Workload and technology trends driving future system design

© 2012 IBM Corporation

Workload and technology trends driving future system design

Bob BlaineyMay 2012


Agenda

� Context: Quick overview of IBM’s software business

� Software development in Canada

� Deep dive into business analytics

� Some systems technology trends

� Q & A


IBM Strategy & Performance: 2000-2010


IBM solutions are built on a core set of software capabilities

Turn Information into Insights

Business Analytics

Data Management

Data Warehousing

Enterprise Content Management

Information Governance

Information Integration and Federation

Enable Product and Service Innovation

Application Lifecycle Management

Complex and Embedded Systems

Design and Development

Enterprise Architecture and Portfolio Management

Enterprise Modernization

Security

Drive Business Integration and Optimization

Application Infrastructure

Business Process Management

Commerce

Connectivity and Integration

Enterprise Marketing Management

Optimize the Impact of Business Infrastructures and Services

Asset Management

Business Service Management

Cloud and Virtualization Management

Network and Service Assurance

Security

Storage Management

Systems Management

Connect and Collaborate Social Business Application Development

Social Collaboration

Unified Communications

Web Experience

Manage Risk, Security, and Compliance

Application and Process

Data and Information

Network, Server, and Endpoint

People and Identity

Physical Infrastructure

Security governance, risk management and compliance


IBM Software Solutions: Making New Markets, Reaching New Buyers


Smarter Cities: Manage, automate & optimize city operations


Watson and Next Generation Analytics


IBM’s Big Data Platform


Software Development in Canada

© 2012 IBM Corporation10


IBM Canada Lab History of Software Product Leadership Growth through Acquisition






Brain

Cities

Water

HPC & Agile Computing

Energy

Applied Research +

Innovation Centre

IBM Canada Research and Development Centre


� IBM is establishing an IBM Research “footprint” in Canada (initially Ontario based).

� Announced April 10, 2012

� Research to be focused on:• Agile Computing (moving towards Exascale computing)• Health (Brain Modeling, Streaming Analytics, Watson)• Intelligent Infrastructure (Smarter Water, Cities, Energy, ….)

• $210 Million total investment.

• The overall project Includes:• The building of a new IBM Data Centre in Barrie, Ontario.• Canada’s first Bluegene/Q in Canada (this will be the largest HPC system in the country

when installed).• A Cloud Computing Cluster.• 145 highly skilled jobs in Research, Development, and Operations.

• A collaborative approach to Research involving IBM and 7 leading universities:• University of Toronto• Western University• Queen’s University• University of Ottawa• University of Waterloo• McMaster University• University of Ontario Institute of Technology

IBM Canada Research and Development Centre


Deep Dive into Business Analytics


Davenport’s Taxonomy of Analytics

Source: Davenport & Harris, Competing on Analytics: The New Science of Winning, February 2007


Drilling down: Analytical application types

Functional Goal Example Applications

PrescriptionSupply chain management, Product scheduling, Logistics, Routing,Workforce management

PredictionRevenue prediction, Disease spread prediction, Semiconductor yield analysis, Predictive policing

ReportingRetail sales analysis, Financial reporting, Budgeting, System management analysis, Social network analysis

SimulationVLSI sensitivity analysis, Insurance risk modeling, Credit risk analysis, Physics/Biology simulations, Games

Pattern MatchingIntrusion Detection, Computational chemistry, Document management, Searching, Bio- and Chemo-informatics

RecommendationCross-sale analysis, Customer retention, Music/Video and restaurant recommendation

AlertingWeb-traffic analysis, Fraud detection, Geological Sensor networks, Geographical analytics (Maps)

Quantitative AnalysisCustomer relationship analysis, Weather forecasting, Medical informatics, Econometrics, Computational finance

Source: Bordawekar et al, Analyzing Analytics: A Survey of Business Analytics Models and Algorithms,IBM Research Report RC25186, 2011


Key Analytics Exemplars and Algorithms

Exemplar Functional Goals Key Algorithms

1Regression Analysis

Prediction, Quantitative analysis

Linear, Non-linear, Logistic, and Probit regression

2 ClusteringPattern matching, Recommendation, Prediction, Reporting

K-Means and Hierarchical clustering, Expectation-Maximization Clustering, Naive Bayes

3Nearest-neighbor search

Pattern recognition, Prediction, Recommendation

K-d, Ball, and Metric trees, Approximate Nearest-neighbor, Locality-sensitive Hashing, Kohonen networks

4Association rule mining

RecommendationApriori, Partition, FP-Growth, Eclat and MaxClique, Decision trees

5 Neural networks Prediction, Pattern matchingSingle- and Multi-level perceptrons, Radial-Basis Function (RBF), Recurrent, and Kohonen networks

6Support vector machines

Prediction, Pattern matchingSVMs with Linear, Polynomial, RBF, Sigmoid, and String kernels

7Decision tree learning

Prediction, Recommendation ID3/C4.5, CART, CHAID, QUEST



Exemplar Functional Goals Key Algorithms

8Time series processing

Prediction, Pattern matching, Reporting, Alerting

Trend, Seasonality, Spectral analysis, ARIMA, Exponential smoothing

9 Text analytics Pattern matching, ReportingNaive Bayes classifier, Latent semantic analysis, String-kernel SVMs, Non-negative matrix factorization

10Monte Carlo methods

Simulation, Quantitative analysis

Markov-chain, Quasi-Monte Carlo methods

11Mathematical programming

Prescription, Quantitative analysis

Primal-dual interior point, Branch & Bound methods, Traveling salesman, A* algorithm, Quadratic programming

12On-line analytical processing (OLAP)

Reporting, PredictionGroup-By, Slice and Dice, Pivoting, Rollup and Drill-down, Cube

13 Graph analyticsPattern matching, Reporting, Recommendation

Eigenvector Centrality (e.g., PageRank), Routing, Coloring, Searching and flow algorithms, Clique and motif finding

Key Analytics Exemplars and Algorithms



Computational Characteristics of Exemplars

Exemplar Computational Patterns Key Data Types, Data Structures and

Functions

1Regression Analysis

Matrix inversion, LU decomposition, Transpose, Cholesky factorization

Double-precision and Complex data, Sparse & dense matrices, Vectors

2 ClusteringMetric-based iterative convergence

Height-balanced tree, Graph, Distance functions (Euclidean,Manhattan, Minkowski, and Log-Likelihood), log functions

3Nearest-neighbor search

Non-iterative distance calculations via metric functions, Singular value decomposition, Hashing

Higher-dimensional data structures (k-d, and Metric trees), Hash tables, Euclidean and Hamming distance functions

4Association rule mining

Set intersections, Unions, and Counting

Hash-tree, Relational tables, Prefix trees, Bit vectors

5 Neural networks

Iterative Weighted Feedback networks, Matrix multiplication, Inversion, Cholesky factorization

Sparse/dense matrices, Vectors, Double-precision/Complex data, Gaussian, Multiquadric, Spline, Logistic, Smoothing functions





Functions

6Support Vector Machines

Cholesky factorization, Matrix multiplication

Double-precision floats, Sparse matrices, Vectors, Kernel functions (e.g., Linear, Sigmoid, Polynomial, String)

7Decision Tree Learning

Dynamic programming, Recursive tree operations

Integers, Double-precision floats, Trees, Vectors, log function

8Time Series Processing

Smoothing via averaging, Correlation, Fourier and Wavelet transforms

Integers, Single-/Double-precision floats, Dense matrices, Vectors, cosine, sine, log functions, Distance and Smoothing functions

9 Text Analytics

Parsing, Bayesian modeling, String matching, Hashing, Singular value decomposition, Matrix multiplication, Transpose, Factorization

Integers, Single/Double precision, Characters, Strings, Sparse matrices, Vectors, Inverse indexes, String functions, Distance functions

10Monte Carlo Methods

Random number generators (e.g., Mersenne, Gaussian), Polynomial evaluation, Interpolation

Double-precision floats, Bit vectors, Bit-level operations (shift, mask), log, sqrt functions





Functions

11Mathematical Programming

Matrix multiplication, Inversion, Cholesky factorization, Dynamic programming, Greedy algorithms, Backtracking-based search

Integers, Double-precision floats, Sparse Matrices, Vectors, Trees, Graphs

12On-line Analytical Processing

Grouping and ordering multi-dimensional elements, Aggregation over hierarchies

Prefix trees, Relational tables, OLAP Operators (e.g., CUBE), Strings, Sorting, Ordering, Aggregation operators (e.g., Sum or Average)

13 Graph Analytics

Graph traversal, Eigensolvers, Matrix-vector and Matrix-matrix multiplication, Factorization

Integer, Single-/Double-precision floats, Adjacency/incident lists, Trees, Queues, Dense/Sparse matrices



Some Systems Technology Trends

© 2012 IBM CorporationDr. Bernard Meyerson

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1.15 Classical ScalingClassical Scaling

Higher Capacitance

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Reduced Stress

Scaling NowScaling Now

Channel ScalingHigh-k / Metal GateBody Controlled DevicesReduced Gate HeightAdvanced BEOL Dielectric

PostPost--Classical ScalingClassical Scaling--Mitigating InnovationsMitigating Innovations--

Semiconductor Scaling in the Past vs. Scaling Now


Post-Classical Systems Scaling

Content removed.See speaker notes.


Many core

GPU

ASIC, ASSP

FPGA

Parallelism + Heterogeneity

Scale-in: What’s Next in Processors?


Heterogeneous Systems: Software Optimization Challenges

� Parallelism– Many (thousands) processors to keep busy– Hierarchies of parallelism (e.g. SMP, multi-

core, super-scalar)– Different styles of parallelism (data, task, bit)– Differing memory/compute balance

� Function placement– Specialized processing patterns – Specific system connect points (e.g. close to

network, close to host memory)– Functional limitations (e.g. FPGA area,

floating point precision)

� Communication optimization– Optimize available bandwidth (blocking,

streaming)– Hide latency (reduce round-trips,

asynchronous communication)– Overlap communication and computation


� An array of logic processing elements connected into a grid on a single chip

� Logic elements can be programmed to execute simple binary functions

� Logic elements also include memory elements for simple state management (e.g. flip-flop)

� Special purpose elements such as block RAMs and DSP ALUs are also typically included

� Switches in the grid interconnect can be configured to create a network of logic elements

29

Typically “programmed” using HDL languages such as Verilog of VHDL

Typically “programmed” using HDL languages such as Verilog of VHDL

What is a Field Programmable Gate Array?


Reaching a tipping point in FPGA capability (logic & memory)

Source: Altera, “FPGA Coprocessing Evolution: Sustained Performance Approaches Peak Performance”, June 2009


The IBM Netezza 1000 Data Warehouse Appliance

High-performance databaseengine streaming joins,aggregations, sorts, etc.

SQL CompilerQuery PlanOptimizeAdmin

Processor &streaming DB logic

Slice of User DataSwap and Mirror partitionsHigh speed data streaming

SMP Hosts

Snippet Blades™

(S-Blades™)

Disk Enclosures


The IBM Netezza S-Blade™


Accelerating Data Warehouse Queries on the S-Blade™


Summary

� IBM has a vast software business, covering virtually every aspect of enterprise information technology

– Major focus on extracting insight from massive amounts of data– Increasing focus on novel analytic applications such as Smarter Cities and Watson to

deliver value directly to line of business customers

� In Canada, IBM has a very large software development organization– Sites across the country, created and expanded through organic growth but also through

many acquistions– Many different development missions in Canada but notably a major focus on

information management and business analytics

� Business analytics is a huge driver of value & differentiation for IBM customers– Many different analytic applications commonly deployed– We have investigated these applications, extracted common analytical models

(“exemplars”) and identified key computational characteristics

� Systems technology is in a period of change– We are taking the opportunity to drive system design from the top-down– Specifically, we are building systems which are focused on important analytics

workloads, driven by our deepening understanding of these workloads and drawing on insights into the systems technology roadmap


Questions?

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Workload and technology trends driving future system design