CAPS team Compiler and Architecture for superscalar and embedded processors

CAPS team

Compiler and Architecture

for superscalar and embedded processors

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CAPS members

2 INRIA researchers: A. Seznec, P. Michaud

2 professors: F. Bodin, J. Lenfant

11 Ph D students: R. Amicel, R. Dolbeau, A. Monsifrot , L. Bertaux,

K. Heydemann, L. Morin, G. Pokam, A. Djabelkhir,

A. Fraboulet, O. Rochecouste, E.Toullec

3 engineers: S. Bihan, P. Villalon, J. Simonnet

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CAPS themes

Two interacting activities

High performance microprocessor

architecture

Performance oriented compilation

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CAPS Grail

Performance at the best cost

Progress in computer science and applications are driven by

performance

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CAPS path to the Grail

Defining the tradeoffs between:

what should be done through hardware

what can be done by the compiler

for maximum performance

or for minimum cost

or for minimum size, power ..

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Need for high-performance processors

Current applications general purpose: scientific, multimedia, data bases … embedded systems: cell phones, automotive, set-top boxes ..

Future applications don’t worry: users have a lot of imagination !

New software engineering techniques are CPU hungry: reusability, generality portability, extensibility (indirections, virtual machines) safety (run-time verifications) encryption/decryption

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CAPS (ancient) background

« ancient » background in hardware and software

management of ILP decoupled pipeline architectures OPAC, an hardware matrix floating-point

coprocessor software pipeline for LIW

« Supercomputing » background interleaved memories Fortran-S

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CAPS background in architecture

Solid knowledge in microprocessor architecture technological watch on microprocessors A. Seznec worked with Alpha Development Group in

1999-2000

Researches in cache architecture

Researches in branch prediction mechanisms

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CAPS background in compilers

Software optimizations for cache memories Numerical algorithms on dense structures Optimizing data layout

Many prototype environments for parallel compilers: CT++ (with CEA): image processing C++ library for a SIMD

architecture, Menhir: a parallel compiler for MatLab

IPF (with Thomson-LER): Fortran Compiler for image processing on Maspar

Sage (with Indiana): Infrastusture for source level transformation

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We build on

SALTO: System for Assembly-Language Transformations and Optimizations retargetable assembly source to source preprocessor Erven Rohou’s Ph. D 

TSF: Scripting language for program transformation on top

of ForeSys (Simulog) Yann Mevel’s Ph. D

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Salto overview Assembly source to source preprocessor Fine grain machine description Independent from compilers

Transformationtool

SALTO

inte

rfa

ce

C++

Machine Description

assemblylanguage

assemblylanguage

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Compiler activities

Code optimizations for embedded applications infrastructures rather than compilers

optimizing compiler strategies rather than new code optimizations

Global constraints performance /code sizes/ low power (starting)

Focus on interactive tools rather than automatic code tuning case based reasoning assembly code optimizations

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Computer aided hand tuning

Automatic optimization has many shortcomings

rather provide the user with a testbed to hand-tune

applications

Target applications

Fortran codes and embedded C applications

Our approach

case based reasoning

static code analysis and pattern matching

profiling

learning techniques

the user is the ultimate responsible

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Prototype built onForesys: Fortran interactive front-end (from Simulog)

TSF: Scripting language for program transformation

Sage++: Infrastusture for source level transformation

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Analysis and Tuning tool for Low Level Assembly and Source code (with Thomson Multimedia)

ATLLAS objectives : Has the compiler done a good job ? Try to match source and optimized assembly at fine

grain Development/analysis environment:

Models for both source and assembly Global and local analysis (WCET, …) at both levels Interactive environment for codes visualization and

manual/ automatic analysis and optimization

Built using Salto and Sage++: Retargetable with compilers and architectures

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ATLLAS - Analysis and Tuning tool for Low Level Assembly and Source code : Tuning method

Good ?

Half-Automatic or Manual Source

Optimisations

Atllas

compilation profiling

End

Yes

Half-Automatic or Manual Assembly

Optimisations

Source Code Assembly Code

Post-Processing

ProcessingSupport

Code matching analysis and evaluationsGraphic Display of Ass. And Src. Code

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Assembly Level Infrastrure for Software Enhancement (with STmicroelectonics)

ALISE enhanced SALTO for code optimization:

• better integration with code generation– interface with front-end– interface for profiling data

• targets global optimization• based on component software optimization

engines

Answer to a real need from industry: A retargetable infrastructure

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ALISE

Environment for: global assembly code optimization providing optimization alternatives

Support for new embedded processors ISAs with ILP support (VLIW, EPIC) Predicated instructions Functional unit clusters, ..

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ALISE

ArchitectureDescription

D to MArchitecture Model

Intermediate representation

Opt 1 Opt 2 Opt n

P to IRTextInput

IR to Ass(Emit)

OptimizedProgram

High Level API

Interfaces

External Infrastructure

User interfaceG.U.I.

IntermediateCode

External Infrastructure

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Preprocessor for media processors (MEDEA+ Mesa project)

Multimedia instructions on embedded and general-purpose processors but : no consensus on MMD instructions among constructors:

• saturated arithmetic or not, different instructions, …

Multimedia instructions are not well handled by compilers:

• but performance is very dependent

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Preprocessor for media processors:our approach

C source to source preprocessor user oriented idioms recognition:

easy to retarget target dedicated recognition

exploiting loop parallelism vectorization techniques multiprocessor systems

available soon

Collaboration with Stmicroelectonics

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Iterative compilation

Embedded systems: Compile time is not critical Performance/code size/power are critical One can often relate on profiling

Classical compiler: local optimizations but constraints are GLOBAL

Proof of concept for code sizes (Rohou ’s Ph. D) new Ph. D. beginning in september 2000

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High performance instruction set simulation

Embedded processors: // development of silicon, ISA, compiler and applications

Need for flexible instruction set simulation: high performance simulation of large codes debugging retargetable to experiment:

• new ISA• various microarchitecture options

First results: up to 50x faster than ad-hoc simulator

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ctABSCISS: Assembly Based System for Compiled Instruction Set Simulation

C Source TriMedia Assemblytmcc

TriMedia Binary

ABSCISS

tmsim

tmas

gcc

C/C++ Source

Compiled simulator

Architecture Description

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Enabling superscalar processor simulation

Complete O-O-O microprocessor simulation: 10000-100000 slower than real hardware can not simulate realistic applications, but slices even fast mode emulation is slow (50-100x):

• simulation generally limited to slices at the beginning of the application

• representativeness ? Calvin2 + DICE:

combines direct execution with simulation really fast mode: 1-2x slowdown enables simulating slices distributed over the whole

application

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User analysisroutines

Calvin2 + DICE

Original code

SPARC V9 assembly

code

calvin2Static Code Annotation Tool

checkpoint

checkpoint

checkpoint

checkpoint

checkpoint

Switching event

Emulation modeSwitching event

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Moving tools to IA64

New 64bit ISA from Intel/HP: Explicitly Parallel Instruction Computing Predicated Execution Advanced loads (i.e. speculative) A very interesting platform for research !!

Porting SALTO and Calvin2+DICE approach to IA64

Exploring new trade-offs enabled by instruction sets: predicting the predicates ? advanced loads against predicting dependencies ultimate out-of-order execution against compiler

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Low power, compilation, architecture, …(just beginning :=)

Power consumption becomes a major issue: Embedded and general purpose

Compilation (setting a collaboration with STmicroelectronics/Stanford/Milan): Is it different from performance optimization ? Global constraint optimization Instruction Set Architecture support ?

Architecture: High order bits are generally null, … registers and memory ALUs

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Caches and branch predictors

International CAPS visibility in architecture = skewed associative cache + decoupled sectored cache + multiple block ahead branch prediction + skewed branch predictor

Continue recurrent work on these topics: multiple block ahead + tradeoffs complexity/accuracy

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Simultaneous Multithreading

Sharing functional units among several processes Among the first groups working on this topic

S. Hily’s Ph. D. SMT behavior well understood for independent threads

now, focus on // threads from a single application

Current research directions: speculative multithreading

• ultimate performance with a single thread through predicting threads

performance/complexity tradeoffs: SMT/CMP/hybrid

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« Enlarging » the instruction window (supported by Intel)

In an O-O-O processor, fireable instructions are chosen in a window of a few tens of RISC-like instructions.

Limitations are: size of the window number of physical registers

Prescheduling: separate data flow scheduling from resource arbitration. coarser units of work ?

Reducing the number of physical registers: how to detect when a physical register is dead ? Per group validation ? revisiting CISC/RISC war ?

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Unwritten rule on superscalar processor designs

For general purpose registers:

Any physical register can be the source or the result of any instruction executed

on any functional unit

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4-cluster WSRS architecture(supported by Intel)

S0

S0 C0

S1

S1C1

S2

C2

S3

S3C3S2

•Half the read ports, one

fourth the write ports•Register file:

• Silicon area x 1/8• Power x 1/2• Access time x 0.6

•Gains on:•bypass network•selection logic

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Multiprocessor on a chip

Not just replicating board level solutions !

A way to manage a large on-chip cache capacity: how can a sequential application use efficiently a distributed

cache ? architectural supports for distributing a sequential application

on several processors ? how should instructions and data be distributed ?

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HIPSORHIgh Performance SOftware Random number generation

Need for unpredicable random number generation: sequences that cannot be reproduced

State of the art: < 100 bit/s using the operating system 75Kbit/s using hardware generator on Pentium III

Internal state of a superscalar can not be reproduced use this state to generate unpredictable random

numbers

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HIPSOR (2)

1000’s of unmonitorable states modified by OS interrupts

Hardware clock counter to indirectly probe these states

Combined with in-line pseudo-random number generation

100 Mbit/s unpredictable random numbers

ARC INRIA with CODES