Meta-analytical comparison of energy consumed by two sorting algorithms

Dlamini, Gcinizwe; Jolha, Firas; Kholmatova, Zamira; Succi, Giancarlo

doi:10.1016/j.ins.2021.09.061

Cited by 7 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They showed natural mergesort to have a clear power advantage against a highly optimized 3-way quicksort. A meta-analytical comparison of the energy consumption of mergesort and quicksort was conducted in [33] based on an extensive literature review that included [11]. The study concluded that there was no significant difference in energy needs.…”

Section: Introductionmentioning

confidence: 99%

Exploring Power Advantage of Binary Search: An Experimental Study

Al-Hashimi¹,

Aljabri²

2022

IJACSA

View full text Add to dashboard Cite

As exascale systems come online, more ways are needed to keep them within reasonable power budgets. This study aims to help uncover power advantages in algorithms likely ubiquitous in high-performance workloads such as searching. This study explored the power efficiency of binary search and its ternary variant, comparing consumption under different scenarios and workloads. Accurate modern on-chip integrated voltage regulators were used to get reliable power measurements. Results showed the binary version of the algorithm, which runs slower but relies on a barrel-shifter circuit, to be more power efficient in all studied scenarios offering an attractive time-power tradeoff. The cumulative savings were significant and will likely be valuable where the search may be a substantial fraction of workloads, especially massive ones.

show abstract

Section: Introductionmentioning

confidence: 99%

Exploring Power Advantage of Binary Search: An Experimental Study

Al-Hashimi¹,

Aljabri²

2022

IJACSA

View full text Add to dashboard Cite

show abstract

Investigating the effect of varying block size on power and energy consumption of GPU kernels

Ikram¹,

Saleh

Al-Hashimi

et al. 2022

J Supercomput

View full text Add to dashboard Cite

Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have become an accepted architectural feature for exascale computing due to their scalable performance and power efficiency. In a recent study, we found that we can achieve a reasonable amount of power and energy savings based on the selection of algorithms. In this research, we suggest that we can save more power and energy by varying the block size in the kernel configuration. We show that we may attain more savings by selecting the optimum block size while executing the workload. We investigated two kernels on NVIDIA Tesla K40 GPU, a Bitonic Mergesort and Vector Addition kernels, to study the effect of varying block sizes on GPU power and energy consumption. The study should offer insights for upcoming exascale systems in terms of power and energy efficiency.

show abstract

Cost reference particle filter with multi-probability distribution for nonlinear dynamic systems with unknown noise statistics

Wang

et al. 2023

Digital Signal Processing

View full text Add to dashboard Cite

Meta-analytical comparison of energy consumed by two sorting algorithms

Cited by 7 publications

References 28 publications

Exploring Power Advantage of Binary Search: An Experimental Study

Exploring Power Advantage of Binary Search: An Experimental Study

Investigating the effect of varying block size on power and energy consumption of GPU kernels

Cost reference particle filter with multi-probability distribution for nonlinear dynamic systems with unknown noise statistics

Contact Info

Product

Resources

About