Name
Abstract | ||
|---|---|---|
Multicore computing presents unique challenges for performance and power optimizations due to the multiplicity of cores and the complexity of interactions between the hardware resources. Understanding multicore power and its implications on application behavior is critical to the future of multicore software development. In this paper, we propose Watts-inside, a hardware-software cooperative framework that relies on the efficiency of hardware support to accurately gather application power profiles, and utilizes software support and causation principles for a more comprehensive understanding of application power. We show the design of our framework, along with certain optimizations that increase the ease of implementation. We present a case study using two real applications, Ocean (Splash-2) and Streamcluster (Parsec-1.0) where, with the help of feedback from Watts-inside framework, we made simple code modifications and realized up to 5% power savings on chip power consumption. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICCD.2013.6657062 | ICCD |
Keywords | Field | DocType |
streamcluster applications,multicore power debugging,power aware computing,power debugging,performance optimizations,microprocessor chips,core multiplicity,hardware-software cooperative framework,power optimizations,multicore software development,splash-2 applications,multiprocessing systems,hardware-software cooperative approach,parsec-1.0 applications,power savings,software support and causation principles,hardware-software codesign,ocean applications,performance evaluation,chip power consumption,code modifications,multicore power,watts-inside framework,hardware resource interaction complexity,application power profiles | Computer architecture,Computer science,Parallel computing,Multicore computing,Real-time computing,Software,Hardware software,Multi-core processor,Software development,Debugging,Power consumption,Embedded system | Conference |
Citations | PageRank | References |
3 | 0.38 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Chen | 1 | 2487 | 353.65 |
| Fan Yao | 2 | 109 | 11.34 |
| Guru Venkataramani | 3 | 394 | 29.49 |