algoritma pemrograman parallel graph coloring

We demonstrate that the execution of irregular applications in CPU and NDP/PIM architectures can be significantly accelerated by co-designing lightweight synchronization approaches along with well-crafted data access policies. Fourth, we design SparseP, the first library for high-performance Sparse Matrix Vector Multiplication on real PIM systems.

Third, we introduce SynCron, the first practical hardware synchronization mechanism tailored for NDP systems. Second, we propose SmartPQ, an adaptive priority queue that achieves high performance under all various contention scenarios in Non-Uniform Memory Access CPU systems. First, we design ColorTM, a novel parallel graph coloring algorithm for CPU systems that trades off using synchronization with lower data access costs. We make four major contributions to accelerating irregular applications in different contexts including CPU and Near-Data-Processing (NDP) (or Processing-In-Memory (PIM)) systems. This dissertation studies the root causes of inefficiency of irregular applications in modern computing systems, and fundamentally addresses such inefficiencies, by proposing low-overhead synchronization techniques among parallel threads in cooperation with well-crafted data access policies. Therefore, achieving high performance and energy efficiency in the execution of emerging irregular applications is of vital importance. Irregular applications comprise an increasingly important workload domain for many fields, including bioinformatics, chemistry, physics, social sciences and machine learning. Key words: distributed memory computers, grap. This bound is an improvement over the previously known best upper bound for the expected running time of a random heuristic for the graph coloring problem. For bounded degree graphs, we show that the expected running time of the heuristic under the P-RAM computation model is bounded by EO(log(n)= log log(n)).

We present experimental results obtained on an Intel iPSC/860 which demonstrate that, for graphs arising from finite element applications, the heuristic exhibits scalable performance and generates colorings usually within three or four colors of the best-known linear time sequential heuristics. In this paper we present an asynchronous graph coloring heuristic well suited to distributed memory parallel computers. The problem of computing good graph colorings arises in many diverse applications, such as in the estimation of sparse Jacobians and in the development of efficient, parallel iterative methods for solving sparse linear systems.

Algoritma pemrograman parallel graph coloring