IS-4082, Real-Time insight in Big Data – Even faster using HSA, by Norbert Heusser

REAL-‐TIME INSIGHT IN BIG DATA EVEN FASTER USING HSA

| REAL-‐TIME INSIGHT IN BIG DATA| November 19, 2013 | CONFIDENTIAL 2

AGENDA

WHAT ARE BIG DATA AND PARSTREAM

TECHNICAL ARCHITECTURE

HSA USAGE

What are Big Data and ParStream


What is Big Data? COMMON SENSE FROM WIKIPEDIA

“Big data is a collecRon of data sets so large and complex that it becomes difficult to process using on-‐hand database management tools or tradiBonal data processing applicaRons. The challenges include capture, curaRon, storage, search, sharing, analysis and visualizaRon.”


WHAT BIG DATA IS NOT

Big Data is NOT Storage of large datasets

A COMMON MISTAKE


REAL-TIME IN BIG DATA IS A TWO-DIMENSIONAL PROBLEM

Sub-second response times

Continuous extremely fast data load and availability


ANALYTICS LANDSCAPE BIG DATA ANALYTICS REQUIRES NEW TECHNOLOGICAL SOLUTIONS

Real-‐Time

Lag Time

OperaBonal Data

Massively parallel (MPP) Real-‐Time

Map Reduce Batches (NoSQL)

OLTP ReporBng

In-‐Memory DB

Complex Event Processing

Gigabyte Terabyte Petabyte

< 1..10 milli sec

10 sec

10 min

10..100 milli sec

1 sec

1 min OLAP

Big Data Response Rme

Batch-‐AnalyBcs

Real-‐Time AnalyBcs Stream-‐AnalyBcs

OperaBons AnalyBcs

1h

● ParStream


PARSTREAM IS A UNIQUE PRODUCT

!  Analyze and Filter Billions of Records !  Query Data Structures with 1000’s of columns

!  Get Answers in Milliseconds without Cubes

!  Get Answers in Milliseconds without Cubes

!  Execute 1000’s of Concurrent Queries

PARSTREAM EMPOWERS CUSTOMERS TO REALIZE NEW BUSINESS OPPORTUNITIES EVOLVING WITH BIG DATA

High Performance Index

Column Store

In-‐Memory Technology

High-‐Speed Import

Scalability

Scalability Clustering Clustering Real-‐Rme Queries

Technical Architecture


ARCHITECTURE BUILDING BLOCKS

!  Columnar Storage

!  In Memory Technology

!  Shared Nothing Architecture !  Standard Interfaces !  User Defined FuncRons !  Unique High Performance

Compressed Index

PARSTREAM IS THE BIG DATA ANALYTICS PLATFORM BASED ON A UNIQUE HIGH PERFORMANCE COMPRESSED INDEX

SQL/JDBC/ODBC C++ UDF API

Real-‐Time AnalyRcs Engine

Compressed Index

MPP

In-‐Memory & Disc Technology

ParRRoning

Shared Nothing

Fast Columnar Storage


PARALLEL ARCHITECTURE

!  STANDARD DW ARCHITECTURE ‒  Long Query RunRme ‒  Frequent Full Table Scans ‒ Data is at Least 1 Day Old

!  PARSTREAM ARCHITECTURE ‒ Each Query Uses MulRple Processor Cores ‒ Query execuRon using compressed indices ‒ ConRnuous Import Assures Timeliness of Data

PARSTREAM OVERCOMES LIMITATIONS OF TRADITIONAL DW ARCHITECTURES

Nightly Batch -‐ Import

Query

Query

Parallel Import

HPCI


TRADITIONAL DATABASE QUERY EXECUTION STATIC QUERY EXECUTION

SQL-‐Statement

Parser

Parsed-‐Statement ExecuRonPlan

OpRmizer/Planner Executor


MODULAR EXECUTION TREE

!  Parsed query descripRons are transformed into execuRon trees

!  OpRmizer distributes execuRon operaRons to available hardware

!  Data-‐locality and current load are used for allocaRon

!  During query execuRon opRmizer can re-‐allocate if beneficial

!  OpRmizer conRnuously refines allocaRon based on past queries

!  Flow based execuRon control !  Each ExecNode processes blocks of data !  Data transfer between nodes using queues

ATOMIC OPERATIONS COMBINED USING QUEUES

ExecuBon Tree

aggregate

sort

aggregaRon

fetch

filter

calc

aggregaRon

fetch

filter

calc

aggregaRon

fetch

filter

calc

aggregaRon

fetch

filter

calc

HSA Usage


ExecuBon Tree

aggregate

sort

aggregaRon

fetch

filter

calc

aggregaRon

fetch

filter

calc

aggregaRon

fetch

filter

calc

aggregaRon

fetch

filter

calc

ARCHITECUTRE ALLOWS USAGE OF DIFFERENT PROCESSING UNITS

!  Each atomic operaRon may be processed using any available compute resource

!  Dynamic workload assignment during query execuRon

!  Overall workload management ensures opRmal resource usage

ANY PART OF THE QUERY MAY BE EXECUTED INDIVIDUALLY


aggregaRon

fetch

filter

calc

PROBLEMS USING TRADITIONAL GPU COMPUTE UNITS

!  Target scenario Real-‐Time BIG DATA ‒ Processing huge amounts of data ‒ Dynamically changing of data ‒  InteracRve response Rme

!  Part of the data fixed in GPU memory ‒  Input data transferred once via PCI during loading ‒ Transfer of result via PCI during execuRon

!  Data resident in main memory ‒ Offload of computaRonal task to GPU ‒ Transfer in and out via PCI during execuRon

!  Global data needs to be transferred to GPU too !  Global data needs to be synchronized !  Latency based on blockwise processing !  Different programming models

THE TRANSFER AND COMMUNICATION PROBLEM

fetch

calc

aggregaRon

filter


HSA SOLVES ALL OUR PROBLEMS

!  No Data transfer required !  Shared page table support !  Coherent memory regions

!  User-‐level command queueing

!  Hardware scheduling !  Bold allows uniform programming model


DISCLAIMER & ATTRIBUTION

The informaRon presented in this document is for informaRonal purposes only and may contain technical inaccuracies, omissions and typographical errors.

The informaRon contained herein is subject to change and may be rendered inaccurate for many reasons, including but not limited to product and roadmap changes, component and motherboard version changes, new model and/or product releases, product differences between differing manufacturers, soqware changes, BIOS flashes, firmware upgrades, or the like. AMD assumes no obligaRon to update or otherwise correct or revise this informaRon. However, AMD reserves the right to revise this informaRon and to make changes from Rme to Rme to the content hereof without obligaRon of AMD to noRfy any person of such revisions or changes.

AMD MAKES NO REPRESENTATIONS OR WARRANTIES WITH RESPECT TO THE CONTENTS HEREOF AND ASSUMES NO RESPONSIBILITY FOR ANY INACCURACIES, ERRORS OR OMISSIONS THAT MAY APPEAR IN THIS INFORMATION.

AMD SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT WILL AMD BE LIABLE TO ANY PERSON FOR ANY DIRECT, INDIRECT, SPECIAL OR OTHER CONSEQUENTIAL DAMAGES ARISING FROM THE USE OF ANY INFORMATION CONTAINED HEREIN, EVEN IF AMD IS EXPRESSLY ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

ATTRIBUTION

© 2013 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo and combinaRons thereof are trademarks of Advanced Micro Devices, Inc. in the United States and/or other jurisdicRons. SPEC is a registered trademark of the Standard Performance EvaluaRon CorporaRon (SPEC). Other names are for informaRonal purposes only and may be trademarks of their respecRve owners.

IS-4082, Real-Time insight in Big Data – Even faster using HSA, by Norbert Heusser

Technology

Transcript of IS-4082, Real-Time insight in Big Data – Even faster using HSA, by Norbert Heusser