Transitive closure algorithm MEMTC and its performance analysis

AbstractIn this paper, we present a new algorithm for computing the full transitive closure designed for operation in layered memories. The algorithm is based on strongly connected component detection and on a very compact representation of data. We analyze the average-case performance of the algorithm experimentally in an environment where two layers of memory of different speed are used. In our analysis, we use trace-based simulation of memory operations

Accelerating transitive closure of large-scale sparse graphs

Finding the transitive closure of a graph is a fundamental graph problem where another graph is obtained in which an edge exists between two nodes if and only if there is a path in our graph from one node to the other. The reachability matrix of a graph is its transitive closure. This thesis describes a novel approach that uses anti-sections to obtain the transitive closure of a graph. It also examines its advantages when implemented in parallel on a CPU using the Hornet graph data structure. Graph representations of real-world systems are typically sparse in nature due to lesser connectivity between nodes. The anti-section approach is designed specifically to improve performance for large scale sparse graphs. The NVIDIA Titan V CPU is used for the execution of the anti-section parallel implementations. The Dual-Round and Hash-Based implementations of the Anti-Section transitive closure approach provide a significant speedup over several parallel and sequential implementations

Transitive closure algorithm MEMTC and its performance analysis

We present a new algorithm for computing the full transitive closure designed for operation in layered memories. We analyze its average-case performance experimentally in an environment where two layers of memory of different speed are used. In our analysis, we use trace-based simulation of memory operations