areg melik-adamyan, engineering manager · software and workloads used in performance tests may...

Areg Melik-Adamyan, Engineering Manager

Copyright © 2015, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others.

Optimization Notice

Legal Disclaimer & Optimization Notice INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS”. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO THIS INFORMATION INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT.

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.

Copyright © 2015, Intel Corporation. All rights reserved. Intel, Pentium, Xeon, Xeon Phi, Core, VTune, Cilk, and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries.

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.

Notice revision #20110804

2


Optimization Notice

75% of current cloud demand comes from consumer services

Cloud 2015 Digital Services Economy

Cloud 2020 IoT, Big Data, and

Enterprise

1: Source: Intel Internal Analysis 2: Intel Estimates

1

By 2020, 65-85% of apps will be delivered via cloud infrastructure

2

Cloud Infrastructure Enables New Usages

2


Optimization Notice

Software Defined Infrastructure

OS, Libraries

Merging Technology Paths • Enterprises seeking Cloud Native Apps • Hyperscale Providers expanding into

traditional workloads

Fragmented Cloud Ecosystem • Growing Cloud ecosystem • Limited out of the box solutions Resource Pool

Orchestration Software

Virtualization Software

Developer Environment

3

Compute Storage Network

A world where the application defines the system

But… Cloud Technology is Too Complex


Optimization Notice

5

Continuous workload optimization

CH

ALL

EN

GE

S

GO

AL

S

Volatility of demand

Difficult to achieve high efficiency and scale

Fragmented cloud stacks with significant feature gaps

Proprietary solutions can be costly to deploy

Open source stacks are complex and lack enterprise features

Hyperscale Fast Followers Broad Enterprise

Deliver the best performance / TCO

Deliver differentiated services at hyperscale economics

Enable business innovation through the cloud

Industry Is Not Moving Fast Enough


Optimization Notice

5

Hyperscale Fast Followers Broad Enterprise

Deliver the best performance / TCO

Deliver differentiated services at hyperscale economics

Enable business innovation through the cloud

Collaborate with industry leaders

Improve Software Defined Infrastructure efficiency and ease deployment

Optimize cloud infrastructure across a full range of workloads 1 2 3

INTEL STRATEGY:

Ultimate Need for Simple and Effective Cloud Infrastructure


Optimization Notice

Optimize Cloud Infrastructure across a range of workloads

Source1: Intel estimates Other names and brands may be claimed as the property of others

Intel Xeon processor trends (Top 7 CSPs Silicon volume)

Optimizing to take full advantage of hardware capabilities

Shipping custom Si to all hyperscale CSPs

2x+ performance on programmable algorithms

Azure-G C4, M4

Early adoption of high performance components

1

1

6

Up to 50% cost reduction in service delivery


Optimization Notice

Make SDI efficient and easier to deploy

8

Accelerating technology maturity • Enterprise ready: reliability features,

installation, and operations • Scale to thousands of nodes • Intelligent automation and orchestration

Exposing underlying hardware technology • Support for Xeon, Xeon Phi, SoCs, … • Workload specific accelerators (eg: FPGA) • Expose platform telemetry • Hardware-enabled security

So far… What’s next

2

Other names and brands may be claimed as the property of others Reduce infrastructure deployment time from weeks to hours


Optimization Notice

Align the industry

9

3

Reference Architectures Open Solutions and Industry Standards

Rackscale Architecture

Routes to market

Cloud SW OEMs VARs

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

Open Container Project

Clear Linux Project

Enable thousands of new cloud deployments


Optimization Notice

Making Cloud Simple & Efficient WILL Accelerate USE

10

Align the industry

Make SDI efficient and easier to deploy

Optimize cloud infrastructure across a full range of workloads 1

2

3

1: Source: Intel and Bain estimates 2: Source: Intel IT internal estimate

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

Intel Strategy Emerging Cloud Usages

• Up to 50% savings in relative cost per VM instance • Reduce infrastructure setup time from weeks to hours • Enable thousands of new cloud deployments

1

2

4


Optimization Notice

Different Workloads - Two Large-Scale Systems

11

Modern supercomputer

Run programs in hours or days that would require decades or centuries on normal machine

Designed for numerically-intensive applications

Internet Data Center

Support millions of customers

– Mostly small transactions

– …and large-scale analytics

Designed for data collection, storage, and analysis

NCSI August, 2015


Optimization Notice

Computing Landscape

12

Computational Intensity (Petaflops)

Personal Computing

Cloud Services D

ata

Inte

nsity

(Pet

abyt

es)

• Web search • Mapping / directions • Language translation • Video streaming

Internet Data Center

Traditional Supercomputer

Internet-Scale Computing

Modeling & Simulation-Driven

Science & Engineering


Optimization Notice

Computing Landscape

13

Computational Intensity (Petaflops)

Personal Computing

Cloud Services D

ata

Inte

nsity

(Pet

abyt

es) Internet Data Center

Traditional Supercomputer

Internet-Scale Computing

Modeling & Simulation-Driven

Science & Engineering

Mixing simulation with real-world data Real-time analysis of simulation results

Desire for Convergence

Sophisticated data analysis E.g., deep learning


Optimization Notice

Aspirations for Convergence

14

NCSI, 2015

“Big Data” Data Analytics

High-Performance Modeling And Simulation

Large Scale Data Driven

Modeling And Simulation

Dat

a In

tens

ity (E

xaby

tes)

Computational Intensity (Exaflops)


Optimization Notice

Observations

Cannot Measure a Machine by Single Benchmark

Top500 benchmark: Perform Gaussian elimination of dense matrix

Not representative of real-world applications

Cannot Measure a Country’s HPC Capabilities by Its Biggest Machine

Want diversity (size and design) of machines – Encourages use and innovation

Want them widely deployed

Want well-developed software infrastructure

Want machines to be running useful applications across many disciplines

Want research program to ensure continued progress

15

NCSI, 2015


Optimization Notice

Modern Supercomputer Programming

System Level Message-Passing Interface (MPI) supports node computation, synchronization

and communication

Node Level OpenMP supports thread-level operation of node CPU Exploiting large vectors (SIMD) Xeon Phi and CUDA programming

environment for GPUs – Performance degrades quickly if don’t have perfect balance among memories and

processors

Result Single program is complex combination of multiple programming paradigms Tend to optimize for specific hardware configuration

“As the performance of HPC machines approaches infinity, the number of people who program them is a approaching zero.” — Dan Reed

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NCSI, 2015


Optimization Notice

Supercomputer Programming Model

Program on top of bare hardware

Performance

Low-level programming to maximize node performance

Keep everything globally synchronized and balanced

Reliability

Single failure causes major delay

Engineer hardware to minimize failures

Drawbacks Application development requires resources and expertise Hard to port applications between systems Low degree of software reuse

17

Hardware

Software Packages

Application Programs

Machine-Dependent Programming Model

NCSI, 2015


Optimization Notice

Example Cluster Programming Systems

18

Hadoop Parallel file system aggregating disk drives across cluster Map/Reduce programming model providing data parallel programming

abstraction

Spark Project at U.C., Berkeley Grown to have large open source community

GraphLab Environment for describing machine-learning algorithms

– Sparse matrix structure described by graph – Computation proceeds by updating node values asynchronously

NCSI, 2015


Optimization Notice

Cluster Programming Model

Application programs written in terms of high-level operations on data

Runtime system controls scheduling, load balancing, fault tolerance

Performance Challenges Centralized scheduler forms bottleneck

Copying to/from disk very costly – vs. memory-resident computation

Hard to limit data movement – Significant performance factor

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Hardware

Machine-Independent Programming Model

Runtime System

Application Programs

NCSI, 2015


Optimization Notice

Application Developer’s Dream

Develop application based on well-defined abstractions

Computation

Data organization

Resource allocation

Fault tolerance

Libraries, compilers, autotuners take care of machine-specific mappings

Performance comparable to what can be obtained by hand tuning

Rich collection of domain-independent and domain-specific software modules

Enable sharing and reuse of software

20


Optimization Notice

Let Some Dreams Come True - PaaS

Traditional IT Custom app or Packaged SW

Storage

Servers

Networking

O/S

Middleware

Virtualization

Data

Applications

Runtime

You

Man

age

O/S

IaaS Custom app or Packaged SW

Storage

Servers

Networking

Middleware

Virtualization

Data

Applications

Runtime Yo

u M

anag

e

Pro

vide

r Man

ages

O/S

PaaS Custom app

Storage

Servers

Networking

Middleware

Virtualization

Data

Applications

Runtime

Business Value, Agility & Cost Savings

You

Man

age

Pro

vide

r Man

ages

PaaS enables developers to use self-service to build, deploy and manage their custom apps 21


Optimization Notice

Developers code their app & deploy into production without IT assistance Cloud tooling: self-service, on-demand, multi-

tenant, metered Pre-provisioned common platform of abstracted

middleware & infrastructure

Facilitates creation of cloud-ready applications Platform provides runtime container, elastic scaling and high availability Maximize resource sharing via multi-tenancy and reusable web services

Why PaaS?

22

Speed to market and reduced SW delivery risk Reduced time to deploy a new app from 6 months to minutes PaaS helps accelerate innovation improving margin vs. reducing cost (IaaS)


Optimization Notice

Computing Progression for SDI

Systems

App

App A

A

Systems

Virtualization

S

1

S

2

S

64 …

A

A

A

A

A

A

A

Orchestration

Traditional Virtualized Cloud

Orchestration

HyperScale Cloud

V V V

Cloud A Cloud B

v p p p p

p p p p

p v p

1. Multi-tenant 2. Rapid elasticity 3. Self-service 4. Measured services 5. Resource pooling

1. Highly optimized 2. Highly efficient 3. Managed cross-cloud 4. Heterogeneous *Note: not all clouds require virtualization

Resource Pool

Resource Pool

…

…

… v p p p p

p p p p

p v p

A

A

A

A

A

A

A

…

Multiple computing models will persist for foreseeable future


Optimization Notice 24

Go Application Development Platform

Key features • Statically typed, garbage-collected, concurrency

support at language level, with fast compilation • Support for concurrency, memory management

and faster compilation • Productivity of a dynamic language • Multiplatform, Portable, Open source

Appeals to.. • SDI domain • Python & Ruby developers who want faster

performance • C programmers that want simplicity and

productivity

Challenges • Small developer community • Limited library support • Success Stories

Build applications for Distributed, Concurrent & Cloud Computing Applications


Optimization Notice 25

Call to action!

• Enhance local contribution to Go and PaaS (e.g. CloudFoundry)

• Research the different cloud models – fun and profitable (maybe)

areg melik-adamyan, engineering manager · software and workloads used in performance tests may...

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