soc performance evaluation using high performance systemq and tlm

SoC performance evaluation using high performance SystemQ and TLM models for communications SoCs

Bernhard Keppler, Lantiq Germany GmbH Rocco Jonack, Sonics Inc. 16th March 2012

• The material presented is result of a collaborate effort by two companies: – Sonics Inc. – Lantiq

• Refer also to according paper in download package

Copyright © Lantiq/Sonics Inc. 2012. All rights reserved. 11/04/12

Agenda

Introduction

Infrastructure

Application Model

Simulation Results

Outlook

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Agenda

Introduction

Infrastructure

Application Model

Simulation Results

Outlook

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Introduction - Distinguishing on Abstraction Levels

Γ Modeling design space defined by accuracy levels: Computation Communication Timing

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Γ System models comprise a mix of abstraction levels Γ Modeling sweet spot changes position over project time Γ Performance Models introduce a new flavor of abstraction


Introduction - Early Starting Point: Performance Model, Example Store-forward Buffer for Ethernet packets

Γ Block Diagram Rate Balance Calculus Static Average/Peak

Rate computation (spreadsheet)

Fifo dimensions calculated

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Γ Queuing Diagram Implemented as a

Performance model Stochastic behavior

added and simulated Fifo dimensions

measured


Introduction - Executable Spec, yet a Need to Compromise for Implementation Speed

Γ Overlay of dynamic effects at critical elements in a system are difficult to comprehend. Calls for executable spec. Simulation models augment static analytical methods for

architectural dimensioning

Γ Model is required as early as possible in project schedule Not another RTL code! We choose a high abstraction level that

helps to answer major architectural questions: Performance Model

During project we refine the model to level of detail as where needed

Γ We can include partners/customer during architecture finding phase Exchange of executable model See algorithms work in a simulated target application

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Introduction - SystemQ: A SystemC Library Comprising Tools for Performance Models

Γ Message: a pointer to an object symbolizing a data chunk of certain size (e.g. ethernet frame incl. headers, etc. )

Γ Queue: a buffer storing messages (eg. FIFO, Stack, etc.) Γ Server: handles messages and consumes time. Can add

computation accuracy to model (refinement where needed) Γ Channel: transports messages, blocking/non-blocking, bitrate

semantics case needed. Γ Traffic Sink/Sources: generate and sink messages

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Agenda

Introduction

Infrastructure

Application Model

Simulation Results

Outlook

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Infrastructure – Bridging from Performance Model Top level to Integrated Sonics Interconnect Model

Γ SystemQ Top-level message transaction timed Abstract communication across module interfaces

Γ Sonics SSX TLM based, close to RTL Communication via OCP

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Adapter translates SQ-Message into OCP-Bus Sequence and vice versa


Infrastructure – OCP TL1

Γ OCP TL1 channels allow cycle accurate modeling Phases are handled independently

Γ Using events for runtime efficiency

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SPA Analysis

Infrastructure – data collection SPA

Γ Performance observation point objects trace information during runtime

Γ Data is collected SQLite-formatted database Γ Data contains Bandwidth Latency Queue levels Buffer levels QoS information

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Infrastructure – data collection SPA

Γ TCL shell allows information extraction with customized scripts Γ Graphical tool reads simulation results and produces

predefined views of data

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Infrastructure – data collection using R

Γ „R“ is open source, huge user base in statistical computing See http://www.r-project.org/ Very lean way to extract and post-process values of interest from

simulation traces

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Value to be extracted from trace

R script searches for pattern match

Plotting value

http://www.r-project.org/


Agenda

Introduction

Infrastructure

Application Model

Simulation Results

Outlook

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Application Model – Typical Home-Gateway

Γ Typical Home-Gateway SOC comprising: WAN (as Traffic Sink/Source) LAN (as Traffic Sink/Source) CPU (Voice Stack/Router) Packet buffer

Γ Find good dimensioning for SSX and MemMax by exploration of Use-Cases Best possible utilization of

DDR… …within latency and

throughput requirements of home gateway application

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Application Model – Use-Cases Define Architectural Requirements

Γ Parameter sets derived from use-cases are applied to the Performance Model (data rates for TSS, packet length variations, etc.)

Γ Simulation runs based on Parameter sweeps

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Application Model – Sonics IP SSX

Γ SSX crossbar Configurable Distance spanning Decoupling for protocol conversions

Γ Multithreaded connections Non blocking behavior prevents performance choke points Implements dedicated services like QoS

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Initiator agent

SSX crossbar exchange

Configurable pipelinestages

Dedicated single or multi threaded links

External interfaces , e.g AXI, AHB, OCP


Application Model – Sonics IPs II

Γ Configurable memory scheduler DDR2/DDR3 support Various service filters optimize bandwidth, while maintaining QoS

requirements Runtime programmable

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RequestBuffer

Write Data

Buffer

Read Data

Buffer

Optional Pipeline Stage

Burst Converter

SchedulerOptional Pipeline Stage

Optional Pipeline Stage Optional Pipeline Stage

ORequest Phase Data Phase Respnse Phase

Optional ClockDomain Crossing

Dedicated buffering close to destination

Optional asynchronous boundary


Agenda

Introduction

Infrastructure

Application Model

Simulation Results

Outlook

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Simulation Results I

Γ Analyzing buffer levels in initiators over time using R

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Γ Elevated levels indicate problems in transporting traffic through system


Simulation Results II

Γ Analyzing bandwidth and cross over queue levels over time using SPA

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Γ Cross-over queue remains at 1 over entire simulation time Points to configuration problem Check configuration parameters


Simulation Results III

Γ Analyzing bandwidth and cross over queue levels again over time using SPA

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Γ Cross-over queues vary now Maximum of 10 outstanding

transaction Probably 8 would be

sufficient Further optimization would

be possible to optimize area


Simulation Results IV

Γ Analyzing again buffer levels in initiators over time using R Γ Lower levels of transaction buffering in initiators confirms

configuration choice

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Agenda

Introduction

Infrastructure

Application Model

Simulation Results

Outlook

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Outlook

Γ Further usage of performance analyze data throughout the implementation process

Γ Further integration of 3rd party models Γ Runtime speed optimizations

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soc performance evaluation using high performance systemq and tlm

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