Combining Multiple Metrics to Control BSP Process Rescheduling in Response to Resource and Application Dynamics

Righi, Rodrigo da Rosa; Graebin, Lucas; Ávila, Rafael Bohrer; Navaux, Philippe; Pilla, Laércio Lima

doi:10.1109/icpads.2011.44

Cited by 1 publication

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors measured both the speedup and the worker idle time of each implementation. Other features used in the context of FIC, considering a multicomputer environment, were Web Services (Fang et al, 2011) and process migration (Righi, 2012). Particular, this second work applies process rescheduling in grid environments for dealing with architecture heterogeneity and application dynamicity.…”

Section: Resultsmentioning

confidence: 99%

“…Basically, the most alternatives try to reduce the coding time by reducing the search for the best-match block in a large domain pool (Fu & Zhu, 2009;Jeng et al, 2009;Mitra et al, 1998;Qin et al, 2009;Revathy & Jayamohan, 2012;Rowshanbin et al, 2006;Sun & Wun, 2009;Vahdati et al, 2010). Other possibilities consist in exploring the power of parallel architectures like nCUBE (Jackson & Blom, 1995), SIMD (Single Instruction Multiple Data) (Khan & Akhtar, 2013;Wakatani, 2012) processors and clusters (Righi, 2012;Qureshi & Hussain, 2008). The use of multitasking on recent computing systems is a possibility not deeply explored for solving the FIC problem (Cao & Gu, 2010;Cao & Gu, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Data-Parallelism on Multicore and SMT Systems for Implementing the Fractal Image Compressing Problem

Righi¹,

Rodrigues²,

Costa³

et al. 2016

CIS

View full text Add to dashboard Cite

This paper presents a parallel modeling of a lossy image compression method based on the fractal theory and its evaluation over two versions of dual-core processors: with and without simultaneous multithreading (SMT) support. The idea is to observe the speedup on both configurations when changing application parameters and the number of threads at operating system level. Our target application is particularly relevant in the Big Data era. Huge amounts of data often need to be sent over low/medium bandwidth networks, and/or to be saved on devices with limited store capacity, motivating efficient image compression. Especially, the fractal compression presents a CPU-bound coding method known for offering higher indexes of file reduction through highly time-consuming calculus. The structure of the problem allowed us to explore data-parallelism by implementing an embarrassingly parallel version of the algorithm. Despite its simplicity, our modeling is useful for fully exploiting and evaluating the considered architectures. When comparing performance in both processors, the results demonstrated that the SMT-based one presented gains up to 29%. Moreover, they emphasized that a large number of threads does not always represent a reduction in application time. In average, the results showed a curve in which a strong time reduction is achieved when working with 4 and 8 threads when evaluating pure and SMT dual-core processors, respectively. The trend concerns a slow growing of the execution time when enlarging the number of threads due to both task granularity and threads management.

show abstract

Section: Resultsmentioning

confidence: 99%