fabric software - ornl€¦ · dr. robert w. wisniewski, chief software architect extreme scale...

Dr. Robert W. Wisniewski, Chief Software Architect Extreme Scale Computing

Smoky Mountain Conference 2016

Intel Confidential - For Disclosure Under NDA Only

Compute Memory/Storage

Fabric Software

Intel Silicon Photonics

Legal DisclaimerResults have been estimated based on internal Intel analysis and are provided for informational purposes only. Any difference in system hardware or software design or configuration may affect actual performance.

Intel, processors, chipsets, and desktop boards may contain design defects or errors known as errata, which may cause the product to deviate from published specifications. Current characterized errata are available on request.

Intel, Intel Xeon, Intel Core microarchitecture, and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries.

Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com, or from the OEM or retailer.

No computer system can be absolutely secure.

Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance. Consult other sources of information to evaluate performance as you consider your purchase. For more complete information about performance and benchmark results, visit http://www.intel.com/performance.

Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future costs and provide cost savings. Circumstances will vary. Intel does not guarantee any costs or cost reduction.

This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.

No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.

Intel does not control or audit third-party benchmark data or the web sites referenced in this document. You should visit the referenced web site and confirm whether referenced data are accurate.

Intel, the Intel logo, Xeon, Xeon Phi, Core, and others are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others.

Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit http://www.intel.com/performance.

Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at {most relevant URL to your product}.

Optimization Notice: Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

*Other names and brands may be claimed as the property of others

Copyright © 2016, Intel Corporation. All rights reserved.

Agenda

�Big Data

– Today

– Today and tomorrow

• Known capabilities on the horizon

• Longer term potential

3

Aurora | Science From Day One!Science From Day One!Science From Day One!Science From Day One!Extreme performance for a broad range of compute and data-centric workloads

Transportation Biological Science Renewable Energy

Materials Science Accelerators

Batteries / Solar Cells

Design / Data Analysis

Combustion Biofuels / Disease Control Wind Turbine Design / Placement

Fo

cu

s A

rea

s

Argonne Training Program on Extreme-

Scale Computing

US Industryand International

Training

Public Access

4Other names and brands may be claimed as the property of others.

Increase internal combustion engine efficiency by potentially 25%-50% with lower emissions through improved high-pressure designs with improved ignition

Enhanced extraction of biofuels from biomass by modeling the bottlenecks in bioconversion to enable rational design of superior catalysts

Exploration of evolution path of protein structures and extracting function from protein sequence and genomic context

Enhanced blade and bearing endurance and optimized turbine placement within a field

Optimize variable renewable energy injection into power grid over days and geographic regions

Climate Science

Dynamic Climate Systems

Materials design enabling the discovery of specific materials with higher energy and power densities, better stability and safety, and longer lifetimes

Creating optimized materials to improve photovoltaic efficiency and lowering manufacturing costs

Global optimization of accelerator design with integrated simulators for guiding, focusing and accelerating fields and assessment of stability

Tools that combine theory, modeling and analysis to interpret vast collections of experimental data from neutron, electron and x-ray accelerators to discover new material and molecule properties

Dynamic ecological and chemical evolution of the climate system through models that utilize observations, simulation and reanalysis data from multiple sources; improved hydrologic and carbon cycle processes

Personalized Medicine

5

Deep learning

6

The Four Vs of Big Data

7

• Volume

– 50 ZB by 2020

– 7B people each with potentially multiple devices

– 7B x N appliances each feeding information

• Velocity

– Smart cars each streaming 100s of sensors

– Data feeds, news, financial, etc.

• Variety

– Structured, static

– Video, picture

– Audio

• Veracity

– Uncertainty

– Inaccurate

Big Data Gone Wrong

8

Big Data (and AI) Gone Wrong

9

Copyright © 2016, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others.

Optimization Notice10

Big data frameworks: Hadoop, Spark, Cassandra, etc.

SQ

L

sto

res

NoS

QL

sto

res

In-m

em

ory

sto

res

Co

nn

ecto

rs

Data mining

Recommendation engines

Customer behavior modeling

BI

analytics

Real time analytics

Big data analyticsCurrent common practice

• Limited performance

• Many layers of dependencies

• Low ROI on HW investment

• Run on state-of-art hardware

• Built with a patchwork of math libs

• Under-exploiting hardware performance features

Data sources

Finance

Social media

Marketing

IoT

Mfg

…

Problem Statement

Agenda

• Big Data

– Today


�Known capabilities on the horizon

• Longer term potential

11

computecompute

Pro

cess

or

Ad

jace

nt

or

I/O

N

od

e

Co

mp

ute

N

od

eR

em

ote

S

tora

ge

Processor

Compute Node

I/O Node

Remote Storage

Addressing the memory and i/O walls

12

Keeping data closer to compute �better data intensive app performance and energy efficiency

Parallel File System (Hard Drive Storage)

SSD Storage

Local Memory

Enough Capacity to Support Local Application Storage

Local Processing Node Temporal Storage

Faster CheckpointingQuicker RecoveryBetter App Performance

Today Tomorrow

High

er Ba

ndw

idth

. Lo

wer

Late

ncy

and

Capa

city

Caches

Parallel File System (Hard Drive Storage)

In-Package High Bandwidth

Memory

Non-Volatile Memory

Burst Buffer Storage

Caches

SSF: Enabling Configurability & Scalability from components to racks to clusters to supercomputers

“Rack”

“Cluster”

• Xeon or Xeon-Phi – based on workload needs

• Compute flexibly aggregated

• Lowest latency compute to compute interconnect

• I/O Topologies for best performance

• Configurable I/O bandwidth director switch

• Burst buffer to decouple storage from I/O

“Chassis”Simple, dense,

& configurable

“Memory”Enough capacity

to support apps“I/O”

Adaptive & configurable “Compute”Right sized” & configurable

8

>3TFpeak DP

PERFORMANCE

3XFASTER

ST PERFORMANCEVS. KNC

5XFASTER

MCDRAM VS.DDR4 DIMMs

Knight’s LandingNext-Gen Intel® Xeon Phi™ processor

CPUCPU

MCDRAMMCDRAM

DDRDDR

NAND SSDNAND SSD

Hard Disk DrivesHard Disk Drives

CPU

MCDRAM

DDR

NAND SSD

Hard Disk Drives

15

Intel Directions (From IDF 2016)

• Commitment to open source with optimized machine learning frameworks (Caffe, Theano) and libraries (Intel® Math Kernel Library – Deep Learning Neural Network, Intel Deep Learning SDK).

• Disclosure of the next-generation Intel® Xeon™ Phi processor, codename Knights Mill, with enhanced variable precision and flexible, high-capacity memory.

• Today we completed the acquisition of NervanaSystems, bringing together the Intel engineers who create the Intel® Xeon® and Intel Xeon Phi processors with Nervana’s machine learning experts to advance the AI industry faster than would have otherwise been possible.

16

1000Xfaster

THAN NAND

1000Xendurance

OF NAND

10Xdenser

THAN DRAM

3D XPoint™ Technology

CPUCPU

DDRDDR

INTEL® DIMMSINTEL® DIMMS

Intel® Optane™ SSDIntel® Optane™ SSD

NAND SSDNAND SSD

Hard Disk DrivesHard Disk Drives

CPU

DDR

INTEL® DIMMS

Intel® Optane™ SSD

NAND SSD

Hard Disk Drives

17


Optimization Notice

Big Data & Machine Learning Challenge

Problem:

� Big data needs high performance computing.

� Many big data applications leave performance at the table –> Not optimized for underlying hardware.

Solution:

� A performance library provides building blocks to be easily integrated into big data analytics workflows.

Volume

Velocity Variety

Value



Intel® Data Analytics Acceleration Library (Intel® DAAL)

� Python, Java & C++ APIs

� Can be used with many platforms (Hadoop*, Spark*, R*, …) but not tied to any of them

� Flexible interface to connect to different data sources (CSV, SQL, HDFS, …)

� Windows*, Linux*, and OS X*

� Developed by same team as the industry-leading Intel® Math Kernel Library

� Open source, Free community-supported and commercial premium-supported options

� Also included in Parallel Studio XE suites

An Intel-optimized library that provides building blocks for all data analytics stages, from data preparation to data mining & machine learning


Optimization Notice

Intel DAAL Overview

Industry leading performance, C++/Java/Python library for machine learning and deep learning optimized for Intel® Architectures.

(De-)CompressionPCAStatistical momentsVariance matrixQR, SVD, CholeskyApriori

Linear regressionNaïve BayesSVMClassifier boosting

KmeansEM GMM

Collaborative filtering

Neural Networks

Pre-processing Transformation Analysis Modeling Decision Making

Sci

en

tifi

c/E

ng

ine

eri

ng

We

b/S

oci

al

Bu

sin

ess

Validation



• Speeds math processing for machine learning, scientific, engineering financial and design applications

• Includes functions for dense and sparse linear algebra (BLAS, LAPACK, PARDISO), FFTs, vector math, summary statistics and more

• De facto standard APIs for easy switching from other math libraries

• Highly optimized, threaded and vectorized to maximize processor performance

Intel® Math Kernel LibraryEnergy Financial

AnalyticsEngineering

DesignDigital

Content Creation

Science & Research

Signal Processing



Components of Intel MKL 2017

Linear Algebra

• BLAS• LAPACK• ScaLAPACK• Sparse BLAS• Sparse Solvers• Iterative • PARDISO*• Cluster Sparse

Solver

Fast Fourier Transforms

• Multidimensional• FFTW interfaces• Cluster FFT

Vector Math

• Trigonometric• Hyperbolic • Exponential• Log• Power• Root• Vector RNGs

Summary Statistics

• Kurtosis• Variation

coefficient• Order

statistics• Min/max• Variance-

covariance

And More…

• Splines• Interpolation• Trust Region• Fast Poisson

Solver

Deep Neural Networks

• Convolution• Pooling• Normalization• ReLU• Softmax

New

23 | © SAFFRON TECHNOLOGY 2016

NATURAL NATURAL NATURAL NATURAL INTELLIGENCEINTELLIGENCEINTELLIGENCEINTELLIGENCE

Associative Memory Scale COMPUTING COMPUTING COMPUTING COMPUTING

POWERPOWERPOWERPOWER

Experience-based Reasoning

RAISED WITHOUT RULES (OR MODELS)

A Cognitive Platform that Learns and Adapts AutomaticallyA Cognitive Platform that Learns and Adapts AutomaticallyA Cognitive Platform that Learns and Adapts AutomaticallyA Cognitive Platform that Learns and Adapts Automatically

BORN OF NEUROSCIENCE AND DATA SCIENCE

Accuracy

FOUNDATIONAL BELIEFS OUTCOMES

24 | © SAFFRON TECHNOLOGY 2016

CONTEXTUAL MATRIX CORRELATION AND DE-CORRELATION

JIM MEMORY

A massiveA massiveA massiveA massive hypergraphhypergraphhypergraphhypergraph of connections and coincidencesof connections and coincidencesof connections and coincidencesof connections and coincidencesSemantic and statistical deSemantic and statistical deSemantic and statistical deSemantic and statistical de----correlation correlation correlation correlation depending on question

Intel Confidential - For Disclosure Under NDA Only 25

Community name: OpenHPC Web Address: www.openhpc.communityCommunity name: OpenHPC Web Address: www.openhpc.community

Goals for the HPC software community

� Provide a common platform to the HPC community that works across multiple segments and on which end-users can collaborate and innovate.

� Simplify the complexity of installation, configuration, and ongoing maintenance of an HPC software stack

� Receive contributions and feedback from community

� Enable developers to focus on their differentiation and unique area, rather than having to spend effort on developing, testing, and maintaining a whole stack

� Deliver integrated hardware and software innovations to ease the path to extreme scale

Participation in OpenHPC as of 8/14/2016

OpenHPC is a Linux Foundation Project initiated by Intel and gained wide participation right away

The goal is to collaboratively advance the state of the software ecosystem

Governing board is composed of Platinum members (Intel, Dell, HPE, SUSE) plus representatives from Silver and Academic, Technical committees

Members

Performance Peak Framework: OSS Project and Product

27

University

Community

OEM

CommunityGNU

Linux

Parallel File system

Upstream

source

Communities

Resource Manager

Upstream

source

Communities

Upstream

source

Communities

Upstream

source

Communities

Integrates and tests HPC stacks and makes them available as OS

Base

HPC Stack

OEMStack

UniversityStack

Contributors include Intel, OEMs, ISVs, labs, academia

RRV

RRV

RRV

RRV

RRVs

Continuous Integration Environment-Build Environment & Source Control-Bug Tracking-User & Dev Forums-Collaboration tools-Validation Environment

Cadence 6~12 mo

“RRV” = Relevant and Reliable Version

Intel HPC

Orchestrator

PRODUCTPROJECT

Supported HPC Stack-Premium Features-Advanced Integration Testing-Testing at scale-Validated updates-Level3 Support across stack

OEMStack

Agenda

• Big Data

– Today


• Known capabilities on the horizon

�Longer term potential

28

29

*source: BDEC report (Reed et. al.) - to be released

*

30

Convergence

HW vs SW

31

Convergence

?

Data Management for Big Data(Long-Term View)

• Smooth and automatic representation between– Application data structure in memory

– Representation and access to NVRAM

– Storage to disk

• Moving compute to data

• Application makes system call

– make_permanent(*data), make_durable(*data)

32

main()

A[100][100][100];

graph_node {int value;edge e1;

} RAM nvram

main()

Intel Confidential

fabric software - ornl€¦ · dr. robert w. wisniewski, chief software architect extreme scale...

Documents