Microarchitecture-independent Characteristics Research Articles

AMPS: Accelerating McPAT Power Evaluation Without Cycle-Accurate Simulations

Multicore power, area, and timing (McPAT) is one of the popular system-level simulators that can evaluate power either in design space explorations (DSEs) or in run-time applications. However, when estimating the dynamic power of a processor, the inputs of McPAT are microarchitecture statistics captured from cycle-accurate simulations, which usually are unacceptably time-consuming. In this letter, we propose an accelerated McPAT power simulator (AMPS) without any cycle-accurate simulations. The statistics are analytically modeled using micro-architecture independent characteristics of the application (MICA) that extracted from the trace profiling. AMPS is verified under the SPEC2006 benchmark suite. The average error of power evaluation using AMPS is 4.96% relative to that based on the simulation statistics from gem5. Meanwhile, the evaluation time can be sped up by 9.76 times compared with the approach of accurate simulation inputs.

Analytical Processor Performance and Power Modeling using Micro-Architecture Independent Characteristics

Optimizing processors for (a) specific application(s) can substantially improve energy-efficiency. With the end of Dennard scaling, and the corresponding reduction in energy-efficiency gains from technology scaling, such approaches may become increasingly important. However, designing application-specific processors requires fast design space exploration tools to optimize for the targeted application(s). Analytical models can be a good fit for such design space exploration as they provide fast performance and power estimates and insight into the interaction between an application's characteristics and the micro-architecture of a processor. Unfortunately, prior analytical models for superscalar out-of-order processors require micro-architecture dependent inputs, such as cache miss rates, branch miss rates and memory-level parallelism. This requires profiling the applications for each cache and branch predictor configuration of interest, which is far more time-consuming than evaluating the analytical performance models. In this work we present a micro-architecture independent profiler and associated analytical models that allow us to produce performance and power estimates across a large superscalar out-of-order processor design space almost instantaneously. We show that using a micro-architecture independent profile leads to a speedup of 300 $\times$ compared to detailed simulation for our evaluated design space. Over a large design space, the model has a 9.3 percent average error for performance and a 4.3 percent average error for power, compared to detailed cycle-level simulation. The model is able to accurately determine the optimal processor configuration for different applications under power or performance constraints, and provides insight into performance through cycle stacks.

Measuring benchmark similarity using inherent program characteristics

This paper proposes a methodology for measuring the similarity between programs based on their inherent microarchitecture-independent characteristics, and demonstrates two applications for it: 1) finding a representative subset of programs from benchmark suites and 2) studying the evolution of four generations of SPEC CPU benchmark suites. Using the proposed methodology, we find a representative subset of programs from three popular benchmark suites - SPEC CPU2000, MediaBench, and MiBench. We show that this subset of representative programs can be effectively used to estimate the average benchmark suite IPC, L1 data cache miss-rates, and speedup on 11 machines with different ISAs and microarchitectures - this enables one to save simulation time with little loss in accuracy. From our study of the similarity between the four generations of SPEC CPU benchmark suites, we find that, other than a dramatic increase in the dynamic instruction count and increasingly poor temporal data locality, the inherent program characteristics have more or less remained unchanged.