gather etc.

Intra-chip barrier synch. and other memory enhancing features

 GPU-like High performance in HPC, AI/Big Data, Auto Driving…

10

Green500, Nov. 2019
 A64FX prototype – Fujitsu A64FX 48C 2GHz
ranked#1 on the list

 768x general purpose
A64FX CPU w/oaccelerators• 1.9995 PFLOPS @ HPL,
84.75%• 16.876 GF/W
• Power quality level 2
FUJITSU CONFIDENTIAL

11

Copyright 2019 FUJITSU LIMITEDst

SC19 Green500 ranking and 1 appeared TOP500
edition “A64FX prototype”, prototype of Supercomputer Fugaku,
ranked #1, 16.876 GF/W
w/o AccApprox. 3x better GF/W to 2nd system w/CPUs
w/ Acc

18
16
14
12

The first system w/o Acc ranked #1 in 9.5 years

GF/W

10
8

Sunway TaihuLight: #35, 6.051 GF/W

6

Endeavor: #94, 2.961 GF/W

4
2
0
0

50

100

FUJITSU CONFIDENTIAL

150

200

250

300

Green500 rank
12

350

400

450

500 3540

45

50

TOP500 edition
Copyright 2019 FUJITSU LIMITEDSignificance of Green500 Achievement
 Green500 until nowCPU

Custom

CPU

GPU
GPU

CPU

Specialized Architectures e.g. GPUs dominated Green500 w/Fugaku
FugakuCPU

CPU

Custom
GPU

CPU

CPU

GPU

First time ever general purpose CPU became #1

13
13

A64FX CPU performance evaluation for real apps
Relative speed up ratio Open source software, Realapps on an A64FX @ 2.2GHz
 Up to 1.8x faster over the latest
x86 processor (24core, 2.9GHz)
x 2, or 3.6x per socket High memory B/W and long
SIMD length of A64FX work
effectively with theseapplications

0.5

1.0

1.5

OpenFOAM
FrontlSTR

ABINITSALMON

SPECFEM3D
WRF
MPAS

FUJITSU A64FX
19x86 (24core, 2.9GHz) x2

A64FX CPU power efficiency for real apps
 Performance /Energy
consumption on an A64FX @2.2GHzRelative power efficiency ratio
1.0

3.0

OpenFOAM
FrontlSTR

 Up to 3.7x more efficient overthe latest x86 processor(24core, 2.9GHz) x2
 High efficiency is achieved byenergy-conscious design andimplementation

2.0

ABINITSALMON

SPECFEM3D
WRF
MPAS

FUJITSU A64FX
20x86 (24core, 2.9GHz) x2

4.0

Fugaku is a Year’s worth of IT in Japan
Units

Power

CPU ISA
System SW

AI
Acceleration

Smartphones

IDC Servers incl
Clouds20 million

300,000(2/3 annual shipments
in Japan)

10W×20 mil ＝200MW= (2/3 annual shipments =FugakuK Computer1

30~100in Japan)
600-700W x 30K＝

=

200MW
(incl cooling)

Arm
iOS/
Android Linux

x86/Arm
Linux (Red Hatetc.)/Win

Custom ASIC
Inference Only

Gen. Purpos
Accelerator e.g.
GPU

> 1/
30MW
>

15MW

Arm

Sparc

Linux (RedHat etc.)

Proprietary LinuxLow generality

Gen. CPU
SVE
instructions

None
16

Fugaku Performance Estimate on 9 Co-Design Target Apps
Category 100 times faster than K for some applications(tuning included)
 30 to 40 MW power consumption

 Peak performance to be achieved(double precision)

Peak SP(single precision)

K>400+ Pflops(34x +)

11.3 Pflops>800+ Pflops(70x +)

11.3 Pflops--

Total memory
bandwidth>150+ PB/sec(29x +)

5,184TB/sec

Peak HP

As of 2019/05/14

Basic
science> 37x+

Industrial
competitivenessenhancement

 Geometric Mean of Performance
Speedup of the 9 Target Applications
over the K-Computer

Energy issue(half precision)>1600+ Pflops(141x +)

Disasterprevention andEnvironment

Peak DP

PostK

Health and longevity

 Performance target goal

Performance
Speedup over K

Application

Brief description

125x +

GENESISMD for proteins

2. Personalized andpreventive medicine using big
data

8x +

Genomon

Genome processing(Genome alignment)

3. Integrated simulation
systems induced byearthquake and tsunami

45x +GAMERA

Earthquake simulator (FEM in
unstructured & structured grid)

4. Meteorological and global
environmental prediction using
big data

120x +NICAM+LETKF

Weather prediction system using
Big data (structured grid stencil &ensemble Kalman filter)

5. New technologies forenergy creation, conversion /storage, and use

40x +NTChemMolecular electronic simulation(structure calculation)

Priority Issue Area
1. Innovative computing
infrastructure for drug
discovery

6. Accelerated development ofinnovative clean energy
systems

35x +Adventure

Computational Mechanics System
for Large Scale Analysis andDesign (unstructured grid)

7. Creation of new functional
devices and high-performancematerials

30x +

RSDFT

Ab-initio simulation(density functional theory)

8. Development of innovative
design and production
processes

25x +

FFB

Large Eddy Simulation(unstructured grid)

9. Elucidation of thefundamental laws andevolution of the universe

25x +

LQCD

Lattice QCD simulation (structured
grid Monte Carlo)

Fugaku Programming EnvironmentPrograming Languages and Compilers provided by
Fujitsu

Fortran2008 & Fortran2018 subset



C11 & GNU and Clang extensions

OpenMP 4.5 & OpenMP 5.0 subset













FDPS (Framework for Developing Particle Simulator)

Process/Thread Library provided by RIKENOpen MPI base (Fujitsu), MPICH (RIKEN）

Low-level Communication Libraries
uTofu (Fujitsu), LLC(RIKEN）

File I/O Libraries provided by RIKENLustre

pnetCDF, DTF, FTAR

Math Libraries


BLAS, LAPACK, ScaLAPACK, SSL II （Fujitsu）



EigenEXA, Batched BLAS （RIKEN）

Programming Tools provided by Fujitsu

MPI 3.1 & MPI4.0 subset
Java

XcalableMP

E.g., Python+NumPy, SciPy

Communication Libraries


Parallel Programming Language & Domain SpecificLibrary provided by RIKEN


Script Languages provided by Linux distributor

C++14 & C++17 subset and GNU and Clangextensions

GCC and LLVM will be also available
Profiler, Debugger, GUI

NEW: Containers (Singularity) and other Cloud APIs

NEW: AI software stacks (w/ARM)PiP (Process in Process)

Optimized PyTorch, TensorFlow, etc.

NEW: DoE Spack Package Manager

18

New PRIMEHPC Lineup

PRIMEHPC FX1000
Supercomputer optimized for large scale computing
High Scalability

High Density

Superior power efficiency

PRIMEHPC
FX700
Supercomputer based on
Ease
to use technologies
Installation
standard

Copyright 2019 FUJITSU LIMITED

19

Introducing the Cray CS500 - Fujitsu A64FX Arm Server Next generation Arm® solution
 Cray Fujitsu Technology Agreement

 Supported in Cray CS500 infrastructure Cray Programming Environment Leadership performance for many memory
intensive HPC applications
 GA in mid’2020
FUJITSU CONFIDENTIAL

20

21

Two-fold Fugaku Missions
Carryover mission of K: Large-scale Simulation etc.
Support of Basic Sciences

Enhancement of R&D
missions carried overfrom K
Merit-based

Society 5.0 mission imposed by CSTI
Rapidly deliver rapid tangible results of highinterest to the society and SDG
Central platform for R&D
of next-gen Society 5.0
applications
Strategic w/industry

22Society5.0 will Fundamentally Change the Internet (1) Current Internet: more than 90% of traffic IDC=> edge,

video traffic (YouTube, NetFlix, etc.), caching optimization

As such IDC+Internet+Edge (Smartphones, PCs) are optimized
as CDN (Contents Delivery Network)








Same data used many times: (e.g. YouTube, Netflix)-> moderatedemand for storageIntra-IDC traffic is mainly ‘North-South’ i.e., between storage nodes
in the IDC towards the outgoing traffic to the edge. Intra-IDC traffic‘East-West’ is mostly for control, does not need high performanceinterconnect as is with supercomputers

Exo-IDC traffic is downstream outgoing. Since the same data are sent
repeatedly, caching traffic in the Internet is effective e.g. Akamai
Low affinity with scientific big data (-_-;)

 How will this chage with Society 5.0?

23

Society5.0 will Fundamentally Change the Internet (2) Future Internet: Traffic will reverse, Edge=>IDC, variety of sensor

data, in-flight processing



IDC+Internet+Edge: A real-time data analytics infrastructureRaw edge data are all different, no way to store them all (CAGR of storage
devices ~ 15%) -> must ‘throw away’ data

- Sophisticated AI real-time analytics necessary, not just simple compression
Intra-IDC Network require high performance as is SC, due to AI+HPC centric
workloads (IDCs to become supercomputers)

Inter-IDC Network IDC: Edge->IDC upstream ingest, caching does not work,need continuous real-time processing to ‘throw away’ data => from datacentric to compute centric
Compression is largely ‘feature detection’. Data analytics mainly (1)correlelative analysis between data, and (2) data reconstruction from features

 High affinity with scientific instrumentation and data analytics ☺

24

Society5.0 and Internet
Current InternetSociety5.0 Internet

East-West
North-South

Same data are copied &reusedData Type

Different Data from Sensors

IDC => Edge (Video)

Data Flow
Direction

Edge => IDC (w/processing)Small
North-South DominantIntra IDC
Traffic

Large (HPC & AI)
East-West Dominant
Supercomputer Interconnect

CDN dominated by Video (YouTube)

From Data Centric to Compute
Centric
25

Can Simulation be the Core Society5.0 Technology?
 Society5.0 (def. by MIA) “Human-centric society whereconvergence of cyber space (virtual world) and physical space(real world) resulting in for economic development as well as
resolution of societal issues.”

 Simulation models physical world in cyberspace =>fundamental to Society5.0

That is why major US IT companies are pushing HPC

 But Society 5.0 is mainly sensing=>analytics=>actuationHere, simulation is DIFFICULT TO APPLY due to cost=> AI surrogates & AI-reduced simulation models

=> we must train the AI through simulation

 As such HPC+AI would be fundamental: winning formulaagainst GAFA, including R-CCS

26

Fugaku will be a centerpiernce of Society 5.0
by CyberPhysical Assimilation of Data & Simulationof HPC and AI
Cyberspace
Simulationsynchronize
predict &control

Big Data

Physical World

SensingNatureBig Data Assimilation
110101111010111101011Societal Issues e.g., weather
IoT

AI x HPC

Human society and economy
Figure originally by Takemasa Miyoshi, R-CCS

Precise Bloodflow Simulation of Artery on
TSUBAME2.0(Bernaschi et. al., IAC-CNR, Italy)
Personal CT Scan + Simulation=> Accurate Diagnostics of Cardiac Illness
5 Billion Red Blood Cells + 10 Billion Degrees
of Freedom

MUPHY: Multiphysics simulation of blood flow
(Melchionna, Bernaschi et al.)Combined Lattice-Boltzmann (LB)
simulation for plasma and Molecular
Dynamics (MD) for Red Blood Cells
Realistic geometry ( from CAT scan)
Two-levels of parallelism: CUDA (onGPU) + MPIMultiphyics simulation
with MUPHY software
Fluid: Blood plasma
Lattice Boltzmann

Body: Red blood cell
coupledIrregular mesh is divided byusing PT-SCOTCH tool,considering cutoff distance

Extended MD

Red blood cells(RBCs) arerepresented as
ellipsoidal particles• 1 Billion mesh node for LB
component•100 Million RBCs

ACM
Gordon Bell
Prize 2011
Honorable
Mention4000 GPUs,
0.6Petaflops

Patient-specific cardiac blood flow simulation isscientifically significant, but cannot be utilized directly
to clinical diagnostics due to cost
 Tsubame2 requires 48 hours to simulate ONE heartbeat
 To compute in one hour on Fugaku requires 150 racks

BW bound algorithm: TSUBAME2 ~= Fugaku 3 racks x 48

Need $1 bil over 6 years => At most 40,000 patients => at least$25K per patient just for diagnosis, not feasible

 Large-scale simulation is beneficial when (1) results are fairly

predicable, and (2) result can be utilized broadly, but…

 Most science, both fundamental and applied, involve

numerous & repetitive experimentations due to costBasic science: large simulations cannot be executed often so
probability of new discoveries become low
30AI to the Rescue: Role of AI in Society5.0 (1) IT infrastructure for real time sensing->analysis->actuation
 Analytics and decision-making by surrogate AI is lightweight ->appropriate for the actual use in cyber-physical apps

 But AI needs to be trained: we DON’T NEED to collect large

data blindly. Rather, appropriate sampling of the model space But simple observations nor apriori rules insufficient,

especially to cover low probability ‘rare events’E.g., a pedestrian jumping in front of a car in auto-driving

 We use simulation to generate rare eventsHigh-level simulation capabilities essential

Search strategies in parameter space achieved by AI methods

High cost of repeated simulation amortized by multiple uses of the31
trained network

HPC and AI Convergence for Society 5.0 Manufacturing[Tsubokura et. al., R-CCS]Combining ML/Deep Learning, Data Assimilation, Multivariate
Optimization with Simulation for new generation manufacturingUse output of high-resolution simulation data to train AIConstruct AI surrogate model training on simulation data, allowing real-time CFD
to facilitate designer-engineer collaboration, multivariate design optimization, etc.



Use NN to derive reduced simulation model, allowing digital twin in cyberspacecorresponding to entities in physical space for real-time interactions

Surrogate AI Models
Neural Network
Real-Time CFD

Digital Twin
Physical Space

Designer-Engineer Collaboration

Reduced Simulation Models

Multivariate
Design Optimization

Simulation in CyberSpace

Auto-driving
AI Training

Real-world design
evaluation

Example: Automotive Multivariate CFD Design using HPC & AI
 Train AI with Simulation Inputs to create AI Surrogate Model

Hundreds of CFD simulations on variable car designs to generate training input dataSurrogate NN: body design input => Aerodynamic properties

Multivariate optimization w/genetic algorithm

Derive multiple optimal car designs rapidly

Fuel Efficient Design

CDWind shear Resistant Design
Optimization

ΔCD

Max epoch

10000

Batch size100

Smart Cities and High Performance Computing [Ito et. al., R-CCS
Social phenomena are complex, and have too many DOF and regimes/phases.
HPC with AI is a powerful tool to attack the complexity.
Example: car traffic in Kobe city
～30,000 roads, ～100,000 cars/day
Multivariate analysis ofsimulation results
No.1 factor(colored)→
explain 10% of traffic
and following～300 factors
from 5000 simulations.

Example: Tsunami evacuation planningof Nishi-yodogawa ward in Osaka～9,000 roads
86 safe places～50,000
evacuees from146 regions

Deep Neural Network analysis
Learning of 30,000 dimensional results↓ of Kobe traffic simulations
Classification of↓ city traffic modes

Genetic Optimizations of Evacuation simulations

GA codeof evacuation plan
Evolve towards“simple” and“effective” plan
Pareto optimum plans“Big data” through G5 and the followings, not only be monitored,
but also be used on HPC and AI for optimization and redesign of our society.

Revolutionizing Weather Prediction for Tokyo Olympics[Miyoshi et. al., R-CCS]
1-h-lead downpour forecast
refreshed every 30 seconds
at 100-m mesh
synchronize
predict & control

Cyberspace
Simulation

Big Data

Real world
SensingNatureBig Data Assimilation
110101111010111101011

Prepared for weather
IoT

AI x HPC

Human society and economy

Society5.0 innovates Big Data from being puredata centric to becoming compute centric Society5.0 incurs big change in big data, to be thrown away

Simple disposal: simple data selection, compression, etc
Correct disposal: extract the minimum set of features to allow
for necessary analysis as well as reconstruction of dataSuch extraction to be performed multiple times from edge=>IDC

E.g. Extract CAD data from the photos of a building

Data reconstruction methodologies


Simulation: from physical models parameterized by the features

Generative AI, e.g. GANBoth expensive, appropriate for HPC

 Seamless integration required Society5.0 -> true HPC&AI

36

R-CCS & Fugaku Society 5.0 Values• “Fugaku Arm” Pinnacle of Arm Ecosystem dominant for IOT• A64fx CPU: World’s fastest gen-purpose CPU (but can run PowerPoint)• Converged HPC, Cloud, AI, IoT Software Stack• Support modern SW e.g. VM, Container, Packaging (Spack), etc.

• “Fugaku AI” Development• PyTorch, TensorFlow based on SVE & DNNL for A64fx, Eigen, …
• Collaboration Between Fujitsu (Lab&Product), Arm, Riken (R-CCS, …)..
• Other research efforts on convergence of HPC & AI

• “Fugaku Cloud” facilitates broad cloud-style services• Collaboration with cloud service providers -> 8 companies accepted• 2020 experimental services, 2021 production• “Fugaku Live Stream” provides multipkle live data streams toSociety5.0 IoT applications• Variety of live data from multiple sources stored for certain period for
R&D of Society 5.0 apps as well as basic science apps
• Collaboration with various public & private sectors

37

Fugaku Cloud Strategy
 Industry use of Fugaku via

 A64fx and other Fugaku

Other IaaS
Commerci
al Cloud

Cloud Vendor
1

intermediary cloud SaaS
vendors, Fugaku as IaaS

Industry
User 1
Industry
User 2
Industry
User 3
Various Cloud Ser
vice API for HPC

HPC SaaS
Provider 1
HPC SaaS
Provider 2

HPC SaaS
Provider 3
KVM/Singularity, K
ubernetes, etc.

Technology being
incorporated into the Cloud

Extreme Performance Adv
antage富岳

Cloud Vendor
2
Cloud Workload Becoming
HPC (including AI)↓
Significant Performance Ad
vantage↓
Millions of Units shipped to
Cloud↓

Rebirth of JP Semion

Cloud Vendor
338

Fugaku accessibility with Cloud APIsnew!

interactive use is also available under batch job scheduling KVM and Singularity will be tested

 Front-end/PrePost environment Multi architecture based

x86(w/ GPU), arm TX2(w/ GPU), A64FX(48 nodes)interactive/batch/OpenStack/Kubernetes (will be tested)

OpenStack(will be tested)

CUI and access API(NEWT2.0 based) are available
access APISSH Jobs can be executed via Fujitsu batch job scheduler

Kubernetes(will be tested) Compute nodes

Front-end/PrePost
job control

 Amazon S3 compatible object storage (underprocurement)
39

Cloud Service Providers Partnershiphttps://www.r-ccs.riken.jp/library/topics/200213.html (in Japanese)×
Action Items• Cool Project name and logo!
• Trial methods to provide computing resources of Fugaku to end-users via service providers• Evaluate the effectiveness of the methods quantitatively as possible and organize the issues• The knowledges gained will be feedbacked to scheme design of Fugaku by the government

40

Massive Scale Deep Learning on
FugakuFugaku Processor◆High perf FP16&Int8◆High mem BW for convolution◆Built-in scalable Tofu network
High Performance DNN Convolution

Unprecedened DL scalability

High Performance and Ultra-Scalable Network
for massive scaling model & data parallelism

CPU
For theFugakusupercomputerCPU
For theFugakusupercomputerCPU
For theFugakusupercomputerCPU
For theFugakusupercomputer

TOFU Network w/highinjection BW for fast
reduction

Low Precision ALU + High Memory Bandwi Unprecedented Scalability of Data/dth + Advanced Combining of Convolution
Algorithms (FFT+Winograd+GEMM)

41

Fujitsu-Riken-Arm joint effort on AI framework
development on SVE/A64FX MOU Signed Fujitsu-Riken

Nov. 25, 2019

Step3

Optimization Levels

Step2
DL Frameworks(PyTorch, TensorFlow)

Arm DNNL

Step1
42

 w/Arm in works

Copyright 2019 FUJITSU LIMITEDArm Xbyak, Eigen + Lib

A64fx processor

Exaops of sim, data, and AI on Fugaku and Cloud42

Large Scale Public AI Infrastructures in Japan

Inference
838.5PF
Training
86.9 PFvs. Summit
Inf. 1/4
Train. 1/5

Deployed

PurposeAI Processor

Inference
Peak Perf.

Training
Peak Perf.

Top500Perf/Rank

Green500
Perf/Rank

Tokyo Tech.
TSUBAME3

July 2017

HPC + AI
PublicNVIDIA P100x 216045.8 PF(FP16)

22.9 PF / 45.8PF(FP32/FP16)

8.125 PF#22

13.704 GF/W
#5

U-TokyoReedbushH/L

Apr. 2018(update)

HPC + AI
PublicNVIDIA P100x 496

10.71 PF
(FP16)

5.36 PF / 10.71PF(FP32/FP16)(Unranked)(Unranked)

U-Kyushu
ITO-B

Oct. 2017

HPC + AI
PublicNVIDIA P100x 512

11.1 PF(FP16)

5.53 PF/11.1 PF(FP32/FP16)(Unranked)(Unranked)

AIST-AIRC
AICC

Oct. 2017AI
Lab Only

NVIDIA P100x 400

8.64 PF(FP16)

4.32 PF / 8.64PF(FP32/FP16)

0.961 PF
#446

12.681 GF/W
#7

Riken-AIP
RaidenApr. 2018(update)AI
Lab Only

NVIDIA V100x 432

54.0 PF(FP16)

6.40 PF/54.0 PF(FP32/FP16)

1.213 PF#280

11.363 GF/W
#10

AIST-AIRC
ABCI

Aug.
2018

AI
PublicNVIDIA V100x 4352544.0 PF
(FP16)

65.3 PF/544.0 PF(FP32/FP16)

19.88 PF#7

14.423 GF/W
#4

NICT
(unnamed)

Summer2019

AI
Lab Only

NVIDIA V100
x 1700程度~210 PF(FP16)~26 PF/~210 PF(FP32/FP16)

????

????

C.f. US ORNL
SummitSummer2018

HPC + AI
PublicNVIDIA V100
x 27,000

3,375 PF
(FP16)

405 PF/3,375 PF(FP32/FP16)

143.5 PF#1

14.668 GF/W
#3

Riken R-CCS
Fugaku2020~2021

HPC + AI
PublicFujitsu A64fx> x 150,000> 4000 PO(Int8)>1000PF/>2000PF(FP32/FP16)> 400PF#1 (2020?)> 15 GF/W???

ABCI 22022

AI

Future GPU

Similarsimilar~100PF25~30GF/W

43

Fugaku LiveStream: Society 5.0 Data Storage & Analysis
Platform
Analysis
センサーデータ

AnalysisAnalysis

ゲノムデータ

気象データ

交通データ

オブジェクトス
トレージ（S3互換）

ファイルストレージ（Lustreベース）

Analysis

AI+HPC+BD

様々な学術機関・研究所・企業と連携し、実際のIoT/ストリーム
データを多数一定期間格納し、Society5.0アプリの開発、及び社会実装へ

Post Moore
Cambrian Era

Many Core Era
Flops-Centric Monolithic Algorithms and Apps

Cambrian Heterogeneous Algorithms and Apps

Flops-Centric Monolithic System Software

Cambrian Heterogeneous System Software

Hardware/Software System APIs
Flops-Centric Massively Parallel Architecture

Hardware/Software System APIs
“Cambrian” Heterogeneous Architecture

Homogeneous General Purpose Nodes
Compute+ Localized Data
Compute
Nodes

Nodes

ComputeNodes

Gen CPUGen CPU

Data

Data
Compute
Nodes

~2025
M-P Extinction
Event

Heterogeneous CPUs + Holistic Data

Massive BW
3-D Package
Non-Volatile
Memory汎用CPU

Gen CPU

Data

Data

Loosely Coupled with Electronic Interconnect

Transistor Lithography Scaling(CMOS Logic Circuits, DRAM/SRAM)

Reconfigurable
Dataflow
DNN&
Neuromorphic

Low Precision
Error-Prone

OpticalComputing
Quantum
ComputingUltra Tightly Coupled w/Aggressive
3-D+Photonic Switching Interconnected

Novel Devices + CMOS (Dark Silicon)(Nanophotonics, Non-Volatile Devices etc.)

NEDO 100x Processor Project
Riken (R-CCS)/U-Tokyo/Tokyo Tech

Towards 100x processor in 2028
Various combinations of CPU architectures, new memory devices and 3-D technologiesPerf. measurement/characterization/models for high-BW intra-chip data movement

Cost models and algorithms for horizontal & hierarchical data movement

Programming models and heterogeneous resource management
Future HPC & BD/AI Converged Applications
2028 Strawman 100x
Architecture
100x BYTES-centric archi
tecture through perform
Heterogeneous & Highance simulations (R-CCS)

12 Apr, 2019

High-Bandwidth Hierarchic
al Memory systems and their management & Programming (Tokyo Tech)> 1TB NVM
Novel memory devices

10Tbps WDM Network

New programming met
hodologies for heteroge
neous, high-bandwidth
systems (Univ. Tokyo)

BW Vector CGRA archi
tecture (R-CCS)