Exceeding the dataflow limit via value prediction
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
ISCA '99 Proceedings of the 26th annual international symposium on Computer architecture
Speculative precomputation: long-range prefetching of delinquent loads
ISCA '01 Proceedings of the 28th annual international symposium on Computer architecture
Focusing processor policies via critical-path prediction
ISCA '01 Proceedings of the 28th annual international symposium on Computer architecture
ISCA '01 Proceedings of the 28th annual international symposium on Computer architecture
Slack: maximizing performance under technological constraints
ISCA '02 Proceedings of the 29th annual international symposium on Computer architecture
Reducing power with dynamic critical path information
Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture
Dynamic speculative precomputation
Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture
Near-Critical Path Analysis of Program Activity Graphs
MASCOTS '94 Proceedings of the Second International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems
The Non-Critical Buffer: Using Load Latency Tolerance to Improve Data Cache Efficiency
ICCD '99 Proceedings of the 1999 IEEE International Conference on Computer Design
Dynamic Prediction of Critical Path Instructions
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
HPCA '02 Proceedings of the 8th International Symposium on High-Performance Computer Architecture
Quantifying instruction criticality for shared memory multiprocessors
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Using Interaction Costs for Microarchitectural Bottleneck Analysis
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Application adaptive energy efficient clustered architectures
Proceedings of the 2004 international symposium on Low power electronics and design
Interaction cost and shotgun profiling
ACM Transactions on Architecture and Code Optimization (TACO)
Online performance analysis by statistical sampling of microprocessor performance counters
Proceedings of the 19th annual international conference on Supercomputing
Accurate critical path prediction via random trace construction
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
End-to-end performance forecasting: finding bottlenecks before they happen
Proceedings of the 36th annual international symposium on Computer architecture
Criticality-driven superscalar design space exploration
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
Criticality based speculation control for speculative multithreaded architectures
APPT'05 Proceedings of the 6th international conference on Advanced Parallel Processing Technologies
Criticality driven energy aware speculation for speculative multithreaded processors
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
| Hi-index | 0.00 |
Information about instruction criticality can be used to control the application of micro-architectural resources efficiently. To this end, several groups have proposed methods to predict critical instructions. This paper presents a framework that allows us to directly measure the criticality of individual dynamic instructions. This allows us to (1) measure the accuracy of proposed critical path predictors, (2) quantify the amount of slack present in non-critical instructions, and (3) provide a new metric, called tautness, which ranks critical instructions by their dominance on the critical path. This research investigates methods for improving critical path predictor accuracy and studies the distribution of slack and tautness in programs. It shows that instruction criticality changes dynamically, and that criticality history patterns can be used to significantly improve predictor accuracy.