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BDVA & ETP4HPC

WorkshopJuly 4th, Bologna

Organised by EXDCI

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Agenda item Actions TimeWelcome & Introductions 08:00-08:15

1. Common HPC and BD Glossary: determine a common reference to understand possible relationships between a typical HPC stack and a BD Analytics stack

BDVA Lead: Jim Kenneally (Intel Corp.)

HPC lead: Mark Asch(Univ. de Picardie), Hans-Christian Hoppe (Intel)

2. Cross-Pollination of HPC and BD Technologies: What respective technologies/approaches from a HPC stack or a BD Analytics stack can benefit the other’s needs e.g. respective hybrid systems that incorporate select elements from either HPC or BD technologies/approaches

BDVA lead – Nenad Stojanovic (Nissatech), supported by Gabriel Antoniu (Inria) & Alexandru Costan (Irisa)

HPC lead - Costas Bekas, IBM Research (available @10am), Mark Asch (Univ. de Picardie),

3. Extreme BD Workloads: Understand bottlenecks through better appreciation of centralised and decentralised processing of extreme big data workloads

BDVA Lead: Maria Perez (UPM)

HPC lead: Mark Asch, Stephane Requena (GENCI)

4. Collaboration between HPC CoEs and BD CoEs: Facilitate better communication between HPC’s CoE (https://exdci.eu/collaboration/coe) and BD COEs (http://i-know.tugraz.at/european-network/)

BDVA lead: Paul Czech (know-center) -HPC Lead: Erwin Laure (PDC-KTH)Available 11-12

5. Extreme Scale Demonstrators (ESDs) and Exascale co-design: Information update only

HPC lead: Michael Malms (ETP4HPC)

6. User engagement, that include understanding user base (UX Analysis), Skills development, Business Models

BDVA lead: Andrea Manieri (ENG)

HPC Lead:Francois Bodin (IRISA), Catherine Inglis (epcc)7. Explore options for possible collaborations in view of forthcoming WP18-20

All

Close 12:00

Agree timings

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#1 COMMON HPC AND BD GLOSSARY

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?Spark? ?Flink?

AI/ML/DL ?

July 4th, 2017EXDCI WP25

HPC, Big Data &Deep Learning Stacks

HPC Big Data Deep Learning

Infiniband &

OPA fabrics

Storage &

I/O nodes

x86 nodes,

GPUs, FPGAs

Linux OS Variant

Containers

PFS

(Lustre etc.)

MPIOpenMP,

threading

Accelerator

APIs

Numerical

libraries

Performance &

debugging

Domain-specific libraries

Compiled languages (C, C++,

FORTRAN)

Scripting lang.

(Python)

IDEs & Frameworks

(PETSc, …)

Compiled in-house, commercial & OSS applications

Cluster management

(OpenHPC)

Batch scheduling

(SLURM …)

Linux OS Variant (some Windows)

Ethernet

fabrics

Local

storage

x86 hyper-

convergent nodes

Virtualization: hypervisor or containers

(Dockers, Kubernetes, …)

VMM and container management

I/O libraries

(HDF5, …)

Orchestration and RMS

Cloud service I/FStorage systems

(DFS, Key/value, …)

Map-Reduce Processing

(Hadoop, Spark)

Data stream processing

(Storm, …)

Distributed coordination

(Zookeeper, …)

Workflows combining many application elements

Compiled languages

(C++)

Traditional ML

(Mahout)

Scripting & WF languages

(R, Python, Java, …)

Linux OS Variant (Windows?)

Ethernet

(traditional)

x86 +

GPU/FPGA, TPUInfiniband + OPA

(scale-out)

Virtualization: hypervisor or containers

(Dockers, Kubernetes, …)

VMM and container management

Orchestration and RMS

Cloud service I/FStorage systems

(DFS, Key/value, …)

Numerical libraries

(dense LA)

Neural network frameworks

(Caffe, Torch, Theano, … )

Load distribution layer

Accelerator APIs

Scripting languages

(Python, …)

Inference engines

(low precision)

Defined and instantiated/trained neural networks

Can be part of

Applications

Middleware

& Mgmt.

System

SW

Hardware

Source: Hans-Christian Hoppe, Intel Corp

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#2 CROSS-POLLINATION OF HPC AND

BD TECHNOLOGIES

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Historical Differences between Big Data and HPC

Workload

type

Typical workload focus is Design principles for

infrastructure and

software are

Big Data Data-intensive: devote

most of their processing

time to I/O and

manipulation of data

optimised for cost (IOPS)

first, rather than

maximum performance

HPC Compute-intensive:

devote most of their

execution time to

computation

optimised for

performance (FLOPS) first,

rather than for minimal

cost.

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Traditional Big Data Extreme Data Analytics

Enterprise IT HPC

Data-intensive workloads

[Example] Inferring new insights from big data-sets e.g. pattern recognition across suppliers, consumers, etc for data-driven insights and innovation

Compute- and Data intensive workloads:[Example] Reshaping healthcare through advanced analytics and artificial intelligence – leading to predictive and personalized medicine

‘Regular’ workloads

[Example] Running the enterprise – HR, Legal, Payroll, finance, etc.

Compute-intensive workloads:

[Example] Modelling and simulating focusing on interaction amongst parts of a system and the system as a whole e.g. product design

The hyper-growth area of Machine and Deep Learning and AI sit at the intersection of HPDAand HPC…Key workloads include video analysis, image speech & text analytics, medicine, IoT, ADAS, Security

Real-World Use Cases • Fraud/error anomaly detection e.g. FSI• Intelligence community e.g. anti-terrorism, anti-crime• Cyber security• Data-driven science/ engineering (e.g., biology)• Knowledge discovery e.g. ML/DL, cognitive, AI

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Cross-Pollination of HPC and BD Technologies

Cross-Pollination of respective BD and HPC platforms to build respectively

for

compute-intensive analytics (BD)

data-driven simulations (HPC)

Complex scenarios of this type of computation are emergingThe entire engineering domain based on digital twins is full of scenarios requiring a

hybird system

Digital twins use data from sensors installed on physical objects to represent their near

real-time status, working condition or position.

Increasingly used for improving the real-time operation of complex products/systems

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DIGITAL TWIN = digital model exists1000+ parameters

10+ parameters

COMPUTE INTENSIVE

DATA LESS-INTENSIVE

HPC

COMPUTE LESS INTENSIVE

DATA INTENSIVE

BD

HPC and BD(separate)

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DIGITAL TWIN = digital model exists

COMPUTE INTENSIVE

1000+ parameters

10+ parameters

DATA LESS-INTENSIVE

DATA INTENSIVE

TBs/hour

COMPUTE INTENSIVE

PBs

HPC

BD

HPC and BD INTEGRATED

EXTREME DATAANALYTICS BEHAVIOUR

SIMULATIONS

Model of normal

behaviour(predictive)

BEHAVIOUR PREDICTIONS

DATA TWIN

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Connected car example

1

380 million connected cars will be on the road by 2021

Ford: Predicting data storage requirements of 200PB by 2021 – growing from today’s 13PB

1TB data/hour

PB dataEXTREME DATAANALYTICS

DATA TWIN

BD HPCDIGITAL TWIN

HPCsimulations

edge analytics

streaming analytics

BD

BD

BD

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What are BD advantages (connected car example)

Stream processing

Efficient complex pipelines for real-time processing (e.g. Storm)

Real-time stream analytics (on-the-fly, no storage)

Edge analytics (real-time, on-the-fly, no storage)

Methods for real-time stream analytics can be downsized to work efficiently on the edge

Service logistics

Analytics on different levels

Combining real-time and batch processing (lambda architecture)

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What are challenges for BD

Streaming analytics

The streams and context can be dynamic, implying the need for

dynamically changing processing infrastructure (e.g. Storm has a static

topology)Self-adaptivity is the goal

Intelligent service placementEdge off-loading

Efficiency in processing extremly huge datasets

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What are opportunities for BD – HPC hybrid

Data twin?

…