GPU is a multithreaded multicore unit used for high speed parallel processing of huge amounts of data. Data has to be divided among the threads for independent working. If the threads have to coordinate, threads have to be synchronized to share the information with each other. Graph problems can be a challenge to be solved in parallel as firstly it has to be partitioned evenly for a proper load balancing. After which results may conflict between partitions which have to be solved. Graph coloring is one of the problems to reflect this problem clearly. This research aims to color the graph by reducing the number of iterations taken to resolve the conflicts by coloring the nodes which will cause the conflict first. A Sequential coloring algorithm and a Parallel coloring algorithm are used.

GPU is a multithreaded multicore unit used for high speed parallel processing of huge amounts of data. Data has to be divided among the threads for independent working. If the threads have to coordinate, threads have to be synchronized to share the information with each other. Graph problems can be a challenge to be solved in parallel as firstly it has to be partitioned evenly for a proper load balancing. After which results may conflict between partitions which have to be solved. Graph coloring is one of the problems to reflect this problem clearly. This research aims to color the graph by reducing the number of iterations taken to resolve the conflicts by coloring the nodes which will cause the conflict first. A Sequential coloring algorithm and a Parallel coloring algorithm are used.

CHAPTER1 INTRODUCTION ................................. 1
1.1 GPU ……………………………………………………………. 1
1.2 Graph Coloring Problem …………………………………. 3
1.3 Application of Graph Coloring ……………………………3
CHAPTER 2 PREVIOUS WORK …………………………………….. 5
2.1 Sequential Graph Coloring Algorithms ……………... 5
2.2 Parallel Graph Coloring Algorithms ………………. 6
CHAPTER 3 PROPOSAL …………………………………………….. 8
3.1 Algorithm ……………………………………………………. 8
3.2 Coloring Heuristics ……………………………………… 9
3.2.1 Sequential First Fit Algorithm ……………………… 9
3.2.2 Luby-Jones Parallel Graph Coloring Algorithm (Color Local Maxima) ………………………………………………….. 10
3.2.3 Luby-Jones Parallel Graph Coloring Algorithm (Color Local Maxima and Minima) …………………………………….. 11
3.2.4 Modified Luby-Jones Parallel Graph Coloring Algorithm (Color Local Maxima and Minima) ……………………………. 11
3.3 Parameters …………………………………………………. 13
3.3.1 Graph Partitioning …………………………………….. 13
3.3.2 Coloring Heuristics to Choose …………………… 14
CHAPTER 4 EXPERIMENTS AND OBSERVATION ………………….. 15
4.1 Experiment 1 ……………………………………………….. 15
4.1.1 Observation 1 ……………………………………….. 15
4.2 Experiment 2 ……………………………………………….. 17
4.2.1 Observation 2 ………………………………………….. 17
4.3 Results ……………………………………………………... 19
CHAPTER 5 FUTURE WORK ……………………………………….. 20
CHAPTER 6 REFERENCES ………………………………………….. 21

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[2] A.V.Pascal Grosset, Peihong Zhu, Shusen Liu, Suresh Venkatasubramanian. 2011. Evaluating Graph Coloring on GPUs. University of Utah, USA.
[3] Frank Dehne, Kumanan Yogaratnam. 2010. Exploring the limits of GPUs with Parallel Graph Algorithms. Carleton University, Canada.
[4] Henning Meyerhenke, Peter Sanders, Christian Schulz. 2015. Parallel Graph Partitioning for Complex Networks. Karlsruhe Institute of Technology, Germany.
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[6] Pawan Harish, P.J. Narayanan. Accelerating Large Graph Algorithms on GPU using CUDA. International Institute of Information Technology, India.
[7] Per Normann. 2014. Parallel Graph Coloring Algorithms on Multi-Core CPUs, Uppsala University, Uppsala.
[8] Shamim Ahmed. 2012. Applications of Graph Coloring in Modern Computer Science. Dhaka International University, Bangladesh.