Power Awareness through Selective Dynamically Optimized Traces Roni Rosner, Yoav Almog, Micha...

Power Awareness through Selective Dynamically Optimized Traces

Roni Rosner, Yoav Almog, Micha Moffie, Naftali Schwartz and Avi Mendelson – Intel Labs, Haifa, Israel

Presenter: Ioana Burcea

Agenda

Motivation for PARROT = Power-Aware aRchitecture Running Optimized Traces

PARROT Concept and Architecture Performance and Energy Results Discussion

– What makes PARROT a power-aware architecture?– What is new about this paper? / What are the contributions

of this paper?

Motivation

We pay more energy per task– Poor scaling of performance with power consumption

PARROT tries to change the balance– Filtering Techniques to Improve Trace-Cache Efficiency –

PACT 2001– Selecting Long Atomic Traces for High Coverage – ICS

2003– Specialized Dynamic Optimizations for High-Performance

Energy-Efficient Microarchitecture – CGO 2004

PARROT Concepts – The Big Picture

Based on the well-known cold/hot (10/90) paradigm

PARROT Principles– Reuse: trace-cache centric– Dynamic optimizations: more performance with

less energy– Focus: invest where it pays– Pipeline decoupling: hybrid front-end, cold and

hot execution pipelines– Transparency: immune to s/w compatibility

Traces and Trace Selection

Decoded atomic traces– Complex retirement & recovery in case of misprediction– More aggressive optimizations

Trace Selection – deterministic criteria– Capacity limitation: 64 uops– Complete basic blocks– Terminating CTI (control-transfer instructions)

Indirect jumps, software exceptions, backward taken branches

– Return instructions: procedure inlining– Trace join

Microarchitecture

Split-execution vs. unified-execution– Foreground phase: fetch-to-execution pipeline– Background phase (post-processing): trace selection and

optimization

Microarchitecture (cont’d)

• Two predictors: GHR = Global History Buffer

•Branch predictor

•Trace predictor

• Deterministic trace build scheme

• Filtering mechanisms:

• The hot filter selects frequent traces from those executed on the cold pipeline

• The blazing filter selects for optimization the hottest traces

• Dynamic optimizations

• generic and core specific optimizations

• gradually applied (?)

Simulation framework

An “in-house” proprietary performance and power simulator

Optimizations applied as different passes– Optimization delay for one trace ~ 100 cycles

Energy simulation– Power consumption matrix for each operation on each

hardware unit– Leakage

Uniform leakage in space over the processor core and L2 cache and in time modeling a high temperature

LE = PMAX * (0.05 * M + 0.4*K) * CYC

Configuration Space

Experimental Evaluation

Metrics– IPC– Total energy– Cubic-MIPS-per-WATT (CMPW)

A measure of the design tradeoffs between power and performance

Benchmarks– SpecInt2000– SpecFP2000– Office– Multimedia– DotNet

Performance and Power Awareness

Extreme Microarchitectural Alternatives

Hot Code Predictability

Trace-cache Fetch Coverage

Optimizer Capabilities

Energy Breakdown

Their Conclusions…

Our Conclusions

What makes PARROT a power-aware architecture?

What is new about this paper? / What are the contributions of this paper?– rePlay (?)