Basic block distribution analysis to find periodic behavior and simulation points in applications
By Timothy Sherwood, Erez Perelman, Brad Calder. Proceedings 2001 International Conference on Parallel Architectures and Compilation Techniques, 2001
632 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/58186d2983e1dc1da9d4a48856a6a1ec44f9c380
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Margaret Martonosi
- Canturk Isci
- Brad Calder
- Greg Hamerly
- Timothy Sherwood
- Erez Perelman
- Suleyman Sair
- Jeremy Lau
In Hardware and Architecture
- Margaret Martonosi
- Canturk Isci
- Kevin Skadron
- Mircea R. Stan
- Karthik Sankaranarayanan
- Sivakumar Velusamy
- Wei Huang
- Brad Calder
Citations in the corpus
- SimPoint 3.0: Faster and More Flexible Program Phase Analysis (2005, methodology): “The most commonly used frequency vector is the basic block vector [1], which represents how many times each basic block is executed in an interval.”
- Runtime power monitoring in high-end processors: methodology and empirical data (2003, methodology): “Several prior papers have proposed methods for detecting or exploiting program phases[1, 5, 9, 22, 23, 29].”
- Using SimPoint for accurate and efficient simulation (2003, neutral): “In this paper we describe how to use the SimPoint tool, and introduce an improved SimPoint algorithm designed to signi.cantly reduce the simulation time required when the simulation environment relies upon fast-forwarding.”
- Automatically characterizing large scale program behavior (2002, methodology): “In [19], we used basic block vectors to automatically find a single simulation point to potentially represent the complete execution of a program.”
- Temperature-aware microarchitecture: Modeling and implementation (2004, methodology): “Recently, Srinivasan and Adve [2003] and Heo et al.”
- Live, Runtime Phase Monitoring and Prediction on Real Systems with Application to Dynamic Power Management (2006, background): “In recent years, studies have demonstrated different approaches to track application phase behavior, relying on execution properties such as control flow [7, 10, 15, 20, 22, 23] or power/performance characteristics [3, 5, 6, 12, 13, 26, 27].”
- Phase tracking and prediction (2003, methodology): “In [20], we proposed that by profiling only the code that was executed over time we could automatically identify periodic and phase behavior in programs.”