Abstract
This paper presents a hardware-based dynamic optimizer that continuously optimizes an application's instruction stream. In continuous optimization, dataflow optimizations are performed using simple, table-based hardware placed in the rename stage of the processor pipeline. The continuous optimizer reduces dataflow height by performing constant propagation, reassociation, redundant load elimination, store forwarding, and silent store removal. To enhance the impact of the optimizations, the optimizer integrates values generated by the execution units back into the optimization process. Continuous optimization allows instructions with input values known at optimization time to be executed in the optimizer, leaving less work for the out-of-order portion of the pipeline. Continuous optimization can detect branch mispredictions earlier and thus reduce the misprediction penalty. In this paper, we present a detailed description of a hardware optimizer and evaluate it in the context of a contemporary microarchitecture running current workloads. Our analysis of SPECint, SPECfp, and mediabench workloads reveals that a hardware optimizer can directly execute 33% of instructions, resolve 29% of mispredicted branches, and generate addresses for 76% of memory operations. These positive effects combine to provide speed ups in the range 0.99 to 1.27.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 86-97 |
| Number of pages | 12 |
| Journal | Proceedings - International Symposium on Computer Architecture |
| State | Published - Nov 10 2005 |
| Event | 32nd Interntional Symposium on Computer Architecture, ISCA 2005 - Madison, WI, United States Duration: Jun 4 2005 → Jun 8 2005 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Continuous optimization. / Fahs, Brian; Rafacz, Todd; Patel, Sanjay Jeram; Lumetta, Steven Sam.
In: Proceedings - International Symposium on Computer Architecture, 10.11.2005, p. 86-97.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Continuous optimization
AU - Fahs, Brian
AU - Rafacz, Todd
AU - Patel, Sanjay Jeram
AU - Lumetta, Steven Sam
PY - 2005/11/10
Y1 - 2005/11/10
N2 - This paper presents a hardware-based dynamic optimizer that continuously optimizes an application's instruction stream. In continuous optimization, dataflow optimizations are performed using simple, table-based hardware placed in the rename stage of the processor pipeline. The continuous optimizer reduces dataflow height by performing constant propagation, reassociation, redundant load elimination, store forwarding, and silent store removal. To enhance the impact of the optimizations, the optimizer integrates values generated by the execution units back into the optimization process. Continuous optimization allows instructions with input values known at optimization time to be executed in the optimizer, leaving less work for the out-of-order portion of the pipeline. Continuous optimization can detect branch mispredictions earlier and thus reduce the misprediction penalty. In this paper, we present a detailed description of a hardware optimizer and evaluate it in the context of a contemporary microarchitecture running current workloads. Our analysis of SPECint, SPECfp, and mediabench workloads reveals that a hardware optimizer can directly execute 33% of instructions, resolve 29% of mispredicted branches, and generate addresses for 76% of memory operations. These positive effects combine to provide speed ups in the range 0.99 to 1.27.
AB - This paper presents a hardware-based dynamic optimizer that continuously optimizes an application's instruction stream. In continuous optimization, dataflow optimizations are performed using simple, table-based hardware placed in the rename stage of the processor pipeline. The continuous optimizer reduces dataflow height by performing constant propagation, reassociation, redundant load elimination, store forwarding, and silent store removal. To enhance the impact of the optimizations, the optimizer integrates values generated by the execution units back into the optimization process. Continuous optimization allows instructions with input values known at optimization time to be executed in the optimizer, leaving less work for the out-of-order portion of the pipeline. Continuous optimization can detect branch mispredictions earlier and thus reduce the misprediction penalty. In this paper, we present a detailed description of a hardware optimizer and evaluate it in the context of a contemporary microarchitecture running current workloads. Our analysis of SPECint, SPECfp, and mediabench workloads reveals that a hardware optimizer can directly execute 33% of instructions, resolve 29% of mispredicted branches, and generate addresses for 76% of memory operations. These positive effects combine to provide speed ups in the range 0.99 to 1.27.
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M3 - Conference article
AN - SCOPUS:27544446445
SP - 86
EP - 97
JO - Conference Proceedings - Annual International Symposium on Computer Architecture, ISCA
JF - Conference Proceedings - Annual International Symposium on Computer Architecture, ISCA
SN - 1063-6897
ER -