Performance estimation of parallel processing techniques on various platforms

Jakimovska, Danijela; Jakimovski, Goran; Tentov, Aristotel; Bojchev, Dimitar

doi:10.1109/telfor.2012.6419482

Cited by 3 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many libraries and programming languages were released to support CPU parallelism in general. However, the most famous library that used in parallel game tree algorithms is MPI (Message Passing Interface) [21] which is an extension of C programming language. MPI handles the burden of synchronization, communication and distributed resources management.…”

Section: A Game Tree Parallelism Techniques and Algorithmsmentioning

confidence: 99%

A Comparative Study of Game Tree Searching Methods

Elnaggar¹,

Aziem²,

Gadallah³

et al. 2014

IJACSA

View full text Add to dashboard Cite

Abstract-In this paper, a comprehensive survey on gaming tree searching methods that can use to find the best move in two players zero-sum computer games was introduced. The purpose of this paper is to discuss, compares and analyzes various sequential and parallel algorithms of gaming tree, including some enhancement for them. Furthermore, a number of open research areas and suggestions of future work in this field are mentioned.

show abstract

Section: A Game Tree Parallelism Techniques and Algorithmsmentioning

confidence: 99%

A Comparative Study of Game Tree Searching Methods

Elnaggar¹,

Aziem²,

Gadallah³

et al. 2014

IJACSA

View full text Add to dashboard Cite

show abstract

“…Many libraries and programming languages were released to support CPU parallelism; however, the most famous library used in parallel game tree algorithms is OpenMPI (Open Source Message Passing Interface) [10]. It handles the burden of synchronization, communication and distributed resources management.…”

Section: General Purpose Gpu Programmingmentioning

confidence: 99%

Enhanced parallel NegaMax tree search algorithm on GPU

Elnaggar

Gadallah

Aziem

et al. 2014

2014 IEEE International Conference on Progress in Informatics and Computing

View full text Add to dashboard Cite

Parallel performance for GPUs today surpasses the traditional multi-core CPUs. Currently, many researchers started to test several AI algorithms on GPUs instead of CPUs, especially after the release of libraries such as CUDA and OpenCL that allows the implementation of general algorithms on the GPU. One of the most famous game tree search algorithms is Negamax, which tries to find the optimal next move for zero sum games. In this research, an implementation of an enhanced parallel NegaMax algorithm is presented, that runs on GPU using CUDA library. The enhanced algorithms use techniques such as no divergence, dynamic parallelism and shared GPU table. The approach was tested in checkers and chess games. It was compared with previous studies, including threads on CPU for up to 6x speedup for an 8 core processor and threads on GPU using iterative dependence and fixed grid and block size for up to 40x speedup at 14 depth. Furthermore, the approach was tested with different depths on the CPU and the GPU. The result shows speed up for parallel GPU up to 80x at 14 depth for checkers game and 90x at 14 depth for chess game, which doubled the previous research results.

show abstract